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Sample records for network models synapses

  1. A compound memristive synapse model for statistical learning through STDP in spiking neural networks.

    Science.gov (United States)

    Bill, Johannes; Legenstein, Robert

    2014-01-01

    Memristors have recently emerged as promising circuit elements to mimic the function of biological synapses in neuromorphic computing. The fabrication of reliable nanoscale memristive synapses, that feature continuous conductance changes based on the timing of pre- and postsynaptic spikes, has however turned out to be challenging. In this article, we propose an alternative approach, the compound memristive synapse, that circumvents this problem by the use of memristors with binary memristive states. A compound memristive synapse employs multiple bistable memristors in parallel to jointly form one synapse, thereby providing a spectrum of synaptic efficacies. We investigate the computational implications of synaptic plasticity in the compound synapse by integrating the recently observed phenomenon of stochastic filament formation into an abstract model of stochastic switching. Using this abstract model, we first show how standard pulsing schemes give rise to spike-timing dependent plasticity (STDP) with a stabilizing weight dependence in compound synapses. In a next step, we study unsupervised learning with compound synapses in networks of spiking neurons organized in a winner-take-all architecture. Our theoretical analysis reveals that compound-synapse STDP implements generalized Expectation-Maximization in the spiking network. Specifically, the emergent synapse configuration represents the most salient features of the input distribution in a Mixture-of-Gaussians generative model. Furthermore, the network's spike response to spiking input streams approximates a well-defined Bayesian posterior distribution. We show in computer simulations how such networks learn to represent high-dimensional distributions over images of handwritten digits with high fidelity even in presence of substantial device variations and under severe noise conditions. Therefore, the compound memristive synapse may provide a synaptic design principle for future neuromorphic architectures.

  2. A compound memristive synapse model for statistical learning through STDP in spiking neural networks

    Directory of Open Access Journals (Sweden)

    Johannes eBill

    2014-12-01

    Full Text Available Memristors have recently emerged as promising circuit elements to mimic the function of biological synapses in neuromorphic computing. The fabrication of reliable nanoscale memristive synapses, that feature continuous conductance changes based on the timing of pre- and postsynaptic spikes, has however turned out to be challenging. In this article, we propose an alternative approach, the compound memristive synapse, that circumvents this problem by the use of memristors with binary memristive states. A compound memristive synapse employs multiple bistable memristors in parallel to jointly form one synapse, thereby providing a spectrum of synaptic efficacies. We investigate the computational implications of synaptic plasticity in the compound synapse by integrating the recently observed phenomenon of stochastic filament formation into an abstract model of stochastic switching. Using this abstract model, we first show how standard pulsing schemes give rise to spike-timing dependent plasticity (STDP with a stabilizing weight dependence in compound synapses. In a next step, we study unsupervised learning with compound synapses in networks of spiking neurons organized in a winner-take-all architecture. Our theoretical analysis reveals that compound-synapse STDP implements generalized Expectation-Maximization in the spiking network. Specifically, the emergent synapse configuration represents the most salient features of the input distribution in a Mixture-of-Gaussians generative model. Furthermore, the network’s spike response to spiking input streams approximates a well-defined Bayesian posterior distribution. We show in computer simulations how such networks learn to represent high-dimensional distributions over images of handwritten digits with high fidelity even in presence of substantial device variations and under severe noise conditions. Therefore, the compound memristive synapse may provide a synaptic design principle for future neuromorphic

  3. Wireless synapses in bio-inspired neural networks

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    Jannson, Tomasz; Forrester, Thomas; Degrood, Kevin

    2009-05-01

    Wireless (virtual) synapses represent a novel approach to bio-inspired neural networks that follow the infrastructure of the biological brain, except that biological (physical) synapses are replaced by virtual ones based on cellular telephony modeling. Such synapses are of two types: intracluster synapses are based on IR wireless ones, while intercluster synapses are based on RF wireless ones. Such synapses have three unique features, atypical of conventional artificial ones: very high parallelism (close to that of the human brain), very high reconfigurability (easy to kill and to create), and very high plasticity (easy to modify or upgrade). In this paper we analyze the general concept of wireless synapses with special emphasis on RF wireless synapses. Also, biological mammalian (vertebrate) neural models are discussed for comparison, and a novel neural lensing effect is discussed in detail.

  4. How synapses can enhance sensibility of a neural network

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    Protachevicz, P. R.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Baptista, M. S.; Viana, R. L.; Lameu, E. L.; Macau, E. E. N.; Batista, A. M.

    2018-02-01

    In this work, we study the dynamic range in a neural network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

  5. Stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses

    International Nuclear Information System (INIS)

    Wang, Jiang; Guo, Xinmeng; Yu, Haitao; Liu, Chen; Deng, Bin; Wei, Xile; Chen, Yingyuan

    2014-01-01

    Highlights: •We study stochastic resonance in small-world neural networks with hybrid synapses. •The resonance effect depends largely on the probability of chemical synapse. •An optimal chemical synapse probability exists to evoke network resonance. •Network topology affects the stochastic resonance in hybrid neuronal networks. - Abstract: The dependence of stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses on the probability of chemical synapse and the rewiring probability is investigated. A subthreshold periodic signal is imposed on one single neuron within the neuronal network as a pacemaker. It is shown that, irrespective of the probability of chemical synapse, there exists a moderate intensity of external noise optimizing the response of neuronal networks to the pacemaker. Moreover, the effect of pacemaker driven stochastic resonance of the system depends largely on the probability of chemical synapse. A high probability of chemical synapse will need lower noise intensity to evoke the phenomenon of stochastic resonance in the networked neuronal systems. In addition, for fixed noise intensity, there is an optimal chemical synapse probability, which can promote the propagation of the localized subthreshold pacemaker across neural networks. And the optimal chemical synapses probability turns even larger as the coupling strength decreases. Furthermore, the small-world topology has a significant impact on the stochastic resonance in hybrid neuronal networks. It is found that increasing the rewiring probability can always enhance the stochastic resonance until it approaches the random network limit

  6. Synchronization of the small-world neuronal network with unreliable synapses

    International Nuclear Information System (INIS)

    Li, Chunguang; Zheng, Qunxian

    2010-01-01

    As is well known, synchronization phenomena are ubiquitous in neuronal systems. Recently a lot of work concerning the synchronization of the neuronal network has been accomplished. In these works, the synapses are usually considered reliable, but experimental results show that, in biological neuronal networks, synapses are usually unreliable. In our previous work, we have studied the synchronization of the neuronal network with unreliable synapses; however, we have not paid attention to the effect of topology on the synchronization of the neuronal network. Several recent studies have found that biological neuronal networks have typical properties of small-world networks, characterized by a short path length and high clustering coefficient. In this work, mainly based on the small-world neuronal network (SWNN) with inhibitory neurons, we study the effect of network topology on the synchronization of the neuronal network with unreliable synapses. Together with the network topology, the effects of the GABAergic reversal potential, time delay and noise are also considered. Interestingly, we found a counter-intuitive phenomenon for the SWNN with specific shortcut adding probability, that is, the less reliable the synapses, the better the synchronization performance of the SWNN. We also consider the effects of both local noise and global noise in this work. It is shown that these two different types of noise have distinct effects on the synchronization: one is negative and the other is positive

  7. IR wireless cluster synapses of HYDRA very large neural networks

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    Jannson, Tomasz; Forrester, Thomas

    2008-04-01

    RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

  8. Synapse:neural network for predict power consumption: users guide

    Energy Technology Data Exchange (ETDEWEB)

    Muller, C; Mangeas, M; Perrot, N

    1994-08-01

    SYNAPSE is forecasting tool designed to predict power consumption in metropolitan France on the half hour time scale. Some characteristics distinguish this forecasting model from those which already exist. In particular, it is composed of numerous neural networks. The idea for using many neural networks arises from past tests. These tests showed us that a single neural network is not able to solve the problem correctly. From this result, we decided to perform unsupervised classification of the 24 consumption curves. From this classification, six classes appeared, linked with the weekdays: Mondays, Tuesdays, Wednesdays, Thursdays, Fridays, Saturdays, Sundays, holidays and bridge days. For each class and for each half hour, two multilayer perceptrons are built. The two of them forecast the power for one particular half hour, and for a day including one of the determined class. The input of these two network are different: the first one (short time forecasting) includes the powers for the most recent half hour and relative power of the previous day; the second (medium time forecasting) includes only the relative power of the previous day. A process connects the results of every networks and allows one to forecast more than one half-hour in advance. In this process, short time forecasting networks and medium time forecasting networks are used differently. The first kind of neural networks gives good results on the scale of one day. The second one gives good forecasts for the next predicted powers. In this note, the organization of the SYNAPSE program is detailed, and the user`s menu is described. This first version of synapse works and should allow the APC group to evaluate its utility. (authors). 6 refs., 2 appends.

  9. Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells.

    Science.gov (United States)

    Voelker, Aaron R; Eliasmith, Chris

    2018-03-01

    Researchers building spiking neural networks face the challenge of improving the biological plausibility of their model networks while maintaining the ability to quantitatively characterize network behavior. In this work, we extend the theory behind the neural engineering framework (NEF), a method of building spiking dynamical networks, to permit the use of a broad class of synapse models while maintaining prescribed dynamics up to a given order. This theory improves our understanding of how low-level synaptic properties alter the accuracy of high-level computations in spiking dynamical networks. For completeness, we provide characterizations for both continuous-time (i.e., analog) and discrete-time (i.e., digital) simulations. We demonstrate the utility of these extensions by mapping an optimal delay line onto various spiking dynamical networks using higher-order models of the synapse. We show that these networks nonlinearly encode rolling windows of input history, using a scale invariant representation, with accuracy depending on the frequency content of the input signal. Finally, we reveal that these methods provide a novel explanation of time cell responses during a delay task, which have been observed throughout hippocampus, striatum, and cortex.

  10. A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

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    Karim El-Laithy

    2011-01-01

    Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

  11. Synapse-centric mapping of cortical models to the SpiNNaker neuromorphic architecture

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    James Courtney Knight

    2016-09-01

    Full Text Available While the adult human brain has approximately 8.8x10^10 neurons, this number is dwarfed by its 1x10^15 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.

  12. A cortical attractor network with Martinotti cells driven by facilitating synapses.

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    Pradeep Krishnamurthy

    Full Text Available The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  13. Analog memristive synapse in spiking networks implementing unsupervised learning

    Directory of Open Access Journals (Sweden)

    Erika Covi

    2016-10-01

    Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

  14. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

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    Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

    2016-01-01

    Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO 2 -based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

  15. Storage capacity of attractor neural networks with depressing synapses

    International Nuclear Information System (INIS)

    Torres, Joaquin J.; Pantic, Lovorka; Kappen, Hilbert J.

    2002-01-01

    We compute the capacity of a binary neural network with dynamic depressing synapses to store and retrieve an infinite number of patterns. We use a biologically motivated model of synaptic depression and a standard mean-field approach. We find that at T=0 the critical storage capacity decreases with the degree of the depression. We confirm the validity of our main mean-field results with numerical simulations

  16. Memory and pattern storage in neural networks with activity dependent synapses

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    Mejias, J. F.; Torres, J. J.

    2009-01-01

    We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

  17. Multiple synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses

    International Nuclear Information System (INIS)

    Liu, Chen; Wang, Jiang; Wang, Lin; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

    2014-01-01

    Highlights: • Synchronization transitions in hybrid scale-free neuronal networks are investigated. • Multiple synchronization transitions can be induced by the time delay. • Effect of synchronization transitions depends on the ratio of the electrical and chemical synapses. • Coupling strength and the density of inter-neuronal links can enhance the synchronization. -- Abstract: The impacts of information transmission delay on the synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses are investigated. Numerical results show that multiple appearances of synchronization regions transitions can be induced by different information transmission delays. With the time delay increasing, the synchronization of neuronal activities can be enhanced or destroyed, irrespective of the probability of chemical synapses in the whole hybrid neuronal network. In particular, for larger probability of electrical synapses, the regions of synchronous activities appear broader with stronger synchronization ability of electrical synapses compared with chemical ones. Moreover, it can be found that increasing the coupling strength can promote synchronization monotonously, playing the similar role of the increasing the probability of the electrical synapses. Interestingly, the structures and parameters of the scale-free neuronal networks, especially the structural evolvement plays a more subtle role in the synchronization transitions. In the network formation process, it is found that every new vertex is attached to the more old vertices already present in the network, the more synchronous activities will be emerge

  18. Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

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    Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

    2013-09-01

    The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

  19. Impacts of hybrid synapses on the noise-delayed decay in scale-free neural networks

    International Nuclear Information System (INIS)

    Yilmaz, Ergin

    2014-01-01

    Highlights: • We investigate the NDD phenomenon in a hybrid scale-free network. • Electrical synapses are more impressive on the emergence of NDD. • Electrical synapses are more efficient in suppressing of the NDD. • Average degree has two opposite effects on the appearance time of the first spike. - Abstract: We study the phenomenon of noise-delayed decay in a scale-free neural network consisting of excitable FitzHugh–Nagumo neurons. In contrast to earlier works, where only electrical synapses are considered among neurons, we primarily examine the effects of hybrid synapses on the noise-delayed decay in this study. We show that the electrical synaptic coupling is more impressive than the chemical coupling in determining the appearance time of the first-spike and more efficient on the mitigation of the delay time in the detection of a suprathreshold input signal. We obtain that hybrid networks including inhibitory chemical synapses have higher signal detection capabilities than those of including excitatory ones. We also find that average degree exhibits two different effects, which are strengthening and weakening the noise-delayed decay effect depending on the noise intensity

  20. Resolution enhancement in neural networks with dynamical synapses

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    C. C. Alan Fung

    2013-06-01

    Full Text Available Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

  1. Combined effect of chemical and electrical synapses in Hindmarsh-Rose neural networks on synchronization and the rate of information.

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    Baptista, M S; Moukam Kakmeni, F M; Grebogi, C

    2010-09-01

    In this work we studied the combined action of chemical and electrical synapses in small networks of Hindmarsh-Rose (HR) neurons on the synchronous behavior and on the rate of information produced (per time unit) by the networks. We show that if the chemical synapse is excitatory, the larger the chemical synapse strength used the smaller the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find desynchronous behavior. Otherwise, if the chemical synapse is inhibitory, the larger the chemical synapse strength used the larger the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find synchronous behaviors. Finally, we show how to calculate semianalytically an upper bound for the rate of information produced per time unit (Kolmogorov-Sinai entropy) in larger networks. As an application, we show that this upper bound is linearly proportional to the number of neurons in a network whose neurons are highly connected.

  2. The networks scale and coupling parameter in synchronization of neural networks with diluted synapses

    International Nuclear Information System (INIS)

    Li Yanlong; Ma Jun; Chen Yuhong; Xu Wenke; Wang Yinghai

    2008-01-01

    In this paper the influence of the networks scale on the coupling parameter in the synchronization of neural networks with diluted synapses is investigated. Using numerical simulations, an exponential decay form is observed in the extreme case of global coupling among networks and full connection in each network; the larger linked degree becomes, the larger critical coupling intensity becomes; and the oscillation phenomena in the relationship of critical coupling intensity and the number of neural networks layers in the case of small-scale networks are found

  3. Mixed Analog/Digital Matrix-Vector Multiplier for Neural Network Synapses

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Bruun, Erik; Dietrich, Casper

    1996-01-01

    In this work we present a hardware efficient matrix-vector multiplier architecture for artificial neural networks with digitally stored synapse strengths. We present a novel technique for manipulating bipolar inputs based on an analog two's complements method and an accurate current rectifier...

  4. A Neuron- and a Synapse Chip for Artificial Neural Networks

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where...

  5. Cytoskeletal actin dynamics shape a ramifying actin network underpinning immunological synapse formation

    DEFF Research Database (Denmark)

    Fritzsche, Marco; Fernandes, Ricardo A.; Chang, Veronica T.

    2017-01-01

    optical microscopes to analyze resting and activated T cells, we show that, following contact formation with activating surfaces, these cells sequentially rearrange their cortical actin across the entire cell, creating a previously unreported ramifying actin network above the immunological synapse...

  6. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

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    Gabriel Koch Ocker

    2015-08-01

    Full Text Available The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

  7. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

    Science.gov (United States)

    Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

    2015-08-01

    The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

  8. Unsupervised learning in neural networks with short range synapses

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    Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

    2013-01-01

    Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

  9. Emerging phenomena in neural networks with dynamic synapses and their computational implications

    Directory of Open Access Journals (Sweden)

    Joaquin J. eTorres

    2013-04-01

    Full Text Available In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilisation of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behaviour can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

  10. Reduced Synapse and Axon Numbers in the Prefrontal Cortex of Rats Subjected to a Chronic Stress Model for Depression

    Science.gov (United States)

    Csabai, Dávid; Wiborg, Ove; Czéh, Boldizsár

    2018-01-01

    Stressful experiences can induce structural changes in neurons of the limbic system. These cellular changes contribute to the development of stress-induced psychopathologies like depressive disorders. In the prefrontal cortex of chronically stressed animals, reduced dendritic length and spine loss have been reported. This loss of dendritic material should consequently result in synapse loss as well, because of the reduced dendritic surface. But so far, no one studied synapse numbers in the prefrontal cortex of chronically stressed animals. Here, we examined synaptic contacts in rats subjected to an animal model for depression, where animals are exposed to a chronic stress protocol. Our hypothesis was that long term stress should reduce the number of axo-spinous synapses in the medial prefrontal cortex. Adult male rats were exposed to daily stress for 9 weeks and afterward we did a post mortem quantitative electron microscopic analysis to quantify the number and morphology of synapses in the infralimbic cortex. We analyzed asymmetric (Type I) and symmetric (Type II) synapses in all cortical layers in control and stressed rats. We also quantified axon numbers and measured the volume of the infralimbic cortex. In our systematic unbiased analysis, we examined 21,000 axon terminals in total. We found the following numbers in the infralimbic cortex of control rats: 1.15 × 109 asymmetric synapses, 1.06 × 108 symmetric synapses and 1.00 × 108 myelinated axons. The density of asymmetric synapses was 5.5/μm3 and the density of symmetric synapses was 0.5/μm3. Average synapse membrane length was 207 nm and the average axon terminal membrane length was 489 nm. Stress reduced the number of synapses and myelinated axons in the deeper cortical layers, while synapse membrane lengths were increased. These stress-induced ultrastructural changes indicate that neurons of the infralimbic cortex have reduced cortical network connectivity. Such reduced network connectivity is likely

  11. Reduced Synapse and Axon Numbers in the Prefrontal Cortex of Rats Subjected to a Chronic Stress Model for Depression

    Directory of Open Access Journals (Sweden)

    Dávid Csabai

    2018-01-01

    Full Text Available Stressful experiences can induce structural changes in neurons of the limbic system. These cellular changes contribute to the development of stress-induced psychopathologies like depressive disorders. In the prefrontal cortex of chronically stressed animals, reduced dendritic length and spine loss have been reported. This loss of dendritic material should consequently result in synapse loss as well, because of the reduced dendritic surface. But so far, no one studied synapse numbers in the prefrontal cortex of chronically stressed animals. Here, we examined synaptic contacts in rats subjected to an animal model for depression, where animals are exposed to a chronic stress protocol. Our hypothesis was that long term stress should reduce the number of axo-spinous synapses in the medial prefrontal cortex. Adult male rats were exposed to daily stress for 9 weeks and afterward we did a post mortem quantitative electron microscopic analysis to quantify the number and morphology of synapses in the infralimbic cortex. We analyzed asymmetric (Type I and symmetric (Type II synapses in all cortical layers in control and stressed rats. We also quantified axon numbers and measured the volume of the infralimbic cortex. In our systematic unbiased analysis, we examined 21,000 axon terminals in total. We found the following numbers in the infralimbic cortex of control rats: 1.15 × 109 asymmetric synapses, 1.06 × 108 symmetric synapses and 1.00 × 108 myelinated axons. The density of asymmetric synapses was 5.5/μm3 and the density of symmetric synapses was 0.5/μm3. Average synapse membrane length was 207 nm and the average axon terminal membrane length was 489 nm. Stress reduced the number of synapses and myelinated axons in the deeper cortical layers, while synapse membrane lengths were increased. These stress-induced ultrastructural changes indicate that neurons of the infralimbic cortex have reduced cortical network connectivity. Such reduced network

  12. Process for forming synapses in neural networks and resistor therefor

    Science.gov (United States)

    Fu, Chi Y.

    1996-01-01

    Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.

  13. Effect of synapse dilution on the memory retrieval in structured attractor neural networks

    Science.gov (United States)

    Brunel, N.

    1993-08-01

    We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

  14. Recurrent synapses and circuits in the CA3 region of the hippocampus: an associative network.

    Directory of Open Access Journals (Sweden)

    Richard eMiles

    2014-01-01

    Full Text Available In the CA3 region of the hippocampus, pyramidal cells excite other pyramidal cells and interneurons. The axons of CA3 pyramidal cells spread throughout most of the region to form an associative network. These connections were first drawn by Cajal and Lorente de No. Their physiological properties were explored to understand epileptiform discharges generated in the region. Synapses between pairs of pyramidal cells involve one or few release sites and are weaker than connections made by mossy fibres on CA3 pyramidal cells. Synapses with interneurons are rather effective, as needed to control unchecked excitation. We examine contributions of recurrent synapses to epileptiform synchrony, to the genesis of sharp waves in the CA3 region and to population oscillations at theta and gamma frequencies. Recurrent connections in CA3, as other associative cortices, have a lower connectivity spread over a larger area than in primary sensory cortices. This sparse, but wide-ranging connectivity serves the functions of an associative network, including acquisition of neuronal representations as activity in groups of CA3 cells and completion involving the recall from partial cues of these ensemble firing patterns.

  15. Specific Disruption of Hippocampal Mossy Fiber Synapses in a Mouse Model of Familial Alzheimer's Disease

    Science.gov (United States)

    Wilke, Scott A.; Raam, Tara; Antonios, Joseph K.; Bushong, Eric A.; Koo, Edward H.; Ellisman, Mark H.; Ghosh, Anirvan

    2014-01-01

    The earliest stages of Alzheimer's disease (AD) are characterized by deficits in memory and cognition indicating hippocampal pathology. While it is now recognized that synapse dysfunction precedes the hallmark pathological findings of AD, it is unclear if specific hippocampal synapses are particularly vulnerable. Since the mossy fiber (MF) synapse between dentate gyrus (DG) and CA3 regions underlies critical functions disrupted in AD, we utilized serial block-face electron microscopy (SBEM) to analyze MF microcircuitry in a mouse model of familial Alzheimer's disease (FAD). FAD mutant MF terminal complexes were severely disrupted compared to control – they were smaller, contacted fewer postsynaptic spines and had greater numbers of presynaptic filopodial processes. Multi-headed CA3 dendritic spines in the FAD mutant condition were reduced in complexity and had significantly smaller sites of synaptic contact. Significantly, there was no change in the volume of classical dendritic spines at neighboring inputs to CA3 neurons suggesting input-specific defects in the early course of AD related pathology. These data indicate a specific vulnerability of the DG-CA3 network in AD pathogenesis and demonstrate the utility of SBEM to assess circuit specific alterations in mouse models of human disease. PMID:24454724

  16. Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

    Science.gov (United States)

    Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

    2016-07-01

    Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

  17. A Reaction-Diffusion Model for Synapse Growth and Long-Term Memory

    Science.gov (United States)

    Liu, Kang; Lisman, John; Hagan, Michael

    Memory storage involves strengthening of synaptic transmission known as long-term potentiation (LTP). The late phase of LTP is associated with structural processes that enlarge the synapse. Yet, synapses must be stable, despite continual subunit turnover, over the lifetime of an encoded memory. These considerations suggest that synapses are variable-size stable structure (VSSS), meaning they can switch between multiple metastable structures with different sizes. The mechanisms underlying VSSS are poorly understood. While experiments and theory have suggested that the interplay between diffusion and receptor-scaffold interactions can lead to a preferred stable size for synaptic domains, such a mechanism cannot explain how synapses adopt widely different sizes. Here we develop a minimal reaction-diffusion model of VSSS for synapse growth, incorporating the recent observation from super-resolution microscopy that neural activity can build compositional heterogeneities within synaptic domains. We find that introducing such heterogeneities can change the stable domain size in a controlled manner. We discuss a potential connection between this model and experimental data on synapse sizes, and how it provides a possible mechanism to structurally encode graded long-term memory. We acknowledge the support from NSF INSPIRE Award number IOS-1526941 (KL, MFH, JL) and the Brandeis Center for Bioinspired Soft Materials, an NSF MRSEC, DMR- 1420382 (MFH).

  18. Star-coupled Hindmarsh-Rose neural network with chemical synapses

    Science.gov (United States)

    Usha, K.; Subha, P. A.

    We analyze the patterns like synchrony, desynchrony, and Drum head mode in a network of Hindmarsh-Rose (HR) neurons interacting via chemical synapse in unidirectional and bidirectional star topology. A two-coupled system has been studied for synchronization by varying the coupling strength and the parameter describing the activation and inactivation of the fast ion channel. The transverse Lyapunov exponent spectrum is plotted to observe the point of transition from desynchrony to synchrony. The synchronized, desynchronized, and drum head mode regions are observed when the neurons are connected in unidirectional and bidirectional coupling configurations. A detailed analysis about the time evolution of membrane potential corresponding to each region is presented. The annihilation of synchronized region and the expansion of drum head mode region in bidirectional coupling is discussed using parameter space. Our work provides finer insight into the existence and stability of Drum head mode and is useful for designing communication networks.

  19. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

    Science.gov (United States)

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

  20. Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

    International Nuclear Information System (INIS)

    Nishitani, Y.; Kaneko, Y.; Ueda, M.; Fujii, E.; Morie, T.

    2012-01-01

    Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O 3 (PZT)/SrRuO 3 . In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission for learning.

  1. Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses

    International Nuclear Information System (INIS)

    Cofré, Rodrigo; Cessac, Bruno

    2013-01-01

    We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based integrate-and-fire neural network, driven by Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or generalized linear models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions

  2. From in silico astrocyte cell models to neuron-astrocyte network models: A review.

    Science.gov (United States)

    Oschmann, Franziska; Berry, Hugues; Obermayer, Klaus; Lenk, Kerstin

    2018-01-01

    The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Loss of Synapse Repressor MDGA1 Enhances Perisomatic Inhibition, Confers Resistance to Network Excitation, and Impairs Cognitive Function

    Directory of Open Access Journals (Sweden)

    Steven A. Connor

    2017-12-01

    Full Text Available Synaptopathies contributing to neurodevelopmental disorders are linked to mutations in synaptic organizing molecules, including postsynaptic neuroligins, presynaptic neurexins, and MDGAs, which regulate their interaction. The role of MDGA1 in suppressing inhibitory versus excitatory synapses is controversial based on in vitro studies. We show that genetic deletion of MDGA1 in vivo elevates hippocampal CA1 inhibitory, but not excitatory, synapse density and transmission. Furthermore, MDGA1 is selectively expressed by pyramidal neurons and regulates perisomatic, but not distal dendritic, inhibitory synapses. Mdga1−/− hippocampal networks demonstrate muted responses to neural excitation, and Mdga1−/− mice are resistant to induced seizures. Mdga1−/− mice further demonstrate compromised hippocampal long-term potentiation, consistent with observed deficits in spatial and context-dependent learning and memory. These results suggest that mutations in MDGA1 may contribute to cognitive deficits through altered synaptic transmission and plasticity by loss of suppression of inhibitory synapse development in a subcellular domain- and cell-type-selective manner.

  4. Anatomically detailed and large-scale simulations studying synapse loss and synchrony using NeuroBox

    Directory of Open Access Journals (Sweden)

    Markus eBreit

    2016-02-01

    Full Text Available The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic or reconstruction to the simulation platform UG 4 (which harbors a neuroscientific portfolio and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g. new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.

  5. Reciprocal synapses between outer hair cells and their afferent terminals: evidence for a local neural network in the mammalian cochlea.

    Science.gov (United States)

    Thiers, Fabio A; Nadol, Joseph B; Liberman, M Charles

    2008-12-01

    Cochlear outer hair cells (OHCs) serve both as sensory receptors and biological motors. Their sensory function is poorly understood because their afferent innervation, the type-II spiral ganglion cell, has small unmyelinated axons and constitutes only 5% of the cochlear nerve. Reciprocal synapses between OHCs and their type-II terminals, consisting of paired afferent and efferent specialization, have been described in the primate cochlea. Here, we use serial and semi-serial-section transmission electron microscopy to quantify the nature and number of synaptic interactions in the OHC area of adult cats. Reciprocal synapses were found in all OHC rows and all cochlear frequency regions. They were more common among third-row OHCs and in the apical half of the cochlea, where 86% of synapses were reciprocal. The relative frequency of reciprocal synapses was unchanged following surgical transection of the olivocochlear bundle in one cat, confirming that reciprocal synapses were not formed by efferent fibers. In the normal ear, axo-dendritic synapses between olivocochlear terminals and type-II terminals and/or dendrites were as common as synapses between olivocochlear terminals and OHCs, especially in the first row, where, on average, almost 30 such synapses were seen in the region under a single OHC. The results suggest that a complex local neuronal circuitry in the OHC area, formed by the dendrites of type-II neurons and modulated by the olivocochlear system, may be a fundamental property of the mammalian cochlea, rather than a curiosity of the primate ear. This network may mediate local feedback control of, and bidirectional communication among, OHCs throughout the cochlear spiral.

  6. Inference of topology and the nature of synapses, and the flow of information in neuronal networks

    Science.gov (United States)

    Borges, F. S.; Lameu, E. L.; Iarosz, K. C.; Protachevicz, P. R.; Caldas, I. L.; Viana, R. L.; Macau, E. E. N.; Batista, A. M.; Baptista, M. S.

    2018-02-01

    The characterization of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time series. The success of our approach relies on a surprising property found in neuronal networks by which nonadjacent neurons do "understand" each other (positive mutual information), however, this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronization, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of intermodular connectivity, when only mean fields can be measured.

  7. A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory.

    Science.gov (United States)

    Chicca, E; Badoni, D; Dante, V; D'Andreagiovanni, M; Salina, G; Carota, L; Fusi, S; Del Giudice, P

    2003-01-01

    Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network.

  8. Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

    Science.gov (United States)

    Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

    2018-01-01

    Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural

  9. Energy-efficient STDP-based learning circuits with memristor synapses

    Science.gov (United States)

    Wu, Xinyu; Saxena, Vishal; Campbell, Kristy A.

    2014-05-01

    It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-andfire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

  10. Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses

    Directory of Open Access Journals (Sweden)

    Stefano eCavallari

    2014-03-01

    Full Text Available Models of networks of Leaky Integrate-and-Fire neurons (LIF are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and neural population dynamics of conductance-based networks (COBN and current-based networks (CUBN of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity. However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.

  11. Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

    DEFF Research Database (Denmark)

    Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission...... in astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

  12. A shared synapse architecture for efficient FPGA implementation of autoencoders.

    Science.gov (United States)

    Suzuki, Akihiro; Morie, Takashi; Tamukoh, Hakaru

    2018-01-01

    This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers' units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks.

  13. Metaplasticity at CA1 Synapses by Homeostatic Control of Presynaptic Release Dynamics

    Directory of Open Access Journals (Sweden)

    Cary Soares

    2017-10-01

    Full Text Available Summary: Hebbian and homeostatic forms of plasticity operate on different timescales to regulate synaptic strength. The degree of mechanistic overlap between these processes and their mutual influence are still incompletely understood. Here, we report that homeostatic synaptic strengthening induced by prolonged network inactivity compromised the ability of CA1 synapses to exhibit LTP. This effect could not be accounted for by an obvious deficit in the postsynaptic capacity for LTP expression, since neither the fraction of silent synapses nor the ability to induce LTP by two-photon glutamate uncaging were reduced by the homeostatic process. Rather, optical quantal analysis reveals that homeostatically strengthened synapses display a reduced capacity to maintain glutamate release fidelity during repetitive stimulation, ultimately impeding the induction, and thus expression, of LTP. By regulating the short-term dynamics of glutamate release, the homeostatic process thus influences key aspects of dynamic network function and exhibits features of metaplasticity. : Several forms of synaptic plasticity operating over distinct spatiotemporal scales have been described at hippocampal synapses. Whether these distinct plasticity mechanisms interact and influence one another remains incompletely understood. Here, Soares et al. show that homeostatic plasticity induced by network silencing influences short-term release dynamics and Hebbian plasticity rules at hippocampal synapses. Keywords: synapse, LTP, homeostatic plasticity, metaplasticity, iGluSNFR

  14. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  15. A neural network model of ventriloquism effect and aftereffect.

    Science.gov (United States)

    Magosso, Elisa; Cuppini, Cristiano; Ursino, Mauro

    2012-01-01

    Presenting simultaneous but spatially discrepant visual and auditory stimuli induces a perceptual translocation of the sound towards the visual input, the ventriloquism effect. General explanation is that vision tends to dominate over audition because of its higher spatial reliability. The underlying neural mechanisms remain unclear. We address this question via a biologically inspired neural network. The model contains two layers of unimodal visual and auditory neurons, with visual neurons having higher spatial resolution than auditory ones. Neurons within each layer communicate via lateral intra-layer synapses; neurons across layers are connected via inter-layer connections. The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity at the position of the visual stimulus. Main results are: i) the less localized stimulus is strongly biased toward the most localized stimulus and not vice versa; ii) amount of the ventriloquism effect changes with visual-auditory spatial disparity; iii) ventriloquism is a robust behavior of the network with respect to parameter value changes. Moreover, the model implements Hebbian rules for potentiation and depression of lateral synapses, to explain ventriloquism aftereffect (that is, the enduring sound shift after exposure to spatially disparate audio-visual stimuli). By adaptively changing the weights of lateral synapses during cross-modal stimulation, the model produces post-adaptive shifts of auditory localization that agree with in-vivo observations. The model demonstrates that two unimodal layers reciprocally interconnected may explain ventriloquism effect and aftereffect, even without the presence of any convergent multimodal area. The proposed study may provide advancement in understanding neural architecture and mechanisms at the basis of visual-auditory integration in the spatial realm.

  16. Short-term ionic plasticity at GABAergic synapses

    Directory of Open Access Journals (Sweden)

    Joseph Valentino Raimondo

    2012-10-01

    Full Text Available Fast synaptic inhibition in the brain is mediated by the pre-synaptic release of the neurotransmitter γ-Aminobutyric acid (GABA and the post-synaptic activation of GABA-sensitive ionotropic receptors. As with excitatory synapses, it is being increasinly appreciated that a variety of plastic processes occur at inhibitory synapses, which operate over a range of timescales. Here we examine a form of activity-dependent plasticity that is somewhat unique to GABAergic transmission. This involves short-lasting changes to the ionic driving force for the postsynaptic receptors, a process referred to as short-term ionic plasticity. These changes are directly related to the history of activity at inhibitory synapses and are influenced by a variety of factors including the location of the synapse and the post-synaptic cell’s ion regulation mechanisms. We explore the processes underlying this form of plasticity, when and where it can occur, and how it is likely to impact network activity.

  17. Neuron array with plastic synapses and programmable dendrites.

    Science.gov (United States)

    Ramakrishnan, Shubha; Wunderlich, Richard; Hasler, Jennifer; George, Suma

    2013-10-01

    We describe a novel neuromorphic chip architecture that models neurons for efficient computation. Traditional architectures of neuron array chips consist of large scale systems that are interfaced with AER for implementing intra- or inter-chip connectivity. We present a chip that uses AER for inter-chip communication but uses fast, reconfigurable FPGA-style routing with local memory for intra-chip connectivity. We model neurons with biologically realistic channel models, synapses and dendrites. This chip is suitable for small-scale network simulations and can also be used for sequence detection, utilizing directional selectivity properties of dendrites, ultimately for use in word recognition.

  18. Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

    Directory of Open Access Journals (Sweden)

    Cristina Savin

    2014-02-01

    Full Text Available A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.

  19. A neural network model of ventriloquism effect and aftereffect.

    Directory of Open Access Journals (Sweden)

    Elisa Magosso

    Full Text Available Presenting simultaneous but spatially discrepant visual and auditory stimuli induces a perceptual translocation of the sound towards the visual input, the ventriloquism effect. General explanation is that vision tends to dominate over audition because of its higher spatial reliability. The underlying neural mechanisms remain unclear. We address this question via a biologically inspired neural network. The model contains two layers of unimodal visual and auditory neurons, with visual neurons having higher spatial resolution than auditory ones. Neurons within each layer communicate via lateral intra-layer synapses; neurons across layers are connected via inter-layer connections. The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity at the position of the visual stimulus. Main results are: i the less localized stimulus is strongly biased toward the most localized stimulus and not vice versa; ii amount of the ventriloquism effect changes with visual-auditory spatial disparity; iii ventriloquism is a robust behavior of the network with respect to parameter value changes. Moreover, the model implements Hebbian rules for potentiation and depression of lateral synapses, to explain ventriloquism aftereffect (that is, the enduring sound shift after exposure to spatially disparate audio-visual stimuli. By adaptively changing the weights of lateral synapses during cross-modal stimulation, the model produces post-adaptive shifts of auditory localization that agree with in-vivo observations. The model demonstrates that two unimodal layers reciprocally interconnected may explain ventriloquism effect and aftereffect, even without the presence of any convergent multimodal area. The proposed study may provide advancement in understanding neural architecture and mechanisms at the basis of visual-auditory integration in the spatial realm.

  20. Control of bursting synchronization in networks of Hodgkin-Huxley-type neurons with chemical synapses.

    Science.gov (United States)

    Batista, C A S; Viana, R L; Ferrari, F A S; Lopes, S R; Batista, A M; Coninck, J C P

    2013-04-01

    Thermally sensitive neurons present bursting activity for certain temperature ranges, characterized by fast repetitive spiking of action potential followed by a short quiescent period. Synchronization of bursting activity is possible in networks of coupled neurons, and it is sometimes an undesirable feature. Control procedures can suppress totally or partially this collective behavior, with potential applications in deep-brain stimulation techniques. We investigate the control of bursting synchronization in small-world networks of Hodgkin-Huxley-type thermally sensitive neurons with chemical synapses through two different strategies. One is the application of an external time-periodic electrical signal and another consists of a time-delayed feedback signal. We consider the effectiveness of both strategies in terms of protocols of applications suitable to be applied by pacemakers.

  1. Autaptic effects on synchrony of neurons coupled by electrical synapses

    Science.gov (United States)

    Kim, Youngtae

    2017-07-01

    In this paper, we numerically study the effects of a special synapse known as autapse on synchronization of population of Morris-Lecar (ML) neurons coupled by electrical synapses. Several configurations of the ML neuronal populations such as a pair or a ring or a globally coupled network with and without autapses are examined. While most of the papers on the autaptic effects on synchronization have used networks of neurons of same spiking rate, we use the network of neurons of different spiking rates. We find that the optimal autaptic coupling strength and the autaptic time delay enhance synchronization in our neural networks. We use the phase response curve analysis to explain the enhanced synchronization by autapses. Our findings reveal the important relationship between the intraneuronal feedback loop and the interneuronal coupling.

  2. Impact of weak excitatory synapses on chaotic transients in a diffusively coupled Morris-Lecar neuronal network

    Energy Technology Data Exchange (ETDEWEB)

    Lafranceschina, Jacopo, E-mail: jlafranceschina@alaska.edu; Wackerbauer, Renate, E-mail: rawackerbauer@alaska.edu [Department of Physics, University of Alaska, Fairbanks, Alaska 99775-5920 (United States)

    2015-01-15

    Spatiotemporal chaos collapses to either a rest state or a propagating pulse solution in a ring network of diffusively coupled, excitable Morris-Lecar neurons. Weak excitatory synapses can increase the Lyapunov exponent, expedite the collapse, and promote the collapse to the rest state rather than the pulse state. A single traveling pulse solution may no longer be asymptotic for certain combinations of network topology and (weak) coupling strengths, and initiate spatiotemporal chaos. Multiple pulses can cause chaos initiation due to diffusive and synaptic pulse-pulse interaction. In the presence of chaos initiation, intermittent spatiotemporal chaos exists until typically a collapse to the rest state.

  3. Impact of weak excitatory synapses on chaotic transients in a diffusively coupled Morris-Lecar neuronal network

    International Nuclear Information System (INIS)

    Lafranceschina, Jacopo; Wackerbauer, Renate

    2015-01-01

    Spatiotemporal chaos collapses to either a rest state or a propagating pulse solution in a ring network of diffusively coupled, excitable Morris-Lecar neurons. Weak excitatory synapses can increase the Lyapunov exponent, expedite the collapse, and promote the collapse to the rest state rather than the pulse state. A single traveling pulse solution may no longer be asymptotic for certain combinations of network topology and (weak) coupling strengths, and initiate spatiotemporal chaos. Multiple pulses can cause chaos initiation due to diffusive and synaptic pulse-pulse interaction. In the presence of chaos initiation, intermittent spatiotemporal chaos exists until typically a collapse to the rest state

  4. Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

    Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

  5. Seizures beget seizures in temporal lobe epilepsies: the boomerang effects of newly formed aberrant kainatergic synapses.

    Science.gov (United States)

    Ben-Ari, Yehezkel; Crepel, Valérie; Represa, Alfonso

    2008-01-01

    Do temporal lobe epilepsy (TLE) seizures in adults promote further seizures? Clinical and experimental data suggest that new synapses are formed after an initial episode of status epilepticus, however their contribution to the transformation of a naive network to an epileptogenic one has been debated. Recent experimental data show that newly formed aberrant excitatory synapses on the granule cells of the fascia dentate operate by means of kainate receptor-operated signals that are not present on naive granule cells. Therefore, genuine epileptic networks rely on signaling cascades that differentiate them from naive networks. Recurrent limbic seizures generated by the activation of kainate receptors and synapses in naive animals lead to the formation of novel synapses that facilitate the emergence of further seizures. This negative, vicious cycle illustrates the central role of reactive plasticity in neurological disorders.

  6. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    Science.gov (United States)

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

  7. Integrated plasticity at inhibitory and excitatory synapses in the cerebellar circuit

    Directory of Open Access Journals (Sweden)

    Lisa eMapelli

    2015-05-01

    Full Text Available The way long-term potentiation (LTP and depression (LTD are integrated within the different synapses of brain neuronal circuits is poorly understood. In order to progress beyond the identification of specific molecular mechanisms, a system in which multiple forms of plasticity can be correlated with large-scale neural processing is required. In this paper we take as an example the cerebellar network , in which extensive investigations have revealed LTP and LTD at several excitatory and inhibitory synapses. Cerebellar LTP and LTD occur in all three main cerebellar subcircuits (granular layer, molecular layer, deep cerebellar nuclei and correspondingly regulate the function of their three main neurons: granule cells (GrCs, Purkinje cells (PCs and deep cerebellar nuclear (DCN cells. All these neurons, in addition to be excited, are reached by feed-forward and feed-back inhibitory connections, in which LTP and LTD may either operate synergistically or homeostatically in order to control information flow through the circuit. Although the investigation of individual synaptic plasticities in vitro is essential to prove their existence and mechanisms, it is insufficient to generate a coherent view of their impact on network functioning in vivo. Recent computational models and cell-specific genetic mutations in mice are shedding light on how plasticity at multiple excitatory and inhibitory synapses might regulate neuronal activities in the cerebellar circuit and contribute to learning and memory and behavioral control.

  8. Integrated plasticity at inhibitory and excitatory synapses in the cerebellar circuit.

    Science.gov (United States)

    Mapelli, Lisa; Pagani, Martina; Garrido, Jesus A; D'Angelo, Egidio

    2015-01-01

    The way long-term potentiation (LTP) and depression (LTD) are integrated within the different synapses of brain neuronal circuits is poorly understood. In order to progress beyond the identification of specific molecular mechanisms, a system in which multiple forms of plasticity can be correlated with large-scale neural processing is required. In this paper we take as an example the cerebellar network, in which extensive investigations have revealed LTP and LTD at several excitatory and inhibitory synapses. Cerebellar LTP and LTD occur in all three main cerebellar subcircuits (granular layer, molecular layer, deep cerebellar nuclei) and correspondingly regulate the function of their three main neurons: granule cells (GrCs), Purkinje cells (PCs) and deep cerebellar nuclear (DCN) cells. All these neurons, in addition to be excited, are reached by feed-forward and feed-back inhibitory connections, in which LTP and LTD may either operate synergistically or homeostatically in order to control information flow through the circuit. Although the investigation of individual synaptic plasticities in vitro is essential to prove their existence and mechanisms, it is insufficient to generate a coherent view of their impact on network functioning in vivo. Recent computational models and cell-specific genetic mutations in mice are shedding light on how plasticity at multiple excitatory and inhibitory synapses might regulate neuronal activities in the cerebellar circuit and contribute to learning and memory and behavioral control.

  9. Advances in synapse formation: forging connections in the worm.

    Science.gov (United States)

    Cherra, Salvatore J; Jin, Yishi

    2015-01-01

    Synapse formation is the quintessential process by which neurons form specific connections with their targets to enable the development of functional circuits. Over the past few decades, intense research efforts have identified thousands of proteins that localize to the pre- and postsynaptic compartments. Genetic dissection has provided important insights into the nexus of the molecular and cellular network, and has greatly advanced our knowledge about how synapses form and function physiologically. Moreover, recent studies have highlighted the complex regulation of synapse formation with the identification of novel mechanisms involving cell interactions from non-neuronal sources. In this review, we cover the conserved pathways required for synaptogenesis and place specific focus on new themes of synapse modulation arising from studies in Caenorhabditis elegans. For further resources related to this article, please visit the WIREs website. The authors have declared no conflicts of interest for this article. © 2014 Wiley Periodicals, Inc.

  10. Memory Synapses Are Defined by Distinct Molecular Complexes: A Proposal.

    Science.gov (United States)

    Sossin, Wayne S

    2018-01-01

    Synapses are diverse in form and function. While there are strong evidential and theoretical reasons for believing that memories are stored at synapses, the concept of a specialized "memory synapse" is rarely discussed. Here, we review the evidence that memories are stored at the synapse and consider the opposing possibilities. We argue that if memories are stored in an active fashion at synapses, then these memory synapses must have distinct molecular complexes that distinguish them from other synapses. In particular, examples from Aplysia sensory-motor neuron synapses and synapses on defined engram neurons in rodent models are discussed. Specific hypotheses for molecular complexes that define memory synapses are presented, including persistently active kinases, transmitter receptor complexes and trans-synaptic adhesion proteins.

  11. Deficits in synaptic function occur at medial perforant path-dentate granule cell synapses prior to Schaffer collateral-CA1 pyramidal cell synapses in the novel TgF344-Alzheimer's Disease Rat Model.

    Science.gov (United States)

    Smith, Lindsey A; McMahon, Lori L

    2018-02-01

    Alzheimer's disease (AD) pathology begins decades prior to onset of clinical symptoms, and the entorhinal cortex and hippocampus are among the first and most extensively impacted brain regions. The TgF344-AD rat model, which more fully recapitulates human AD pathology in an age-dependent manner, is a next generation preclinical rodent model for understanding pathophysiological processes underlying the earliest stages of AD (Cohen et al., 2013). Whether synaptic alterations occur in hippocampus prior to reported learning and memory deficit is not known. Furthermore, it is not known if specific hippocampal synapses are differentially affected by progressing AD pathology, or if synaptic deficits begin to appear at the same age in males and females in this preclinical model. Here, we investigated the time-course of synaptic changes in basal transmission, paired-pulse ratio, as an indirect measure of presynaptic release probability, long-term potentiation (LTP), and dendritic spine density at two hippocampal synapses in male and ovariectomized female TgF344-AD rats and wildtype littermates, prior to reported behavioral deficits. Decreased basal synaptic transmission begins at medial perforant path-dentate granule cell (MPP-DGC) synapses prior to Schaffer-collateral-CA1 (CA3-CA1) synapses, in the absence of a change in paired-pulse ratio (PPR) or dendritic spine density. N-methyl-d-aspartate receptor (NMDAR)-dependent LTP magnitude is unaffected at CA3-CA1 synapses at 6, 9, and 12months of age, but is significantly increased at MPP-DGC synapses in TgF344-AD rats at 6months only. Sex differences were only observed at CA3-CA1 synapses where the decrease in basal transmission occurs at a younger age in males versus females. These are the first studies to define presymptomatic alterations in hippocampal synaptic transmission in the TgF344-AD rat model. The time course of altered synaptic transmission mimics the spread of pathology through hippocampus in human AD and provides

  12. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

    Science.gov (United States)

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

  13. Autism-Associated Chromatin Regulator Brg1/SmarcA4 Is Required for Synapse Development and Myocyte Enhancer Factor 2-Mediated Synapse Remodeling.

    Science.gov (United States)

    Zhang, Zilai; Cao, Mou; Chang, Chia-Wei; Wang, Cindy; Shi, Xuanming; Zhan, Xiaoming; Birnbaum, Shari G; Bezprozvanny, Ilya; Huber, Kimberly M; Wu, Jiang I

    2016-01-01

    Synapse development requires normal neuronal activities and the precise expression of synapse-related genes. Dysregulation of synaptic genes results in neurological diseases such as autism spectrum disorders (ASD). Mutations in genes encoding chromatin-remodeling factor Brg1/SmarcA4 and its associated proteins are the genetic causes of several developmental diseases with neurological defects and autistic symptoms. Recent large-scale genomic studies predicted Brg1/SmarcA4 as one of the key nodes of the ASD gene network. We report that Brg1 deletion in early postnatal hippocampal neurons led to reduced dendritic spine density and maturation and impaired synapse activities. In developing mice, neuronal Brg1 deletion caused severe neurological defects. Gene expression analyses indicated that Brg1 regulates a significant number of genes known to be involved in synapse function and implicated in ASD. We found that Brg1 is required for dendritic spine/synapse elimination mediated by the ASD-associated transcription factor myocyte enhancer factor 2 (MEF2) and that Brg1 regulates the activity-induced expression of a specific subset of genes that overlap significantly with the targets of MEF2. Our analyses showed that Brg1 interacts with MEF2 and that MEF2 is required for Brg1 recruitment to target genes in response to neuron activation. Thus, Brg1 plays important roles in both synapse development/maturation and MEF2-mediated synapse remodeling. Our study reveals specific functions of the epigenetic regulator Brg1 in synapse development and provides insights into its role in neurological diseases such as ASD. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  14. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.

    Science.gov (United States)

    Qiao, Ning; Mostafa, Hesham; Corradi, Federico; Osswald, Marc; Stefanini, Fabio; Sumislawska, Dora; Indiveri, Giacomo

    2015-01-01

    Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

  15. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems.

    Science.gov (United States)

    Wang, Zhongqiang; Ambrogio, Stefano; Balatti, Simone; Ielmini, Daniele

    2014-01-01

    Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.

  16. Otanps synapse linear relation multiplier circuit

    International Nuclear Information System (INIS)

    Chible, H.

    2008-01-01

    In this paper, a four quadrant VLSI analog multiplier will be proposed, in order to be used in the implementation of the neurons and synapses modules of the artificial neural networks. The main characteristics of this multiplier are the small silicon area and the low power consumption and the high value of the weight input voltage. (author)

  17. Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

    International Nuclear Information System (INIS)

    Zhou Liming; Zhang Yingyue; Chen Tianlun

    2005-01-01

    Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.

  18. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

    Directory of Open Access Journals (Sweden)

    Stefano eAmbrogio

    2016-03-01

    Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

  19. Spin switches for compact implementation of neuron and synapse

    Energy Technology Data Exchange (ETDEWEB)

    Quang Diep, Vinh, E-mail: vdiep@purdue.edu; Sutton, Brian; Datta, Supriyo [School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States); Behin-Aein, Behtash [GLOBALFOUNDRIES, Inc., Sunnyvale, California 94085 (United States)

    2014-06-02

    Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to explore the possibility of a hardware neural network implementation using a spin switch (SS) as its basic building block. SS is a recently proposed device based on established technology with a transistor-like gain and input-output isolation. This allows neural networks to be constructed with purely passive interconnections without intervening clocks or amplifiers. The weights for the neural network are conveniently adjusted through analog voltages that can be stored in a non-volatile manner in an underlying CMOS layer using a floating gate low dropout voltage regulator. The operation of a multi-layer SS neural network designed for character recognition is demonstrated using a standard simulation model based on coupled Landau-Lifshitz-Gilbert equations, one for each magnet in the network.

  20. Spin switches for compact implementation of neuron and synapse

    International Nuclear Information System (INIS)

    Quang Diep, Vinh; Sutton, Brian; Datta, Supriyo; Behin-Aein, Behtash

    2014-01-01

    Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to explore the possibility of a hardware neural network implementation using a spin switch (SS) as its basic building block. SS is a recently proposed device based on established technology with a transistor-like gain and input-output isolation. This allows neural networks to be constructed with purely passive interconnections without intervening clocks or amplifiers. The weights for the neural network are conveniently adjusted through analog voltages that can be stored in a non-volatile manner in an underlying CMOS layer using a floating gate low dropout voltage regulator. The operation of a multi-layer SS neural network designed for character recognition is demonstrated using a standard simulation model based on coupled Landau-Lifshitz-Gilbert equations, one for each magnet in the network

  1. A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128K synapses

    Directory of Open Access Journals (Sweden)

    Ning eQiao

    2015-04-01

    Full Text Available Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm 2 , and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

  2. ASIC-dependent LTP at multiple glutamatergic synapses in amygdala network is required for fear memory.

    Science.gov (United States)

    Chiang, Po-Han; Chien, Ta-Chun; Chen, Chih-Cheng; Yanagawa, Yuchio; Lien, Cheng-Chang

    2015-05-19

    Genetic variants in the human ortholog of acid-sensing ion channel-1a subunit (ASIC1a) gene are associated with panic disorder and amygdala dysfunction. Both fear learning and activity-induced long-term potentiation (LTP) of cortico-basolateral amygdala (BLA) synapses are impaired in ASIC1a-null mice, suggesting a critical role of ASICs in fear memory formation. In this study, we found that ASICs were differentially expressed within the amygdala neuronal population, and the extent of LTP at various glutamatergic synapses correlated with the level of ASIC expression in postsynaptic neurons. Importantly, selective deletion of ASIC1a in GABAergic cells, including amygdala output neurons, eliminated LTP in these cells and reduced fear learning to the same extent as that found when ASIC1a was selectively abolished in BLA glutamatergic neurons. Thus, fear learning requires ASIC-dependent LTP at multiple amygdala synapses, including both cortico-BLA input synapses and intra-amygdala synapses on output neurons.

  3. Neuromorphic function learning with carbon nanotube based synapses

    International Nuclear Information System (INIS)

    Gacem, Karim; Filoramo, Arianna; Derycke, Vincent; Retrouvey, Jean-Marie; Chabi, Djaafar; Zhao, Weisheng; Klein, Jacques-Olivier

    2013-01-01

    The principle of using nanoscale memory devices as artificial synapses in neuromorphic circuits is recognized as a promising way to build ground-breaking circuit architectures tolerant to defects and variability. Yet, actual experimental demonstrations of the neural network type of circuits based on non-conventional/non-CMOS memory devices and displaying function learning capabilities remain very scarce. We show here that carbon-nanotube-based memory elements can be used as artificial synapses, combined with conventional neurons and trained to perform functions through the application of a supervised learning algorithm. The same ensemble of eight devices can notably be trained multiple times to code successively any three-input linearly separable Boolean logic function despite device-to-device variability. This work thus represents one of the very few demonstrations of actual function learning with synapses based on nanoscale building blocks. The potential of such an approach for the parallel learning of multiple and more complex functions is also evaluated. (paper)

  4. Glutamate synapses in human cognitive disorders.

    Science.gov (United States)

    Volk, Lenora; Chiu, Shu-Ling; Sharma, Kamal; Huganir, Richard L

    2015-07-08

    Accumulating data, including those from large genetic association studies, indicate that alterations in glutamatergic synapse structure and function represent a common underlying pathology in many symptomatically distinct cognitive disorders. In this review, we discuss evidence from human genetic studies and data from animal models supporting a role for aberrant glutamatergic synapse function in the etiology of intellectual disability (ID), autism spectrum disorder (ASD), and schizophrenia (SCZ), neurodevelopmental disorders that comprise a significant proportion of human cognitive disease and exact a substantial financial and social burden. The varied manifestations of impaired perceptual processing, executive function, social interaction, communication, and/or intellectual ability in ID, ASD, and SCZ appear to emerge from altered neural microstructure, function, and/or wiring rather than gross changes in neuron number or morphology. Here, we review evidence that these disorders may share a common underlying neuropathy: altered excitatory synapse function. We focus on the most promising candidate genes affecting glutamatergic synapse function, highlighting the likely disease-relevant functional consequences of each. We first present a brief overview of glutamatergic synapses and then explore the genetic and phenotypic evidence for altered glutamate signaling in ID, ASD, and SCZ.

  5. Regulation of dopamine D1 receptor dynamics within the postsynaptic density of hippocampal glutamate synapses.

    Directory of Open Access Journals (Sweden)

    Laurent Ladepeche

    Full Text Available Dopamine receptor potently modulates glutamate signalling, synaptic plasticity and neuronal network adaptations in various pathophysiological processes. Although key intracellular signalling cascades have been identified, the cellular mechanism by which dopamine and glutamate receptor-mediated signalling interplay at glutamate synapse remain poorly understood. Among the cellular mechanisms proposed to aggregate D1R in glutamate synapses, the direct interaction between D1R and the scaffold protein PSD95 or the direct interaction with the glutamate NMDA receptor (NMDAR have been proposed. To tackle this question we here used high-resolution single nanoparticle imaging since it provides a powerful way to investigate at the sub-micron resolution the dynamic interaction between these partners in live synapses. We demonstrate in hippocampal neuronal networks that dopamine D1 receptors (D1R laterally diffuse within glutamate synapses, in which their diffusion is reduced. Disrupting the interaction between D1R and PSD95, through genetical manipulation and competing peptide, did not affect D1R dynamics in glutamatergic synapses. However, preventing the physical interaction between D1R and the GluN1 subunit of NMDAR abolished the synaptic stabilization of diffusing D1R. Together, these data provide direct evidence that the interaction between D1R and NMDAR in synapses participate in the building of the dopamine-receptor-mediated signalling, and most likely to the glutamate-dopamine cross-talk.

  6. Remodeling of hippocampal spine synapses in the rat learned helplessness model of depression.

    Science.gov (United States)

    Hajszan, Tibor; Dow, Antonia; Warner-Schmidt, Jennifer L; Szigeti-Buck, Klara; Sallam, Nermin L; Parducz, Arpad; Leranth, Csaba; Duman, Ronald S

    2009-03-01

    Although it has been postulated for many years that depression is associated with loss of synapses, primarily in the hippocampus, and that antidepressants facilitate synapse growth, we still lack ultrastructural evidence that changes in depressive behavior are indeed correlated with structural synaptic modifications. We analyzed hippocampal spine synapses of male rats (n=127) with electron microscopic stereology in association with performance in the learned helplessness paradigm. Inescapable footshock (IES) caused an acute and persistent loss of spine synapses in each of CA1, CA3, and dentate gyrus, which was associated with a severe escape deficit in learned helplessness. On the other hand, IES elicited no significant synaptic alterations in motor cortex. A single injection of corticosterone reproduced both the hippocampal synaptic changes and the behavioral responses induced by IES. Treatment of IES-exposed animals for 6 days with desipramine reversed both the hippocampal spine synapse loss and the escape deficit in learned helplessness. We noted, however, that desipramine failed to restore the number of CA1 spine synapses to nonstressed levels, which was associated with a minor escape deficit compared with nonstressed control rats. Shorter, 1-day or 3-day desipramine treatments, however, had neither synaptic nor behavioral effects. These results indicate that changes in depressive behavior are associated with remarkable remodeling of hippocampal spine synapses at the ultrastructural level. Because spine synapse loss contributes to hippocampal dysfunction, this cellular mechanism may be an important component in the neurobiology of stress-related disorders such as depression.

  7. Defects of the Glycinergic Synapse in Zebrafish

    Science.gov (United States)

    Ogino, Kazutoyo; Hirata, Hiromi

    2016-01-01

    Glycine mediates fast inhibitory synaptic transmission. Physiological importance of the glycinergic synapse is well established in the brainstem and the spinal cord. In humans, the loss of glycinergic function in the spinal cord and brainstem leads to hyperekplexia, which is characterized by an excess startle reflex to sudden acoustic or tactile stimulation. In addition, glycinergic synapses in this region are also involved in the regulation of respiration and locomotion, and in the nociceptive processing. The importance of the glycinergic synapse is conserved across vertebrate species. A teleost fish, the zebrafish, offers several advantages as a vertebrate model for research of glycinergic synapse. Mutagenesis screens in zebrafish have isolated two motor defective mutants that have pathogenic mutations in glycinergic synaptic transmission: bandoneon (beo) and shocked (sho). Beo mutants have a loss-of-function mutation of glycine receptor (GlyR) β-subunit b, alternatively, sho mutant is a glycinergic transporter 1 (GlyT1) defective mutant. These mutants are useful animal models for understanding of glycinergic synaptic transmission and for identification of novel therapeutic agents for human diseases arising from defect in glycinergic transmission, such as hyperekplexia or glycine encephalopathy. Recent advances in techniques for genome editing and for imaging and manipulating of a molecule or a physiological process make zebrafish more attractive model. In this review, we describe the glycinergic defective zebrafish mutants and the technical advances in both forward and reverse genetic approaches as well as in vivo visualization and manipulation approaches for the study of the glycinergic synapse in zebrafish. PMID:27445686

  8. Shaping inhibition: activity dependent structural plasticity of GABAergic synapses

    Directory of Open Access Journals (Sweden)

    Carmen E Flores

    2014-10-01

    Full Text Available Inhibitory transmission through the neurotransmitter Ɣ-aminobutyric acid (GABA shapes network activity in the mammalian cerebral cortex by filtering synaptic incoming information and dictating the activity of principal cells. The incredibly diverse population of cortical neurons that use GABA as neurotransmitter shows an equally diverse range of mechanisms that regulate changes in the strength of GABAergic synaptic transmission and allow them to dynamically follow and command the activity of neuronal ensembles. Similarly to glutamatergic synaptic transmission, activity-dependent functional changes in inhibitory neurotransmission are accompanied by alterations in GABAergic synapse structure that range from morphological reorganization of postsynaptic density to de novo formation and elimination of inhibitory contacts. Here we review several aspects of structural plasticity of inhibitory synapses, including its induction by different forms of neuronal activity, behavioral and sensory experience and the molecular mechanisms and signaling pathways involved. We discuss the functional consequences of GABAergic synapse structural plasticity for information processing and memory formation in view of the heterogenous nature of the structural plasticity phenomena affecting inhibitory synapses impinging on somatic and dendritic compartments of cortical and hippocampal neurons.

  9. The biochemical anatomy of cortical inhibitory synapses.

    Directory of Open Access Journals (Sweden)

    Elizabeth A Heller

    Full Text Available Classical electron microscopic studies of the mammalian brain revealed two major classes of synapses, distinguished by the presence of a large postsynaptic density (PSD exclusively at type 1, excitatory synapses. Biochemical studies of the PSD have established the paradigm of the synapse as a complex signal-processing machine that controls synaptic plasticity. We report here the results of a proteomic analysis of type 2, inhibitory synaptic complexes isolated by affinity purification from the cerebral cortex. We show that these synaptic complexes contain a variety of neurotransmitter receptors, neural cell-scaffolding and adhesion molecules, but that they are entirely lacking in cell signaling proteins. This fundamental distinction between the functions of type 1 and type 2 synapses in the nervous system has far reaching implications for models of synaptic plasticity, rapid adaptations in neural circuits, and homeostatic mechanisms controlling the balance of excitation and inhibition in the mature brain.

  10. Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

    Science.gov (United States)

    Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

    2016-01-01

    Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

  11. Accelerated Intoxication of GABAergic Synapses by Botulinum Neurotoxin A Disinhibits Stem Cell-Derived Neuron Networks Prior to Network Silencing

    Science.gov (United States)

    2015-04-23

    administered BoNT can lead to central nervous system intoxication is currently being debated. Recent findings in vitro and in vivo suggest that BoNT...Literature 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE Accelerated intoxication of GABAergic synapses by botulinum neurotoxin A disinhibits 5a...April 2015 Published: 23 April 2015 Citation: Beske PH, Scheeler SM, AdlerM and McNutt PM (2015) Accelerated intoxication of GABAergic synapses by

  12. Coexisting chaotic attractors in a single neuron model with adapting feedback synapse

    International Nuclear Information System (INIS)

    Li Chunguang; Chen Guanrong

    2005-01-01

    In this paper, we consider the nonlinear dynamical behavior of a single neuron model with adapting feedback synapse, and show that chaotic behaviors exist in this model. In some parameter domain, we observe two coexisting chaotic attractors, switching from the coexisting chaotic attractors to a connected chaotic attractor, and then switching back to the two coexisting chaotic attractors. We confirm the chaoticity by simulations with phase plots, waveform plots, and power spectra

  13. On-chip photonic synapse.

    Science.gov (United States)

    Cheng, Zengguang; Ríos, Carlos; Pernice, Wolfram H P; Wright, C David; Bhaskaran, Harish

    2017-09-01

    The search for new "neuromorphic computing" architectures that mimic the brain's approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.

  14. The immunological synapse

    DEFF Research Database (Denmark)

    Klemmensen, Thomas; Pedersen, Lars Ostergaard; Geisler, Carsten

    2003-01-01

    . A distinct 3-dimensional supramolecular structure at the T cell/APC interface has been suggested to be involved in the information transfer. Due to its functional analogy to the neuronal synapse, the structure has been termed the "immunological synapse" (IS). Here, we review molecular aspects concerning...

  15. P2X7 Receptors Drive Spine Synapse Plasticity in the Learned Helplessness Model of Depression.

    Science.gov (United States)

    Otrokocsi, Lilla; Kittel, Ágnes; Sperlágh, Beáta

    2017-10-01

    Major depressive disorder is characterized by structural and functional abnormalities of cortical and limbic brain areas, including a decrease in spine synapse number in the dentate gyrus of the hippocampus. Recent studies highlighted that both genetic and pharmacological invalidation of the purinergic P2X7 receptor (P2rx7) leads to antidepressant-like phenotype in animal experiments; however, the impact of P2rx7 on depression-related structural changes in the hippocampus is not clarified yet. Effects of genetic deletion of P2rx7s on depressive-like behavior and spine synapse density in the dentate gyrus were investigated using the learned helplessness mouse model of depression. We demonstrate that in wild-type animals, inescapable footshocks lead to learned helplessness behavior reflected in increased latency and number of escape failures to subsequent escapable footshocks. This behavior is accompanied with downregulation of mRNA encoding P2rx7 and decrease of spine synapse density in the dentate gyrus as determined by electron microscopic stereology. In addition, a decrease in synaptopodin but not in PSD95 and NR2B/GluN2B protein level was also observed under these conditions. Whereas the absence of P2rx7 was characterized by escape deficit, no learned helpless behavior is observed in these animals. Likewise, no decrease in spine synapse number and synaptopodin protein levels was detected in response to inescapable footshocks in P2rx7-deficient animals. Our findings suggest the endogenous activation of P2rx7s in the learned helplessness model of depression and decreased plasticity of spine synapses in P2rx7-deficient mice might explain the resistance of these animals to repeated stressful stimuli. © The Author 2017. Published by Oxford University Press on behalf of CINP.

  16. Neuroligin-1 loss is associated with reduced tenacity of excitatory synapses.

    Directory of Open Access Journals (Sweden)

    Adel Zeidan

    Full Text Available Neuroligins (Nlgns are postsynaptic, integral membrane cell adhesion molecules that play important roles in the formation, validation, and maturation of synapses in the mammalian central nervous system. Given their prominent roles in the life cycle of synapses, it might be expected that the loss of neuroligin family members would affect the stability of synaptic organization, and ultimately, affect the tenacity and persistence of individual synaptic junctions. Here we examined whether and to what extent the loss of Nlgn-1 affects the dynamics of several key synaptic molecules and the constancy of their contents at individual synapses over time. Fluorescently tagged versions of the postsynaptic scaffold molecule PSD-95, the AMPA-type glutamate receptor subunit GluA2 and the presynaptic vesicle molecule SV2A were expressed in primary cortical cultures from Nlgn-1 KO mice and wild-type (WT littermates, and live imaging was used to follow the constancy of their contents at individual synapses over periods of 8-12 hours. We found that the loss of Nlgn-1 was associated with larger fluctuations in the synaptic contents of these molecules and a poorer preservation of their contents at individual synapses. Furthermore, rates of synaptic turnover were somewhat greater in neurons from Nlgn-1 knockout mice. Finally, the increased GluA2 redistribution rates observed in neurons from Nlgn-1 knockout mice were negated by suppressing spontaneous network activity. These findings suggest that the loss of Nlgn-1 is associated with some use-dependent destabilization of excitatory synapse organization, and indicate that in the absence of Nlgn-1, the tenacity of excitatory synapses might be somewhat impaired.

  17. The brain as a "hyper-network": the key role of neural networks as main producers of the integrated brain actions especially via the "broadcasted" neuroconnectomics.

    Science.gov (United States)

    Agnati, Luigi F; Marcoli, Manuela; Maura, Guido; Woods, Amina; Guidolin, Diego

    2018-06-01

    Investigations of brain complex integrative actions should consider beside neural networks, glial, extracellular molecular, and fluid channels networks. The present paper proposes that all these networks are assembled into the brain hyper-network that has as fundamental components, the tetra-partite synapses, formed by neural, glial, and extracellular molecular networks. Furthermore, peri-synaptic astrocytic processes by modulating the perviousness of extracellular fluid channels control the signals impinging on the tetra-partite synapses. It has also been surmised that global signalling via astrocytes networks and highly pervasive signals, such as electromagnetic fields (EMFs), allow the appropriate integration of the various networks especially at crucial nodes level, the tetra-partite synapses. As a matter of fact, it has been shown that astrocytes can form gap-junction-coupled syncytia allowing intercellular communication characterised by a rapid and possibly long-distance transfer of signals. As far as the EMFs are concerned, the concept of broadcasted neuroconnectomics (BNC) has been introduced to describe highly pervasive signals involved in resetting the information handling of brain networks at various miniaturisation levels. In other words, BNC creates, thanks to the EMFs, generated especially by neurons, different assemblages among the various networks forming the brain hyper-network. Thus, it is surmised that neuronal networks are the "core components" of the brain hyper-network that has as special "nodes" the multi-facet tetra-partite synapses. Furthermore, it is suggested that investigations on the functional plasticity of multi-partite synapses in response to BNC can be the background for a new understanding and perhaps a new modelling of brain morpho-functional organisation and integrative actions.

  18. A model of microsaccade-related neural responses induced by short-term depression in thalamocortical synapses

    Directory of Open Access Journals (Sweden)

    Wujie eYuan

    2013-04-01

    Full Text Available Microsaccades during fixation have been suggested to counteract visual fading. Recent experi- ments have also observed microsaccade-related neural responses from cellular record, scalp elec- troencephalogram (EEG and functional magnetic resonance imaging (fMRI. The underlying mechanism, however, is not yet understood and highly debated. It has been proposed that the neural activity of primary visual cortex (V1 is a crucial component for counteracting visual adaptation. In this paper, we use computational modeling to investigate how short-term depres- sion (STD in thalamocortical synapses might affect the neural responses of V1 in the presence of microsaccades. Our model not only gives a possible synaptic explanation for microsaccades in counteracting visual fading, but also reproduces several features in experimental findings. These modeling results suggest that STD in thalamocortical synapses plays an important role in microsaccade-related neural responses and the model may be useful for further investigation of behavioral properties and functional roles of microsaccades.

  19. A model of microsaccade-related neural responses induced by short-term depression in thalamocortical synapses

    Science.gov (United States)

    Yuan, Wu-Jie; Dimigen, Olaf; Sommer, Werner; Zhou, Changsong

    2013-01-01

    Microsaccades during fixation have been suggested to counteract visual fading. Recent experiments have also observed microsaccade-related neural responses from cellular record, scalp electroencephalogram (EEG), and functional magnetic resonance imaging (fMRI). The underlying mechanism, however, is not yet understood and highly debated. It has been proposed that the neural activity of primary visual cortex (V1) is a crucial component for counteracting visual adaptation. In this paper, we use computational modeling to investigate how short-term depression (STD) in thalamocortical synapses might affect the neural responses of V1 in the presence of microsaccades. Our model not only gives a possible synaptic explanation for microsaccades in counteracting visual fading, but also reproduces several features in experimental findings. These modeling results suggest that STD in thalamocortical synapses plays an important role in microsaccade-related neural responses and the model may be useful for further investigation of behavioral properties and functional roles of microsaccades. PMID:23630494

  20. Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors.

    Science.gov (United States)

    Qian, Chuan; Sun, Jia; Kong, Ling-An; Gou, Guangyang; Yang, Junliang; He, Jun; Gao, Yongli; Wan, Qing

    2016-10-05

    Realization of biological synapses using electronic devices is regarded as the basic building blocks for neuromorphic engineering and artificial neural network. With the advantages of biocompatibility, low cost, flexibility, and compatible with printing and roll-to-roll processes, the artificial synapse based on organic transistor is of great interest. In this paper, the artificial synapse simulation by ion-gel gated organic field-effect transistors (FETs) with poly(3-hexylthiophene) (P3HT) active channel is demonstrated. Key features of the synaptic behaviors, such as paired-pulse facilitation (PPF), short-term plasticity (STP), self-tuning, the spike logic operation, spatiotemporal dentritic integration, and modulation are successfully mimicked. Furthermore, the interface doping processes of electrolyte ions between the active P3HT layer and ion gels is comprehensively studied for confirming the operating processes underlying the conductivity and excitatory postsynaptic current (EPSC) variations in the organic synaptic devices. This study represents an important step toward building future artificial neuromorphic systems with newly emerged ion gel gated organic synaptic devices.

  1. Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

    Science.gov (United States)

    Schneider, Michael L.; Donnelly, Christine A.; Russek, Stephen E.; Baek, Burm; Pufall, Matthew R.; Hopkins, Peter F.; Dresselhaus, Paul D.; Benz, Samuel P.; Rippard, William H.

    2018-01-01

    Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies. PMID:29387787

  2. Mean field methods for cortical network dynamics

    DEFF Research Database (Denmark)

    Hertz, J.; Lerchner, Alexander; Ahmadi, M.

    2004-01-01

    We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...

  3. Treating the Synapse in Major Psychiatric Disorders: The Role of Postsynaptic Density Network in Dopamine-Glutamate Interplay and Psychopharmacologic Drugs Molecular Actions

    Directory of Open Access Journals (Sweden)

    Carmine Tomasetti

    2017-01-01

    Full Text Available Dopamine-glutamate interplay dysfunctions have been suggested as pathophysiological key determinants of major psychotic disorders, above all schizophrenia and mood disorders. For the most part, synaptic interactions between dopamine and glutamate signaling pathways take part in the postsynaptic density, a specialized ultrastructure localized under the membrane of glutamatergic excitatory synapses. Multiple proteins, with the role of adaptors, regulators, effectors, and scaffolds compose the postsynaptic density network. They form structural and functional crossroads where multiple signals, starting at membrane receptors, are received, elaborated, integrated, and routed to appropriate nuclear targets. Moreover, transductional pathways belonging to different receptors may be functionally interconnected through postsynaptic density molecules. Several studies have demonstrated that psychopharmacologic drugs may differentially affect the expression and function of postsynaptic genes and proteins, depending upon the peculiar receptor profile of each compound. Thus, through postsynaptic network modulation, these drugs may induce dopamine-glutamate synaptic remodeling, which is at the basis of their long-term physiologic effects. In this review, we will discuss the role of postsynaptic proteins in dopamine-glutamate signals integration, as well as the peculiar impact of different psychotropic drugs used in clinical practice on postsynaptic remodeling, thereby trying to point out the possible future molecular targets of “synapse-based” psychiatric therapeutic strategies.

  4. Calcium channel-dependent molecular maturation of photoreceptor synapses.

    Directory of Open Access Journals (Sweden)

    Nawal Zabouri

    Full Text Available Several studies have shown the importance of calcium channels in the development and/or maturation of synapses. The Ca(V1.4(α(1F knockout mouse is a unique model to study the role of calcium channels in photoreceptor synapse formation. It features abnormal ribbon synapses and aberrant cone morphology. We investigated the expression and targeting of several key elements of ribbon synapses and analyzed the cone morphology in the Ca(V1.4(α(1F knockout retina. Our data demonstrate that most abnormalities occur after eye opening. Indeed, scaffolding proteins such as Bassoon and RIM2 are properly targeted at first, but their expression and localization are not maintained in adulthood. This indicates that either calcium or the Ca(V1.4 channel, or both are necessary for the maintenance of their normal expression and distribution in photoreceptors. Other proteins, such as Veli3 and PSD-95, also display abnormal expression in rods prior to eye opening. Conversely, vesicle related proteins appear normal. Our data demonstrate that the Ca(V1.4 channel is important for maintaining scaffolding proteins in the ribbon synapse but less vital for proteins related to vesicular release. This study also confirms that in adult retinae, cones show developmental features such as sprouting and synaptogenesis. Overall we present evidence that in the absence of the Ca(V1.4 channel, photoreceptor synapses remain immature and are unable to stabilize.

  5. Calcium channel-dependent molecular maturation of photoreceptor synapses.

    Science.gov (United States)

    Zabouri, Nawal; Haverkamp, Silke

    2013-01-01

    Several studies have shown the importance of calcium channels in the development and/or maturation of synapses. The Ca(V)1.4(α(1F)) knockout mouse is a unique model to study the role of calcium channels in photoreceptor synapse formation. It features abnormal ribbon synapses and aberrant cone morphology. We investigated the expression and targeting of several key elements of ribbon synapses and analyzed the cone morphology in the Ca(V)1.4(α(1F)) knockout retina. Our data demonstrate that most abnormalities occur after eye opening. Indeed, scaffolding proteins such as Bassoon and RIM2 are properly targeted at first, but their expression and localization are not maintained in adulthood. This indicates that either calcium or the Ca(V)1.4 channel, or both are necessary for the maintenance of their normal expression and distribution in photoreceptors. Other proteins, such as Veli3 and PSD-95, also display abnormal expression in rods prior to eye opening. Conversely, vesicle related proteins appear normal. Our data demonstrate that the Ca(V)1.4 channel is important for maintaining scaffolding proteins in the ribbon synapse but less vital for proteins related to vesicular release. This study also confirms that in adult retinae, cones show developmental features such as sprouting and synaptogenesis. Overall we present evidence that in the absence of the Ca(V)1.4 channel, photoreceptor synapses remain immature and are unable to stabilize.

  6. Bifurcation and category learning in network models of oscillating cortex

    Science.gov (United States)

    Baird, Bill

    1990-06-01

    A genetic model of oscillating cortex, which assumes “minimal” coupling justified by known anatomy, is shown to function as an associative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long-range excitatory connections. Using a local Hebb-like learning rule for primary and higher-order synapses at the ends of the long-range connections, the system learns to store the kinds of oscillation amplitude patterns observed in olfactory and visual cortex. In olfaction, these patterns “emerge” during respiration by a pattern forming phase transition which we characterize in the model as a multiple Hopf bifurcation. We argue that these bifurcations play an important role in the operation of real digital computers and neural networks, and we use bifurcation theory to derive learning rules which analytically guarantee CAM storage of continuous periodic sequences-capacity: N/2 Fourier components for an N-node network-no “spurious” attractors.

  7. Biophysical synaptic dynamics in an analog VLSI network of Hodgkin-Huxley neurons.

    Science.gov (United States)

    Yu, Theodore; Cauwenberghs, Gert

    2009-01-01

    We study synaptic dynamics in a biophysical network of four coupled spiking neurons implemented in an analog VLSI silicon microchip. The four neurons implement a generalized Hodgkin-Huxley model with individually configurable rate-based kinetics of opening and closing of Na+ and K+ ion channels. The twelve synapses implement a rate-based first-order kinetic model of neurotransmitter and receptor dynamics, accounting for NMDA and non-NMDA type chemical synapses. The implemented models on the chip are fully configurable by 384 parameters accounting for conductances, reversal potentials, and pre/post-synaptic voltage-dependence of the channel kinetics. We describe the models and present experimental results from the chip characterizing single neuron dynamics, single synapse dynamics, and multi-neuron network dynamics showing phase-locking behavior as a function of synaptic coupling strength. The 3mm x 3mm microchip consumes 1.29 mW power making it promising for applications including neuromorphic modeling and neural prostheses.

  8. Prevention of Noise Damage to Cochlear Synapses

    Science.gov (United States)

    2017-10-01

    Assessment of synapse regeneration : Twelve week old CBA/CaJ mice are exposed to a moderate noise that destroys synapses on inner hair cells (IHCs) but spares...result of excitotoxic trauma to cochlear synapses due to glutamate released from the hair cells . Excitotoxic trauma damages the postsynaptic cell by...components ............................................. 12 d) Quantitative analysis of effects of neurotrophic factors on synapse regeneration in vitro

  9. SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

    Science.gov (United States)

    Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

    2018-01-01

    Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Memory in Neural Networks and Glasses

    NARCIS (Netherlands)

    Heerema, M.

    2000-01-01

    The thesis tries and models a neural network in a way which, at essential points, is biologically realistic. In a biological context, the changes of the synapses of the neural network are most often described by what is called `Hebb's learning rule'. On careful analysis it is, in fact, nothing but a

  11. A Pruning Neural Network Model in Credit Classification Analysis

    Directory of Open Access Journals (Sweden)

    Yajiao Tang

    2018-01-01

    Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

  12. The balancing act of GABAergic synapse organizers.

    Science.gov (United States)

    Ko, Jaewon; Choii, Gayoung; Um, Ji Won

    2015-04-01

    GABA (γ-aminobutyric acid) is the main neurotransmitter at inhibitory synapses in the mammalian brain. It is essential for maintaining the excitation and inhibition (E/I) ratio, whose imbalance underlies various brain diseases. Emerging information about inhibitory synapse organizers provides a novel molecular framework for understanding E/I balance at the synapse, circuit, and systems levels. This review highlights recent advances in deciphering these components of the inhibitory synapse and their roles in the development, transmission, and circuit properties of inhibitory synapses. We also discuss how their dysfunction may lead to a variety of brain disorders, suggesting new therapeutic strategies based on balancing the E/I ratio.

  13. Hybrid discrete-time neural networks.

    Science.gov (United States)

    Cao, Hongjun; Ibarz, Borja

    2010-11-13

    Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

  14. Power-law forgetting in synapses with metaplasticity

    International Nuclear Information System (INIS)

    Mehta, A; Luck, J M

    2011-01-01

    The idea of using metaplastic synapses to incorporate the separate storage of long- and short-term memories via an array of hidden states was put forward in the cascade model of Fusi et al. In this paper, we devise and investigate two models of a metaplastic synapse based on these general principles. The main difference between the two models lies in their available mechanisms of decay, when a contrarian event occurs after the build-up of a long-term memory. In one case, this leads to the conversion of the long-term memory to a short-term memory of the opposite kind, while in the other, a long-term memory of the opposite kind may be generated as a result. Appropriately enough, the response of both models to short-term events is not affected by this difference in architecture. On the contrary, the transient response of both models, after long-term memories have been created by the passage of sustained signals, is rather different. The asymptotic behaviour of both models is, however, characterised by power-law forgetting with the same universal exponent

  15. Stabilization of memory States by stochastic facilitating synapses.

    Science.gov (United States)

    Miller, Paul

    2013-12-06

    Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.

  16. A new measure for the strength of electrical synapses

    Directory of Open Access Journals (Sweden)

    Julie S Haas

    2015-09-01

    Full Text Available Electrical synapses, like chemical synapses, mediate intraneuronal communication. Electrical synapses are typically quantified by subthreshold measurements of coupling, which fall short in describing their impact on spiking activity in coupled neighbors. Here we describe a novel measurement for electrical synapse strength that directly evaluates the effect of synaptically transmitted activity on spike timing. This method, also applicable to neurotransmitter-based synapses, communicates the considerable strength of electrical synapses. For electrical synapses measured in rodent slices of the thalamic reticular nucleus, spike timing is modulated by tens of ms by activity in a coupled neighbor.

  17. Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances

    International Nuclear Information System (INIS)

    Fiete, Ila R.; Seung, H. Sebastian

    2006-01-01

    We present a method of estimating the gradient of an objective function with respect to the synaptic weights of a spiking neural network. The method works by measuring the fluctuations in the objective function in response to dynamic perturbation of the membrane conductances of the neurons. It is compatible with recurrent networks of conductance-based model neurons with dynamic synapses. The method can be interpreted as a biologically plausible synaptic learning rule, if the dynamic perturbations are generated by a special class of 'empiric' synapses driven by random spike trains from an external source

  18. Organizers of inhibitory synapses come of age.

    Science.gov (United States)

    Krueger-Burg, Dilja; Papadopoulos, Theofilos; Brose, Nils

    2017-08-01

    While the postsynaptic density of excitatory synapses is known to encompass a highly complex molecular machinery, the equivalent organizational structure of inhibitory synapses has long remained largely undefined. In recent years, however, substantial progress has been made towards identifying the full complement of organizational proteins present at inhibitory synapses, including submembranous scaffolds, intracellular signaling proteins, transsynaptic adhesion proteins, and secreted factors. Here, we summarize these findings and discuss future challenges in assigning synapse-specific functions to the newly discovered catalog of proteins, an endeavor that will depend heavily on newly developed technologies such as proximity biotinylation. Further advances are made all the more essential by growing evidence that links inhibitory synapses to psychiatric and neurological disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. The role of neurexins and neuroligins in the formation, maturation, and function of vertebrate synapses.

    Science.gov (United States)

    Krueger, Dilja D; Tuffy, Liam P; Papadopoulos, Theofilos; Brose, Nils

    2012-06-01

    Neurexins (NXs) and neuroligins (NLs) are transsynaptically interacting cell adhesion proteins that play a key role in the formation, maturation, activity-dependent validation, and maintenance of synapses. As complex alternative splicing processes in nerve cells generate a large number of NX and NLs variants, it has been proposed that a combinatorial interaction code generated by these variants may determine synapse identity and network connectivity during brain development. The functional importance of NXs and NLs is exemplified by the fact that mutations in NX and NL genes are associated with several neuropsychiatric disorders, most notably with autism. Accordingly, major research efforts have focused on the molecular mechanisms by which NXs and NLs operate at synapses. In this review, we summarize recent progress in this field and discuss emerging topics, such as the role of alternative interaction partners of NXs and NLs in synapse formation and function, and their relevance for synaptic plasticity in the mature brain. The novel findings highlight the fundamental importance of NX-NL interactions in a wide range of synaptic functions. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

    Science.gov (United States)

    Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

    2016-10-01

    Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

  1. Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis

    Science.gov (United States)

    Anton-Sanchez, Laura; Bielza, Concha; Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Larrañaga, Pedro

    2014-01-01

    The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500μm3 and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers. PMID:25206325

  2. The interplay between neurons and glia in synapse development and plasticity.

    Science.gov (United States)

    Stogsdill, Jeff A; Eroglu, Cagla

    2017-02-01

    In the brain, the formation of complex neuronal networks amenable to experience-dependent remodeling is complicated by the diversity of neurons and synapse types. The establishment of a functional brain depends not only on neurons, but also non-neuronal glial cells. Glia are in continuous bi-directional communication with neurons to direct the formation and refinement of synaptic connectivity. This article reviews important findings, which uncovered cellular and molecular aspects of the neuron-glia cross-talk that govern the formation and remodeling of synapses and circuits. In vivo evidence demonstrating the critical interplay between neurons and glia will be the major focus. Additional attention will be given to how aberrant communication between neurons and glia may contribute to neural pathologies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Neuroglial plasticity at striatal glutamatergic synapses in Parkinson's disease

    Directory of Open Access Journals (Sweden)

    Rosa M Villalba

    2011-08-01

    Full Text Available Striatal dopamine denervation is the pathological hallmark of Parkinson’s disease (PD. Another major pathological change described in animal models and PD patients is a significant reduction in the density of dendritic spines on medium spiny striatal projection neurons. Simultaneously, the ultrastructural features of the neuronal synaptic elements at the remaining corticostriatal and thalamostriatal glutamatergic axo-spinous synapses undergo complex ultrastructural remodeling consistent with increased synaptic activity (Villalba et al., 2011. The concept of tripartite synapses (TS was introduced a decade ago, according to which astrocytes process and exchange information with neuronal synaptic elements at glutamatergic synapses (Araque et al., 1999a. Although there has been compelling evidence that astrocytes are integral functional elements of tripartite glutamatergic synaptic complexes in the cerebral cortex and hippocampus, their exact functional role, degree of plasticity and preponderance in other CNS regions remain poorly understood. In this review, we discuss our recent findings showing that neuronal elements at cortical and thalamic glutamatergic synapses undergo significant plastic changes in the striatum of MPTP-treated parkinsonian monkeys. We also present new ultrastructural data that demonstrate a significant expansion of the astrocytic coverage of striatal TS synapses in the parkinsonian state, providing further evidence for ultrastructural compensatory changes that affect both neuronal and glial elements at TS. Together with our limited understanding of the mechanisms by which astrocytes respond to changes in neuronal activity and extracellular transmitter homeostasis, the role of both neuronal and glial components of excitatory synapses must be considered, if one hopes to take advantage of glia-neuronal communication knowledge to better understand the pathophysiology of striatal processing in parkinsonism, and develop new PD

  4. Bi-directional astrocytic regulation of neuronal activity within a network

    Directory of Open Access Journals (Sweden)

    Susan Yu Gordleeva

    2012-11-01

    Full Text Available The concept of a tripartite synapse holds that astrocytes can affect both the pre- and postsynaptic compartments through the Ca2+-dependent release of gliotransmitters. Because astrocytic Ca2+ transients usually last for a few seconds, we assumed that astrocytic regulation of synaptic transmission may also occur on the scale of seconds. Here, we considered the basic physiological functions of tripartite synapses and investigated astrocytic regulation at the level of neural network activity. The firing dynamics of individual neurons in a spontaneous firing network was described by the Hodgkin-Huxley model. The neurons received excitatory synaptic input driven by the Poisson spike train with variable frequency. The mean field concentration of the released neurotransmitter was used to describe the presynaptic dynamics. The amplitudes of the excitatory postsynaptic currents (PSCs obeyed the gamma distribution law. In our model, astrocytes depressed the presynaptic release and enhanced the postsynaptic currents. As a result, low frequency synaptic input was suppressed while high frequency input was amplified. The analysis of the neuron spiking frequency as an indicator of network activity revealed that tripartite synaptic transmission dramatically changed the local network operation compared to bipartite synapses. Specifically, the astrocytes supported homeostatic regulation of the network activity by increasing or decreasing firing of the neurons. Thus, the astrocyte activation may modulate a transition of neural network into bistable regime of activity with two stable firing levels and spontaneous transitions between them.

  5. Ca(2+) influx and neurotransmitter release at ribbon synapses.

    Science.gov (United States)

    Cho, Soyoun; von Gersdorff, Henrique

    2012-01-01

    Ca(2+) influx through voltage-gated Ca(2+) channels triggers the release of neurotransmitters at presynaptic terminals. Some sensory receptor cells in the peripheral auditory and visual systems have specialized synapses that express an electron-dense organelle called a synaptic ribbon. Like conventional synapses, ribbon synapses exhibit SNARE-mediated exocytosis, clathrin-mediated endocytosis, and short-term plasticity. However, unlike non-ribbon synapses, voltage-gated L-type Ca(2+) channel opening at ribbon synapses triggers a form of multiquantal release that can be highly synchronous. Furthermore, ribbon synapses appear to be specialized for fast and high throughput exocytosis controlled by graded membrane potential changes. Here we will discuss some of the basic aspects of synaptic transmission at different types of ribbon synapses, and we will emphasize recent evidence that auditory and retinal ribbon synapses have marked differences. This will lead us to suggest that ribbon synapses are specialized for particular operating ranges and frequencies of stimulation. We propose that different types of ribbon synapses transfer diverse rates of sensory information by expressing a particular repertoire of critical components, and by placing them at precise and strategic locations, so that a continuous supply of primed vesicles and Ca(2+) influx leads to fast, accurate, and ongoing exocytosis. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Modulation, plasticity and pathophysiology of the parallel fiber-Purkinje cell synapse

    Directory of Open Access Journals (Sweden)

    Eriola Hoxha

    2016-11-01

    Full Text Available The parallel fiber-Purkinje cell synapse represents the point of maximal signal divergence in the cerebellar cortex with an estimated number of about 60 billion synaptic contacts in the rat and 100,000 billions in humans. At the same time, the Purkinje cell dendritic tree is a site of remarkable convergence of more than 100,000 parallel fiber synapses. Parallel fibers activity generates fast postsynaptic currents via AMPA receptors, and slower signals, mediated by mGlu1 receptors, resulting in Purkinje cell depolarization accompanied by sharp calcium elevation within dendritic regions. Long-term depression and long-term potentiation have been widely described for the parallel fiber-Purkinje cell synapse and have been proposed as mechanisms for motor learning. The mechanisms of induction for LTP and LTD involve different signaling mechanisms within the presynaptic terminal and/or at the postsynaptic site, promoting enduring modification in the neurotransmitter release and change in responsiveness to the neurotransmitter. The parallel fiber-Purkinje cell synapse is finely modulated by several neurotransmitters, including serotonin, noradrenaline, and acetylcholine. The ability of these neuromodulators to gate LTP and LTD at the parallel fiber-Purkinje cell synapse could, at least in part, explain their effect on cerebellar-dependent learning and memory paradigms. Overall, these findings have important implications for understanding the cerebellar involvement in a series of pathological conditions, ranging from ataxia to autism. For example, parallel fiber-Purkinje cell synapse dysfunctions have been identified in several murine models of spinocerebellar ataxia (SCA types 1, 3, 5 and 27. In some cases, the defect is specific for the AMPA receptor signaling (SCA27, while in others the mGlu1 pathway is affected (SCA1, 3, 5. Interestingly, the parallel fiber-Purkinje cell synapse has been shown to be hyper-functional in a mutant mouse model of autism

  7. miRNA-431 Prevents Amyloid-β-Induced Synapse Loss in Neuronal Cell Culture Model of Alzheimer's Disease by Silencing Kremen1.

    Science.gov (United States)

    Ross, Sean P; Baker, Kelly E; Fisher, Amanda; Hoff, Lee; Pak, Elena S; Murashov, Alexander K

    2018-01-01

    Synapse loss is well regarded as the underlying cause for the progressive decline of memory function over the course of Alzheimer's disease (AD) development. Recent observations suggest that the accumulation of the Wnt antagonist Dickkopf-1 (Dkk1) in the AD brain plays a critical role in triggering synaptic degeneration. Mechanistically, Dkk1 cooperates with Kremen1 (Krm1), its transmembrane receptor, to block the Wnt/β-catenin signaling pathway. Here, we show that silencing Krm1 with miR-431 prevents amyloid-β-mediated synapse loss in cortico-hippocampal cultures isolated from triple transgenic 3xTg-AD mice. Exposure to AβDDL (an amyloid-β derived diffusive ligand) or Dkk1 reduced the number of pre- and post-synaptic puncta in primary neuronal cultures, while treatment with miR-431 prevented synapse loss. In addition, treatment with miR-431 also prevented neurite degeneration. Our findings demonstrate that miR-431 protects synapses and neurites from Aβ-toxicity in an AD cell culture model and may be a promising therapeutic target.

  8. Neuronal synchrony detection on single-electron neural networks

    International Nuclear Information System (INIS)

    Oya, Takahide; Asai, Tetsuya; Kagaya, Ryo; Hirose, Tetsuya; Amemiya, Yoshihito

    2006-01-01

    Synchrony detection between burst and non-burst spikes is known to be one functional example of depressing synapses. Kanazawa et al. demonstrated synchrony detection with MOS depressing synapse circuits. They found that the performance of a network with depressing synapses that discriminates between burst and random input spikes increases non-monotonically as the static device mismatch is increased. We designed a single-electron depressing synapse and constructed the same network as in Kanazawa's study to develop noise-tolerant single-electron circuits. We examined the temperature characteristics and explored possible architecture that enables single-electron circuits to operate at T > 0 K

  9. Molecular switches at the synapse emerge from receptor and kinase traffic.

    Directory of Open Access Journals (Sweden)

    2005-07-01

    Full Text Available Changes in the synaptic connection strengths between neurons are believed to play a role in memory formation. An important mechanism for changing synaptic strength is through movement of neurotransmitter receptors and regulatory proteins to and from the synapse. Several activity-triggered biochemical events control these movements. Here we use computer models to explore how these putative memory-related changes can be stabilised long after the initial trigger, and beyond the lifetime of synaptic molecules. We base our models on published biochemical data and experiments on the activity-dependent movement of a glutamate receptor, AMPAR, and a calcium-dependent kinase, CaMKII. We find that both of these molecules participate in distinct bistable switches. These simulated switches are effective for long periods despite molecular turnover and biochemical fluctuations arising from the small numbers of molecules in the synapse. The AMPAR switch arises from a novel self-recruitment process where the presence of sufficient receptors biases the receptor movement cycle to insert still more receptors into the synapse. The CaMKII switch arises from autophosphorylation of the kinase. The switches may function in a tightly coupled manner, or relatively independently. The latter case leads to multiple stable states of the synapse. We propose that similar self-recruitment cycles may be important for maintaining levels of many molecules that undergo regulated movement, and that these may lead to combinatorial possible stable states of systems like the synapse.

  10. Astrocyte Transforming Growth Factor Beta 1 Protects Synapses against Aβ Oligomers in Alzheimer's Disease Model.

    Science.gov (United States)

    Diniz, Luan Pereira; Tortelli, Vanessa; Matias, Isadora; Morgado, Juliana; Bérgamo Araujo, Ana Paula; Melo, Helen M; Seixas da Silva, Gisele S; Alves-Leon, Soniza V; de Souza, Jorge M; Ferreira, Sergio T; De Felice, Fernanda G; Gomes, Flávia Carvalho Alcantara

    2017-07-12

    Alzheimer's disease (AD) is characterized by progressive cognitive decline, increasingly attributed to neuronal dysfunction induced by amyloid-β oligomers (AβOs). Although the impact of AβOs on neurons has been extensively studied, only recently have the possible effects of AβOs on astrocytes begun to be investigated. Given the key roles of astrocytes in synapse formation, plasticity, and function, we sought to investigate the impact of AβOs on astrocytes, and to determine whether this impact is related to the deleterious actions of AβOs on synapses. We found that AβOs interact with astrocytes, cause astrocyte activation and trigger abnormal generation of reactive oxygen species, which is accompanied by impairment of astrocyte neuroprotective potential in vitro We further show that both murine and human astrocyte conditioned media (CM) increase synapse density, reduce AβOs binding, and prevent AβO-induced synapse loss in cultured hippocampal neurons. Both a neutralizing anti-transforming growth factor-β1 (TGF-β1) antibody and siRNA-mediated knockdown of TGF-β1, previously identified as an important synaptogenic factor secreted by astrocytes, abrogated the protective action of astrocyte CM against AβO-induced synapse loss. Notably, TGF-β1 prevented hippocampal dendritic spine loss and memory impairment in mice that received an intracerebroventricular infusion of AβOs. Results suggest that astrocyte-derived TGF-β1 is part of an endogenous mechanism that protects synapses against AβOs. By demonstrating that AβOs decrease astrocyte ability to protect synapses, our results unravel a new mechanism underlying the synaptotoxic action of AβOs in AD. SIGNIFICANCE STATEMENT Alzheimer's disease is characterized by progressive cognitive decline, mainly attributed to synaptotoxicity of the amyloid-β oligomers (AβOs). Here, we investigated the impact of AβOs in astrocytes, a less known subject. We show that astrocytes prevent synapse loss induced by A

  11. ProBDNF and mature BDNF as punishment and reward signals for synapse elimination at mouse neuromuscular junctions.

    Science.gov (United States)

    Je, H Shawn; Yang, Feng; Ji, Yuanyuan; Potluri, Srilatha; Fu, Xiu-Qing; Luo, Zhen-Ge; Nagappan, Guhan; Chan, Jia Pei; Hempstead, Barbara; Son, Young-Jin; Lu, Bai

    2013-06-12

    During development, mammalian neuromuscular junctions (NMJs) transit from multiple-innervation to single-innervation through axonal competition via unknown molecular mechanisms. Previously, using an in vitro model system, we demonstrated that the postsynaptic secretion of pro-brain-derived neurotrophic factor (proBDNF) stabilizes or eliminates presynaptic axon terminals, depending on its proteolytic conversion at synapses. Here, using developing mouse NMJs, we obtained in vivo evidence that proBDNF and mature BDNF (mBDNF) play roles in synapse elimination. We observed that exogenous proBDNF promoted synapse elimination, whereas mBDNF infusion substantially delayed synapse elimination. In addition, pharmacological inhibition of the proteolytic conversion of proBDNF to mBDNF accelerated synapse elimination via activation of p75 neurotrophin receptor (p75(NTR)). Furthermore, the inhibition of both p75(NTR) and sortilin signaling attenuated synapse elimination. We propose a model in which proBDNF and mBDNF serve as potential "punishment" and "reward" signals for inactive and active terminals, respectively, in vivo.

  12. Synapse geometry and receptor dynamics modulate synaptic strength.

    Directory of Open Access Journals (Sweden)

    Dominik Freche

    Full Text Available Synaptic transmission relies on several processes, such as the location of a released vesicle, the number and type of receptors, trafficking between the postsynaptic density (PSD and extrasynaptic compartment, as well as the synapse organization. To study the impact of these parameters on excitatory synaptic transmission, we present a computational model for the fast AMPA-receptor mediated synaptic current. We show that in addition to the vesicular release probability, due to variations in their release locations and the AMPAR distribution, the postsynaptic current amplitude has a large variance, making a synapse an intrinsic unreliable device. We use our model to examine our experimental data recorded from CA1 mice hippocampal slices to study the differences between mEPSC and evoked EPSC variance. The synaptic current but not the coefficient of variation is maximal when the active zone where vesicles are released is apposed to the PSD. Moreover, we find that for certain type of synapses, receptor trafficking can affect the magnitude of synaptic depression. Finally, we demonstrate that perisynaptic microdomains located outside the PSD impacts synaptic transmission by regulating the number of desensitized receptors and their trafficking to the PSD. We conclude that geometrical modifications, reorganization of the PSD or perisynaptic microdomains modulate synaptic strength, as the mechanisms underlying long-term plasticity.

  13. GABAergic synapse properties may explain genetic variation in hippocampal network oscillations in mice

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    Tim S Heistek

    2010-06-01

    Full Text Available Cognitive ability and the properties of brain oscillation are highly heritable in humans. Genetic variation underlying oscillatory activity might give rise to differences in cognition and behavior. How genetic diversity translates into altered properties of oscillations and synchronization of neuronal activity is unknown. To address this issue, we investigated cellular and synaptic mechanisms of hippocampal fast network oscillations in eight genetically distinct inbred mouse strains. The frequency of carbachol-induced oscillations differed substantially between mouse strains. Since GABAergic inhibition sets oscillation frequency, we studied the properties of inhibitory synaptic inputs (IPSCs received by CA3 and CA1 pyramidal cells of three mouse strains that showed the highest, lowest and intermediate frequencies of oscillations. In CA3 pyramidal cells, the frequency of rhythmic IPSC input showed the same strain differences as the frequency of field oscillations. Furthermore, IPSC decay times in both CA1 and CA3 pyramidal cells were faster in mouse strains with higher oscillation frequencies than in mouse strains with lower oscillation frequency, suggesting that differences in GABAA-receptor subunit composition exist between these strains. Indeed, gene expression of GABAA-receptor β2 (Gabrb2 and β3 (Gabrb2 subunits was higher in mouse strains with faster decay kinetics compared with mouse strains with slower decay kinetics. Hippocampal pyramidal neurons in mouse strains with higher oscillation frequencies and faster decay kinetics fired action potential at higher frequencies. These data indicate that differences in genetic background may result in different GABAA-receptor subunit expression, which affects the rhythm of pyramidal neuron firing and fast network activity through GABA synapse kinetics.

  14. Organization of central synapses by adhesion molecules.

    Science.gov (United States)

    Tallafuss, Alexandra; Constable, John R L; Washbourne, Philip

    2010-07-01

    Synapses are the primary means for transmitting information from one neuron to the next. They are formed during the development of the nervous system, and the formation of appropriate synapses is crucial for the establishment of neuronal circuits that underlie behavior and cognition. Understanding how synapses form and are maintained will allow us to address developmental disorders such as autism, mental retardation and possibly also psychological disorders. A number of biochemical and proteomic studies have revealed a diverse and vast assortment of molecules that are present at the synapse. It is now important to untangle this large array of proteins and determine how it assembles into a functioning unit. Here we focus on recent reports describing how synaptic cell adhesion molecules interact with and organize the presynaptic and postsynaptic specializations of both excitatory and inhibitory central synapses. © The Authors (2010). Journal Compilation © Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  15. Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

    Science.gov (United States)

    Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

    2015-01-01

    Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. PMID:26657024

  16. Computing the Local Field Potential (LFP from Integrate-and-Fire Network Models.

    Directory of Open Access Journals (Sweden)

    Alberto Mazzoni

    2015-12-01

    Full Text Available Leaky integrate-and-fire (LIF network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP. Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

  17. Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations.

    Science.gov (United States)

    Hu, Eric Y; Bouteiller, Jean-Marie C; Song, Dong; Baudry, Michel; Berger, Theodore W

    2015-01-01

    Chemical synapses are comprised of a wide collection of intricate signaling pathways involving complex dynamics. These mechanisms are often reduced to simple spikes or exponential representations in order to enable computer simulations at higher spatial levels of complexity. However, these representations cannot capture important nonlinear dynamics found in synaptic transmission. Here, we propose an input-output (IO) synapse model capable of generating complex nonlinear dynamics while maintaining low computational complexity. This IO synapse model is an extension of a detailed mechanistic glutamatergic synapse model capable of capturing the input-output relationships of the mechanistic model using the Volterra functional power series. We demonstrate that the IO synapse model is able to successfully track the nonlinear dynamics of the synapse up to the third order with high accuracy. We also evaluate the accuracy of the IO synapse model at different input frequencies and compared its performance with that of kinetic models in compartmental neuron models. Our results demonstrate that the IO synapse model is capable of efficiently replicating complex nonlinear dynamics that were represented in the original mechanistic model and provide a method to replicate complex and diverse synaptic transmission within neuron network simulations.

  18. Organization of central synapses by adhesion molecules

    OpenAIRE

    Tallafuss, Alexandra; Constable, John R.L.; Washbourne, Philip

    2010-01-01

    Synapses are the primary means for transmitting information from one neuron to the next. They are formed during development of the nervous system, and formation of appropriate synapses is crucial for establishment of neuronal circuits that underlie behavior and cognition. Understanding how synapses form and are maintained will allow us to address developmental disorders such as autism, mental retardation and possibly also psychological disorders. A number of biochemical and proteomic studies ...

  19. Role of perisynaptic parameters in neurotransmitter homeostasis - computational study of a general synapse

    Science.gov (United States)

    Pendyam, Sandeep; Mohan, Ashwin; Kalivas, Peter W.; Nair, Satish S.

    2015-01-01

    Extracellular neurotransmitter concentrations vary over a wide range depending on the type of neurotransmitter and location in the brain. Neurotransmitter homeostasis near a synapse is achieved by a balance of several mechanisms including vesicular release from the presynapse, diffusion, uptake by transporters, non-synaptic production, and regulation of release by autoreceptors. These mechanisms are also affected by the glia surrounding the synapse. However, the role of these mechanisms in achieving neurotransmitter homeostasis is not well understood. A biophysical modeling framework was proposed to reverse engineer glial configurations and parameters related to homeostasis for synapses that support a range of neurotransmitter gradients. Model experiments reveal that synapses with extracellular neurotransmitter concentrations in the micromolar range require non-synaptic neurotransmitter sources and tight synaptic isolation by extracellular glial formations. The model was used to identify the role of perisynaptic parameters on neurotransmitter homeostasis, and to propose glial configurations that could support different levels of extracellular neurotransmitter concentrations. Ranking the parameters based on their effect on neurotransmitter homeostasis, non-synaptic sources were found to be the most important followed by transporter concentration and diffusion coefficient. PMID:22460547

  20. Learning and forgetting on asymmetric, diluted neural networks

    International Nuclear Information System (INIS)

    Derrida, B.; Nadal, J.P.

    1987-01-01

    It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks

  1. The organization of plasticity in the cerebellar cortex: from synapses to control.

    Science.gov (United States)

    D'Angelo, Egidio

    2014-01-01

    The cerebellum is thought to play a critical role in procedural learning, but the relationship between this function and the underlying cellular and synaptic mechanisms remains largely speculative. At present, at least nine forms of long-term synaptic and nonsynaptic plasticity (some of which are bidirectional) have been reported in the cerebellar cortex and deep cerebellar nuclei. These include long-term potentiation (LTP) and long-term depression at the mossy fiber-granule cell synapse, at the synapses formed by parallel fibers, climbing fibers, and molecular layer interneurons on Purkinje cells, and at the synapses formed by mossy fibers and Purkinje cells on deep cerebellar nuclear cells, as well as LTP of intrinsic excitability in granule cells, Purkinje cells, and deep cerebellar nuclear cells. It is suggested that the complex properties of cerebellar learning would emerge from the distribution of plasticity in the network and from its dynamic remodeling during the different phases of learning. Intrinsic and extrinsic factors may hold the key to explain how the different forms of plasticity cooperate to select specific transmission channels and to regulate the signal-to-noise ratio through the cerebellar cortex. These factors include regulation of neuronal excitation by local inhibitory networks, engagement of specific molecular mechanisms by spike bursts and theta-frequency oscillations, and gating by external neuromodulators. Therefore, a new and more complex view of cerebellar plasticity is emerging with respect to that predicted by the original "Motor Learning Theory," opening issues that will require experimental and computational testing. © 2014 Elsevier B.V. All rights reserved.

  2. High-conductance states in a mean-field cortical network model

    CERN Document Server

    Lerchner, A; Hertz, J

    2004-01-01

    Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1 due to the tendency of spikes being clustered into bursts. We show that this behavior emerges naturally in a balanced cortical network model with random connectivity and conductance-based synapses. We employ mean field theory with correctly colored noise to describe temporal correlations in the neuronal activity. Our results illuminate the connection between two independent experimental findings: high conductance states of cortical neurons in their natural environment, and variable non-Poissonian spike statistics with Fano factors greater than 1.

  3. Stochastic resonance in feedforward acupuncture networks

    Science.gov (United States)

    Qin, Ying-Mei; Wang, Jiang; Men, Cong; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chan, Wai-Lok

    2014-10-01

    Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks (FFN) based on FitzHugh-Nagumo neuron model. It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure. Resonant properties in the noisy network can also be altered by several network parameters, such as heterogeneity, synapse features, and feedback connections. These results may also provide a novel potential explanation for the propagation of acupuncture signal.

  4. A learning algorithm for oscillatory cellular neural networks.

    Science.gov (United States)

    Ho, C Y.; Kurokawa, H

    1999-07-01

    We present a cellular type oscillatory neural network for temporal segregation of stationary input patterns. The model comprises an array of locally connected neural oscillators with connections limited to a 4-connected neighborhood. The architecture is reminiscent of the well-known cellular neural network that consists of local connection for feature extraction. By means of a novel learning rule and an initialization scheme, global synchronization can be accomplished without incurring any erroneous synchrony among uncorrelated objects. Each oscillator comprises two mutually coupled neurons, and neurons share a piecewise-linear activation function characteristic. The dynamics of traditional oscillatory models is simplified by using only one plastic synapse, and the overall complexity for hardware implementation is reduced. Based on the connectedness of image segments, it is shown that global synchronization and desynchronization can be achieved by means of locally connected synapses, and this opens up a tremendous application potential for the proposed architecture. Furthermore, by using special grouping synapses it is demonstrated that temporal segregation of overlapping gray-level and color segments can also be achieved. Finally, simulation results show that the learning rule proposed circumvents the problem of component mismatches, and hence facilitates a large-scale integration.

  5. Synaptic heterogeneity and stimulus-induced modulation of depression in central synapses.

    Science.gov (United States)

    Hunter, J D; Milton, J G

    2001-08-01

    Short-term plasticity is a pervasive feature of synapses. Synapses exhibit many forms of plasticity operating over a range of time scales. We develop an optimization method that allows rapid characterization of synapses with multiple time scales of facilitation and depression. Investigation of paired neurons that are postsynaptic to the same identified interneuron in the buccal ganglion of Aplysia reveals that the responses of the two neurons differ in the magnitude of synaptic depression. Also, for single neurons, prolonged stimulation of the presynaptic neuron causes stimulus-induced increases in the early phase of synaptic depression. These observations can be described by a model that incorporates two availability factors, e.g., depletable vesicle pools or desensitizing receptor populations, with different time courses of recovery, and a single facilitation component. This model accurately predicts the responses to novel stimuli. The source of synaptic heterogeneity is identified with variations in the relative sizes of the two availability factors, and the stimulus-induced decrement in the early synaptic response is explained by a slowing of the recovery rate of one of the availability factors. The synaptic heterogeneity and stimulus-induced modifications in synaptic depression observed here emphasize that synaptic efficacy depends on both the individual properties of synapses and their past history.

  6. Presynaptic learning and memory with a persistent firing neuron and a habituating synapse: a model of short term persistent habituation.

    Science.gov (United States)

    Ramanathan, Kiruthika; Ning, Ning; Dhanasekar, Dhiviya; Li, Guoqi; Shi, Luping; Vadakkepat, Prahlad

    2012-08-01

    Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show that, depending on the amount of synaptic resources, persistent firing either results in continued habituation or maintains the response, both leading to longer recovery times. The effectiveness of the model as an element in a bio-inspired memory system is discussed.

  7. Pyk2 modulates hippocampal excitatory synapses and contributes to cognitive deficits in a Huntington’s disease model

    KAUST Repository

    Giralt, Albert; Brito, Veronica; Chevy, Quentin; Simonnet, Clé mence; Otsu, Yo; Cifuentes-Dí az, Carmen; Pins, Benoit de; Coura, Renata; Alberch, Jordi; Giné s, Sí lvia; Poncer, Jean-Christophe; Girault, Jean-Antoine

    2017-01-01

    The structure and function of spines and excitatory synapses are under the dynamic control of multiple signalling networks. Although tyrosine phosphorylation is involved, its regulation and importance are not well understood. Here we study the role of Pyk2, a non-receptor calcium-dependent protein-tyrosine kinase highly expressed in the hippocampus. Hippocampal-related learning and CA1 long-term potentiation are severely impaired in Pyk2-deficient mice and are associated with alterations in NMDA receptors, PSD-95 and dendritic spines. In cultured hippocampal neurons, Pyk2 has autophosphorylation-dependent and -independent roles in determining PSD-95 enrichment and spines density. Pyk2 levels are decreased in the hippocampus of individuals with Huntington and in the R6/1 mouse model of the disease. Normalizing Pyk2 levels in the hippocampus of R6/1 mice rescues memory deficits, spines pathology and PSD-95 localization. Our results reveal a role for Pyk2 in spine structure and synaptic function, and suggest that its deficit contributes to Huntington’s disease cognitive impairments.

  8. Pyk2 modulates hippocampal excitatory synapses and contributes to cognitive deficits in a Huntington’s disease model

    KAUST Repository

    Giralt, Albert

    2017-05-30

    The structure and function of spines and excitatory synapses are under the dynamic control of multiple signalling networks. Although tyrosine phosphorylation is involved, its regulation and importance are not well understood. Here we study the role of Pyk2, a non-receptor calcium-dependent protein-tyrosine kinase highly expressed in the hippocampus. Hippocampal-related learning and CA1 long-term potentiation are severely impaired in Pyk2-deficient mice and are associated with alterations in NMDA receptors, PSD-95 and dendritic spines. In cultured hippocampal neurons, Pyk2 has autophosphorylation-dependent and -independent roles in determining PSD-95 enrichment and spines density. Pyk2 levels are decreased in the hippocampus of individuals with Huntington and in the R6/1 mouse model of the disease. Normalizing Pyk2 levels in the hippocampus of R6/1 mice rescues memory deficits, spines pathology and PSD-95 localization. Our results reveal a role for Pyk2 in spine structure and synaptic function, and suggest that its deficit contributes to Huntington’s disease cognitive impairments.

  9. Investigation of synapse formation and function in a glutamatergic-GABAergic two-neuron microcircuit.

    Science.gov (United States)

    Chang, Chia-Ling; Trimbuch, Thorsten; Chao, Hsiao-Tuan; Jordan, Julia-Christine; Herman, Melissa A; Rosenmund, Christian

    2014-01-15

    Neural circuits are composed of mainly glutamatergic and GABAergic neurons, which communicate through synaptic connections. Many factors instruct the formation and function of these synapses; however, it is difficult to dissect the contribution of intrinsic cell programs from that of extrinsic environmental effects in an intact network. Here, we perform paired recordings from two-neuron microculture preparations of mouse hippocampal glutamatergic and GABAergic neurons to investigate how synaptic input and output of these two principal cells develop. In our reduced preparation, we found that glutamatergic neurons showed no change in synaptic output or input regardless of partner neuron cell type or neuronal activity level. In contrast, we found that glutamatergic input caused the GABAergic neuron to modify its output by way of an increase in synapse formation and a decrease in synaptic release efficiency. These findings are consistent with aspects of GABAergic synapse maturation observed in many brain regions. In addition, changes in GABAergic output are cell wide and not target-cell specific. We also found that glutamatergic neuronal activity determined the AMPA receptor properties of synapses on the partner GABAergic neuron. All modifications of GABAergic input and output required activity of the glutamatergic neuron. Because our system has reduced extrinsic factors, the changes we saw in the GABAergic neuron due to glutamatergic input may reflect initiation of maturation programs that underlie the formation and function of in vivo neural circuits.

  10. Poisson-Like Spiking in Circuits with Probabilistic Synapses

    Science.gov (United States)

    Moreno-Bote, Rubén

    2014-01-01

    Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

  11. Intercellular protein-protein interactions at synapses.

    Science.gov (United States)

    Yang, Xiaofei; Hou, Dongmei; Jiang, Wei; Zhang, Chen

    2014-06-01

    Chemical synapses are asymmetric intercellular junctions through which neurons send nerve impulses to communicate with other neurons or excitable cells. The appropriate formation of synapses, both spatially and temporally, is essential for brain function and depends on the intercellular protein-protein interactions of cell adhesion molecules (CAMs) at synaptic clefts. The CAM proteins link pre- and post-synaptic sites, and play essential roles in promoting synapse formation and maturation, maintaining synapse number and type, accumulating neurotransmitter receptors and ion channels, controlling neuronal differentiation, and even regulating synaptic plasticity directly. Alteration of the interactions of CAMs leads to structural and functional impairments, which results in many neurological disorders, such as autism, Alzheimer's disease and schizophrenia. Therefore, it is crucial to understand the functions of CAMs during development and in the mature neural system, as well as in the pathogenesis of some neurological disorders. Here, we review the function of the major classes of CAMs, and how dysfunction of CAMs relates to several neurological disorders.

  12. The Diversity of Cortical Inhibitory Synapses

    Directory of Open Access Journals (Sweden)

    Yoshiyuki eKubota

    2016-04-01

    Full Text Available The most typical and well known inhibitory action in the cortical microcircuit is a strong inhibition on the target neuron by axo-somatic synapses. However, it has become clear that synaptic inhibition in the cortex is much more diverse and complicated. Firstly, at least ten or more inhibitory non-pyramidal cell subtypes engage in diverse inhibitory functions to produce the elaborate activity characteristic of the different cortical states. Each distinct non-pyramidal cell subtype has its own independent inhibitory function. Secondly, the inhibitory synapses innervate different neuronal domains, such as axons, spines, dendrites and soma, and their IPSP size is not uniform. Thus cortical inhibition is highly complex, with a wide variety of anatomical and physiological modes. Moreover, the functional significance of the various inhibitory synapse innervation styles and their unique structural dynamic behaviors differ from those of excitatory synapses. In this review, we summarize our current understanding of the inhibitory mechanisms of the cortical microcircuit.

  13. Silent synapses in neuromuscular junction development.

    Science.gov (United States)

    Tomàs, Josep; Santafé, Manel M; Lanuza, Maria A; García, Neus; Besalduch, Nuria; Tomàs, Marta

    2011-01-01

    In the last few years, evidence has been found to suggest that some synaptic contacts become silent but can be functionally recruited before they completely retract during postnatal synapse elimination in muscle. The physiological mechanism of developmental synapse elimination may be better understood by studying this synapse recruitment. This Mini-Review collects previously published data and new results to propose a molecular mechanism for axonal disconnection. The mechanism is based on protein kinase C (PKC)-dependent inhibition of acetylcholine (ACh) release. PKC activity may be stimulated by a methoctramine-sensitive M2-type muscarinic receptor and by calcium inflow though P/Q- and L-type voltage-dependent calcium channels. In addition, tropomyosin-related tyrosine kinase B (trkB) receptor-mediated brain-derived neurotrophic factor (BDNF) activity may oppose the PKC-mediated ACh release depression. Thus, a balance between trkB and muscarinic pathways may contribute to the final functional suppression of some neuromuscular synapses during development. © 2010 Wiley-Liss, Inc.

  14. Comparative anatomy of phagocytic and immunological synapses

    Directory of Open Access Journals (Sweden)

    Florence eNiedergang

    2016-01-01

    Full Text Available The generation of phagocytic cups and immunological synapses are crucial events of the innate and adaptive immune responses, respectively. They are triggered by distinct immune receptors and performed by different cell types. However, growing experimental evidence shows that a very close series of molecular and cellular events control these two processes. Thus, the tight and dynamic interplay between receptor signaling, actin and microtubule cytoskeleton, and targeted vesicle traffic are all critical features to build functional phagosomes and immunological synapses. Interestingly, both phagocytic cups and immunological synapses display particular spatial and temporal patterns of receptors and signaling molecules, leading to the notion of phagocytic synapse. Here we discuss both types of structures, their organization and the mechanisms by which they are generated and regulated.

  15. Preferential loss of dorsal-hippocampus synapses underlies memory impairments provoked by short, multimodal stress.

    Science.gov (United States)

    Maras, P M; Molet, J; Chen, Y; Rice, C; Ji, S G; Solodkin, A; Baram, T Z

    2014-07-01

    The cognitive effects of stress are profound, yet it is unknown if the consequences of concurrent multiple stresses on learning and memory differ from those of a single stress of equal intensity and duration. We compared the effects on hippocampus-dependent memory of concurrent, hours-long light, loud noise, jostling and restraint (multimodal stress) with those of restraint or of loud noise alone. We then examined if differences in memory impairment following these two stress types might derive from their differential impact on hippocampal synapses, distinguishing dorsal and ventral hippocampus. Mice exposed to hours-long restraint or loud noise were modestly or minimally impaired in novel object recognition, whereas similar-duration multimodal stress provoked severe deficits. Differences in memory were not explained by differences in plasma corticosterone levels or numbers of Fos-labeled neurons in stress-sensitive hypothalamic neurons. However, although synapses in hippocampal CA3 were impacted by both restraint and multimodal stress, multimodal stress alone reduced synapse numbers severely in dorsal CA1, a region crucial for hippocampus-dependent memory. Ventral CA1 synapses were not significantly affected by either stress modality. Probing the basis of the preferential loss of dorsal synapses after multimodal stress, we found differential patterns of neuronal activation by the two stress types. Cross-correlation matrices, reflecting functional connectivity among activated regions, demonstrated that multimodal stress reduced hippocampal correlations with septum and thalamus and increased correlations with amygdala and BST. Thus, despite similar effects on plasma corticosterone and on hypothalamic stress-sensitive cells, multimodal and restraint stress differ in their activation of brain networks and in their impact on hippocampal synapses. Both of these processes might contribute to amplified memory impairments following short, multimodal stress.

  16. Preferential loss of dorsal-hippocampus synapses underlies memory impairments provoked by short, multimodal stress

    Science.gov (United States)

    Maras, P M; Molet, J; Chen, Y; Rice, C; Ji, S G; Solodkin, A; Baram, T Z

    2014-01-01

    The cognitive effects of stress are profound, yet it is unknown if the consequences of concurrent multiple stresses on learning and memory differ from those of a single stress of equal intensity and duration. We compared the effects on hippocampus-dependent memory of concurrent, hours-long light, loud noise, jostling and restraint (multimodal stress) with those of restraint or of loud noise alone. We then examined if differences in memory impairment following these two stress types might derive from their differential impact on hippocampal synapses, distinguishing dorsal and ventral hippocampus. Mice exposed to hours-long restraint or loud noise were modestly or minimally impaired in novel object recognition, whereas similar-duration multimodal stress provoked severe deficits. Differences in memory were not explained by differences in plasma corticosterone levels or numbers of Fos-labeled neurons in stress-sensitive hypothalamic neurons. However, although synapses in hippocampal CA3 were impacted by both restraint and multimodal stress, multimodal stress alone reduced synapse numbers severely in dorsal CA1, a region crucial for hippocampus-dependent memory. Ventral CA1 synapses were not significantly affected by either stress modality. Probing the basis of the preferential loss of dorsal synapses after multimodal stress, we found differential patterns of neuronal activation by the two stress types. Cross-correlation matrices, reflecting functional connectivity among activated regions, demonstrated that multimodal stress reduced hippocampal correlations with septum and thalamus and increased correlations with amygdala and BST. Thus, despite similar effects on plasma corticosterone and on hypothalamic stress-sensitive cells, multimodal and restraint stress differ in their activation of brain networks and in their impact on hippocampal synapses. Both of these processes might contribute to amplified memory impairments following short, multimodal stress. PMID:24589888

  17. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

    Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

    2015-07-15

    While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

  18. Pursuit of Neurotransmitter Functions: Being Attracted with Fascination of the Synapse.

    Science.gov (United States)

    Konishi, Shiro

    2017-01-01

    In the beginning of the 1970s, only two chemical substances, acetylcholine and γ-aminobutyric acid (GABA), had been definitely established as neurotransmitters. Under such circumstances, I started my scientific career in Professor Masanori Otsuka's lab searching for the transmitter of primary sensory neurons. Until 1976, lines of evidence had accumulated indicating that the undecapeptide substance P could be released as a transmitter from primary afferent fibers into spinal synapses, although the substance P-mediated synaptic response had yet to be identified. Peripheral synapses could serve as a good model and thus, it was demonstrated in the prevertebral sympathetic ganglia by1985 that substance P released from axon collaterals of primary sensory neurons acts as the transmitter mediating non-cholinergic slow excitatory postsynaptic potential (EPSP). At that time, we also found that autonomic synapses were useful to uncover the transmitter role of the opioid peptide enkephalins, whose functions had been unknown since their discovery in 1975. Accordingly, enkephalins were found to serve a transmitter role in mediating presynaptic inhibition of cholinergic fast and non-cholinergic slow transmission in the prevertebral sympathetic ganglia. In 1990s, we attempted to devise a combined technique of brain slices and patch-clamp recordings. We applied it to study the regulatory mechanisms that operate around cerebellar GABAergic inhibitory synapses, because most of the studies then had centered on excitatory synapses and because inhibitory synapses are crucially involved in brain functions and disorders. Consequently, we discovered novel forms of heterosynaptic interactions, dual actions of a single transmitter, and receptor crosstalk, the details of which are described in this review.

  19. Evolving spiking networks with variable resistive memories.

    Science.gov (United States)

    Howard, Gerard; Bull, Larry; de Lacy Costello, Ben; Gale, Ella; Adamatzky, Andrew

    2014-01-01

    Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

  20. Energy-efficient neuron, synapse and STDP integrated circuits.

    Science.gov (United States)

    Cruz-Albrecht, Jose M; Yung, Michael W; Srinivasa, Narayan

    2012-06-01

    Ultra-low energy biologically-inspired neuron and synapse integrated circuits are presented. The synapse includes a spike timing dependent plasticity (STDP) learning rule circuit. These circuits have been designed, fabricated and tested using a 90 nm CMOS process. Experimental measurements demonstrate proper operation. The neuron and the synapse with STDP circuits have an energy consumption of around 0.4 pJ per spike and synaptic operation respectively.

  1. Efficient spiking neural network model of pattern motion selectivity in visual cortex.

    Science.gov (United States)

    Beyeler, Michael; Richert, Micah; Dutt, Nikil D; Krichmar, Jeffrey L

    2014-07-01

    Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.

  2. Synapse Pathology in Psychiatric and Neurologic Disease

    NARCIS (Netherlands)

    M. van Spronsen (Myrrhe); C.C. Hoogenraad (Casper)

    2010-01-01

    textabstractInhibitory and excitatory synapses play a fundamental role in information processing in the brain. Excitatory synapses usually are situated on dendritic spines, small membrane protrusions that harbor glutamate receptors and postsynaptic density components and help transmit electrical

  3. The interplay between neurons and glia in synapse development and plasticity

    OpenAIRE

    Stogsdill, Jeff A; Eroglu, Cagla

    2016-01-01

    In the brain, the formation of complex neuronal networks amenable to experience-dependent remodeling is complicated by the diversity of neurons and synapse types. The establishment of a functional brain depends not only on neurons, but also non-neuronal glial cells. Glia are in continuous bi-directional communication with neurons to direct the formation and refinement of synaptic connectivity. This article reviews important findings, which uncovered cellular and molecular aspects of the neuro...

  4. High-conductance states in a mean-field cortical network model

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ahmadi, Mandana; Hertz, John

    2004-01-01

    cortical network model with random connectivity and conductance-based synapses. We employ mean-field theory with correctly colored noise to describe temporal correlations in the neuronal activity. Our results illuminate the connection between two independent experimental findings: high-conductance states......Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1, indicating a tendency toward spikes being clustered. We show that this behavior emerges naturally in a balanced...... of cortical neurons in their natural environment, and variable non-Poissonian spike statistics with Fano factors greater than 1. (C) 2004 Elsevier B.V. All rights reserved....

  5. Specific recycling receptors are targeted to the immune synapse by the intraflagellar transport system

    Science.gov (United States)

    Finetti, Francesca; Patrussi, Laura; Masi, Giulia; Onnis, Anna; Galgano, Donatella; Lucherini, Orso Maria; Pazour, Gregory J.; Baldari, Cosima T.

    2014-01-01

    ABSTRACT T cell activation requires sustained signaling at the immune synapse, a specialized interface with the antigen-presenting cell (APC) that assembles following T cell antigen receptor (TCR) engagement by major histocompatibility complex (MHC)-bound peptide. Central to sustained signaling is the continuous recruitment of TCRs to the immune synapse. These TCRs are partly mobilized from an endosomal pool by polarized recycling. We have identified IFT20, a component of the intraflagellar transport (IFT) system that controls ciliogenesis, as a central regulator of TCR recycling to the immune synapse. Here, we have investigated the interplay of IFT20 with the Rab GTPase network that controls recycling. We found that IFT20 forms a complex with Rab5 and the TCR on early endosomes. IFT20 knockdown (IFT20KD) resulted in a block in the recycling pathway, leading to a build-up of recycling TCRs in Rab5+ endosomes. Recycling of the transferrin receptor (TfR), but not of CXCR4, was disrupted by IFT20 deficiency. The IFT components IFT52 and IFT57 were found to act together with IFT20 to regulate TCR and TfR recycling. The results provide novel insights into the mechanisms that control TCR recycling and immune synapse assembly, and underscore the trafficking-related function of the IFT system beyond ciliogenesis. PMID:24554435

  6. Synaptic Plasticity and Spike Synchronisation in Neuronal Networks

    Science.gov (United States)

    Borges, Rafael R.; Borges, Fernando S.; Lameu, Ewandson L.; Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Viana, Ricardo L.; Macau, Elbert E. N.; Baptista, Murilo S.; Grebogi, Celso; Batista, Antonio M.

    2017-12-01

    Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.

  7. Resolving dynamics of cell signaling via real-time imaging of the immunological synapse.

    Energy Technology Data Exchange (ETDEWEB)

    Stevens, Mark A.; Pfeiffer, Janet R. (University of New Mexico, Albuquerque, NM); Wilson, Bridget S. (University of New Mexico, Albuquerque, NM); Timlin, Jerilyn Ann; Thomas, James L. (University of New Mexico, Albuquerque, NM); Lidke, Keith A. (University of New Mexico, Albuquerque, NM); Spendier, Kathrin (University of New Mexico, Albuquerque, NM); Oliver, Janet M. (University of New Mexico, Albuquerque, NM); Carroll-Portillo, Amanda (University of New Mexico, Albuquerque, NM); Aaron, Jesse S.; Mirijanian, Dina T.; Carson, Bryan D.; Burns, Alan Richard; Rebeil, Roberto

    2009-10-01

    This highly interdisciplinary team has developed dual-color, total internal reflection microscopy (TIRF-M) methods that enable us to optically detect and track in real time protein migration and clustering at membrane interfaces. By coupling TIRF-M with advanced analysis techniques (image correlation spectroscopy, single particle tracking) we have captured subtle changes in membrane organization that characterize immune responses. We have used this approach to elucidate the initial stages of cell activation in the IgE signaling network of mast cells and the Toll-like receptor (TLR-4) response in macrophages stimulated by bacteria. To help interpret these measurements, we have undertaken a computational modeling effort to connect the protein motion and lipid interactions. This work provides a deeper understanding of the initial stages of cellular response to external agents, including dynamics of interaction of key components in the signaling network at the 'immunological synapse,' the contact region of the cell and its adversary.

  8. Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways

    Science.gov (United States)

    Chung, Won-Suk; Clarke, Laura E.; Wang, Gordon X.; Stafford, Benjamin K.; Sher, Alexander; Chakraborty, Chandrani; Joung, Julia; Foo, Lynette C.; Thompson, Andrew; Chen, Chinfei; Smith, Stephen J.; Barres, Ben A.

    2013-12-01

    To achieve its precise neural connectivity, the developing mammalian nervous system undergoes extensive activity-dependent synapse remodelling. Recently, microglial cells have been shown to be responsible for a portion of synaptic pruning, but the remaining mechanisms remain unknown. Here we report a new role for astrocytes in actively engulfing central nervous system synapses. This process helps to mediate synapse elimination, requires the MEGF10 and MERTK phagocytic pathways, and is strongly dependent on neuronal activity. Developing mice deficient in both astrocyte pathways fail to refine their retinogeniculate connections normally and retain excess functional synapses. Finally, we show that in the adult mouse brain, astrocytes continuously engulf both excitatory and inhibitory synapses. These studies reveal a novel role for astrocytes in mediating synapse elimination in the developing and adult brain, identify MEGF10 and MERTK as critical proteins in the synapse remodelling underlying neural circuit refinement, and have important implications for understanding learning and memory as well as neurological disease processes.

  9. Silent Synapse-Based Circuitry Remodeling in Drug Addiction.

    Science.gov (United States)

    Dong, Yan

    2016-05-01

    Exposure to cocaine, and likely other drugs of abuse, generates α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor-silent glutamatergic synapses in the nucleus accumbens. These immature synaptic contacts evolve after drug withdrawal to redefine the neurocircuital properties. These results raise at least three critical questions: (1) what are the molecular and cellular mechanisms that mediate drug-induced generation of silent synapses; (2) how are neurocircuits remodeled upon generation and evolution of drug-generated silent synapses; and (3) what behavioral consequences are produced by silent synapse-based circuitry remodeling? This short review analyzes related experimental results, and extends them to some speculations. © The Author 2015. Published by Oxford University Press on behalf of CINP.

  10. Evidence for presynaptically silent synapses in the immature hippocampus

    International Nuclear Information System (INIS)

    Yoon, Jae Young; Choi, Sukwoo

    2017-01-01

    Silent synapses show NMDA receptor (NMDAR)-mediated synaptic responses, but not AMPAR-mediated synaptic responses. A prevailing hypothesis states that silent synapses contain NMDARs, but not AMPARs. However, alternative presynaptic hypotheses, according to which AMPARs are present at silent synapses, have been proposed; silent synapses show slow glutamate release via a fusion pore, and glutamate spillover from the neighboring synaptic terminals. Consistent with these presynaptic hypotheses, the peak glutamate concentrations at silent synapses have been estimated to be ≪170 μM, much lower than those seen at functional synapses. Glutamate transients predicted based on the two presynaptic mechanisms have been shown to activate only high-affinity NMDARs, but not low-affinity AMPARs. Interestingly, a previous study has developed a new approach to distinguish between the two presynaptic mechanisms using dextran, an inert macromolecule that reduces the diffusivity of released glutamate: postsynaptic responses through the fusion pore mechanism, but not through the spillover mechanism, are potentiated by reduced glutamate diffusivity. Therefore, we reasoned that if the fusion pore mechanism underlies silent synapses, dextran application would reveal AMPAR-mediated synaptic responses at silent synapses. In the present study, we recorded AMPAR-mediated synaptic responses at the CA3-CA1 synapses in neonatal rats in the presence of blockers for NMDARs and GABAARs. Bath application of dextran revealed synaptic responses at silent synapses. GYKI53655, a selective AMPAR-antagonist, completely inhibited the unsilenced synaptic responses, indicating that the unsilenced synaptic responses are mediated by AMPARs. The dextran-mediated reduction in glutamate diffusivity would also lead to the activation of metabotropic glutamate receptors (mGluRs), which might induce unsilencing via the activation of unknown intracellular signaling. Hence, we determined whether mGluR-blockers alter

  11. TFH-derived dopamine accelerates productive synapses in germinal centres.

    Science.gov (United States)

    Papa, Ilenia; Saliba, David; Ponzoni, Maurilio; Bustamante, Sonia; Canete, Pablo F; Gonzalez-Figueroa, Paula; McNamara, Hayley A; Valvo, Salvatore; Grimbaldeston, Michele; Sweet, Rebecca A; Vohra, Harpreet; Cockburn, Ian A; Meyer-Hermann, Michael; Dustin, Michael L; Doglioni, Claudio; Vinuesa, Carola G

    2017-07-20

    Protective high-affinity antibody responses depend on competitive selection of B cells carrying somatically mutated B-cell receptors by follicular helper T (T FH ) cells in germinal centres. The rapid T-B-cell interactions that occur during this process are reminiscent of neural synaptic transmission pathways. Here we show that a proportion of human T FH cells contain dense-core granules marked by chromogranin B, which are normally found in neuronal presynaptic terminals storing catecholamines such as dopamine. T FH cells produce high amounts of dopamine and release it upon cognate interaction with B cells. Dopamine causes rapid translocation of intracellular ICOSL (inducible T-cell co-stimulator ligand, also known as ICOSLG) to the B-cell surface, which enhances accumulation of CD40L and chromogranin B granules at the human T FH cell synapse and increases the synapse area. Mathematical modelling suggests that faster dopamine-induced T-B-cell interactions increase total germinal centre output and accelerate it by days. Delivery of neurotransmitters across the T-B-cell synapse may be advantageous in the face of infection.

  12. Bidirectional Signaling of Neuregulin-2 Mediates Formation of GABAergic Synapses and Maturation of Glutamatergic Synapses in Newborn Granule Cells of Postnatal Hippocampus.

    Science.gov (United States)

    Lee, Kyu-Hee; Lee, Hyunsu; Yang, Che Ho; Ko, Jeong-Soon; Park, Chang-Hwan; Woo, Ran-Sook; Kim, Joo Yeon; Sun, Woong; Kim, Joung-Hun; Ho, Won-Kyung; Lee, Suk-Ho

    2015-12-16

    Expression of neuregulin-2 (NRG2) is intense in a few regions of the adult brain where neurogenesis persists; however, little is understood about its role in developments of newborn neurons. To study the role of NRG2 in synaptogenesis at different developmental stages, newborn granule cells in rat hippocampal slice cultures were labeled with retrovirus encoding tetracycline-inducible microRNA targeting NRG2 and treated with doxycycline (Dox) at the fourth or seventh postinfection day (dpi). The developmental increase of GABAergic postsynaptic currents (GPSCs) was suppressed by the early Dox treatment (4 dpi), but not by late treatment (7 dpi). The late Dox treatment was used to study the effect of NRG2 depletion specific to excitatory synaptogenesis. The Dox effect on EPSCs emerged 4 d after the impairment in dendritic outgrowth became evident (10 dpi). Notably, Dox treatment abolished the developmental increases of AMPA-receptor mediated EPSCs and the AMPA/NMDA ratio, indicating impaired maturation of glutamatergic synapses. In contrast to GPSCs, Dox effects on EPSCs and dendritic growth were independent of ErbB4 and rescued by concurrent overexpression of NRG2 intracellular domain. These results suggest that forward signaling of NRG2 mediates GABAergic synaptogenesis and its reverse signaling contributes to dendritic outgrowth and maturation of glutamatergic synapses. The hippocampal dentate gyrus is one of special brain regions where neurogenesis persists throughout adulthood. Synaptogenesis is a critical step for newborn neurons to be integrated into preexisting neural network. Because neuregulin-2 (NRG2), a growth factor, is intensely expressed in these regions, we investigated whether it plays a role in synaptogenesis and dendritic growth. We found that NRG2 has dual roles in the development of newborn neurons. For GABAergic synaptogenesis, the extracellular domain of NRG2 acts as a ligand for a receptor on GABAergic neurons. In contrast, its intracellular

  13. Astrocyte-secreted thrombospondin-1 modulates synapse and spine defects in the fragile X mouse model.

    Science.gov (United States)

    Cheng, Connie; Lau, Sally K M; Doering, Laurie C

    2016-08-02

    Astrocytes are key participants in various aspects of brain development and function, many of which are executed via secreted proteins. Defects in astrocyte signaling are implicated in neurodevelopmental disorders characterized by abnormal neural circuitry such as Fragile X syndrome (FXS). In animal models of FXS, the loss in expression of the Fragile X mental retardation 1 protein (FMRP) from astrocytes is associated with delayed dendrite maturation and improper synapse formation; however, the effect of astrocyte-derived factors on the development of neurons is not known. Thrombospondin-1 (TSP-1) is an important astrocyte-secreted protein that is involved in the regulation of spine development and synaptogenesis. In this study, we found that cultured astrocytes isolated from an Fmr1 knockout (Fmr1 KO) mouse model of FXS displayed a significant decrease in TSP-1 protein expression compared to the wildtype (WT) astrocytes. Correspondingly, Fmr1 KO hippocampal neurons exhibited morphological deficits in dendritic spines and alterations in excitatory synapse formation following long-term culture. All spine and synaptic abnormalities were prevented in the presence of either astrocyte-conditioned media or a feeder layer derived from FMRP-expressing astrocytes, or following the application of exogenous TSP-1. Importantly, this work demonstrates the integral role of astrocyte-secreted signals in the establishment of neuronal communication and identifies soluble TSP-1 as a potential therapeutic target for Fragile X syndrome.

  14. Large-scale simulations of plastic neural networks on neuromorphic hardware

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-04-01

    Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

  15. Communication Breakdown: The Impact of Ageing on Synapse Structure

    Science.gov (United States)

    Petralia, Ronald S.; Mattson, Mark P.; Yao, Pamela J.

    2014-01-01

    Impaired synaptic plasticity is implicated in the functional decline of the nervous system associated with ageing. Understanding the structure of ageing synapses is essential to understanding the functions of these synapses and their role in the ageing nervous system. In this review, we summarize studies on ageing synapses in vertebrates and invertebrates, focusing on changes in morphology and ultrastructure. We cover different parts of the nervous system, including the brain, the retina, the cochlea, and the neuromuscular junction. The morphological characteristics of aged synapses could shed light on the underlying molecular changes and their functional consequences. PMID:24495392

  16. Distinct structural and catalytic roles for Zap70 in formation of the immunological synapse in CTL

    Science.gov (United States)

    Jenkins, Misty R; Stinchcombe, Jane C; Au-Yeung, Byron B; Asano, Yukako; Ritter, Alex T; Weiss, Arthur; Griffiths, Gillian M

    2014-01-01

    T cell receptor (TCR) activation leads to a dramatic reorganisation of both membranes and receptors as the immunological synapse forms. Using a genetic model to rapidly inhibit Zap70 catalytic activity we examined synapse formation between cytotoxic T lymphocytes and their targets. In the absence of Zap70 catalytic activity Vav-1 activation occurs and synapse formation is arrested at a stage with actin and integrin rich interdigitations forming the interface between the two cells. The membranes at the synapse are unable to flatten to provide extended contact, and Lck does not cluster to form the central supramolecular activation cluster (cSMAC). Centrosome polarisation is initiated but aborts before reaching the synapse and the granules do not polarise. Our findings reveal distinct roles for Zap70 as a structural protein regulating integrin-mediated control of actin vs its catalytic activity that regulates TCR-mediated control of actin and membrane remodelling during formation of the immunological synapse. DOI: http://dx.doi.org/10.7554/eLife.01310.001 PMID:24596147

  17. Neural Activity During The Formation Of A Giant Auditory Synapse

    NARCIS (Netherlands)

    M.C. Sierksma (Martijn)

    2018-01-01

    markdownabstractThe formation of synapses is a critical step in the development of the brain. During this developmental stage neural activity propagates across the brain from synapse to synapse. This activity is thought to instruct the precise, topological connectivity found in the sensory central

  18. Phencyclidine-induced Loss of Asymmetric Spine Synapses in Rodent Prefrontal Cortex is Reversed by Acute and Chronic Treatment with Olanzapine

    Science.gov (United States)

    Elsworth, John D; Morrow, Bret A; Hajszan, Tibor; Leranth, Csaba; Roth, Robert H

    2011-01-01

    Enduring cognitive deficits exist in schizophrenic patients, long-term abusers of phencyclidine (PCP), as well as in animal PCP models of schizophrenia. It has been suggested that cognitive performance and memory processes are coupled with remodeling of pyramidal dendritic spine synapses in prefrontal cortex (PFC), and that reduced spine density and number of spine synapses in the medial PFC of PCP-treated rats may potentially underlie, at least partially, the cognitive dysfunction previously observed in this animal model. The present data show that the decrease in number of asymmetric (excitatory) spine synapses in layer II/III of PFC, previously noted at 1-week post PCP treatment also occurs, to a lesser degree, in layer V. The decrease in the number of spine synapses in layer II/III was sustained and persisted for at least 4 weeks, paralleling the observed cognitive deficits. Both acute and chronic treatment with the atypical antipsychotic drug, olanzapine, starting at 1 week after PCP treatment at doses that restore cognitive function, reversed the asymmetric spine synapse loss in PFC of PCP-treated rats. Olanzapine had no significant effect on spine synapse number in saline-treated controls. These studies demonstrate that the effect of PCP on asymmetric spine synapse number in PFC lasts at least 4 weeks in this model. This spine synapse loss in PFC is reversed by acute treatment with olanzapine, and this reversal is maintained by chronic oral treatment, paralleling the time course of the restoration of the dopamine deficit, and normalization of cognitive function produced by olanzapine. PMID:21677652

  19. Human tau increases amyloid β plaque size but not amyloid β-mediated synapse loss in a novel mouse model of Alzheimer's disease.

    Science.gov (United States)

    Jackson, Rosemary J; Rudinskiy, Nikita; Herrmann, Abigail G; Croft, Shaun; Kim, JeeSoo Monica; Petrova, Veselina; Ramos-Rodriguez, Juan Jose; Pitstick, Rose; Wegmann, Susanne; Garcia-Alloza, Monica; Carlson, George A; Hyman, Bradley T; Spires-Jones, Tara L

    2016-12-01

    Alzheimer's disease is characterized by the presence of aggregates of amyloid beta (Aβ) in senile plaques and tau in neurofibrillary tangles, as well as marked neuron and synapse loss. Of these pathological changes, synapse loss correlates most strongly with cognitive decline. Synapse loss occurs prominently around plaques due to accumulations of oligomeric Aβ. Recent evidence suggests that tau may also play a role in synapse loss but the interactions of Aβ and tau in synapse loss remain to be determined. In this study, we generated a novel transgenic mouse line, the APP/PS1/rTg21221 line, by crossing APP/PS1 mice, which develop Aβ-plaques and synapse loss, with rTg21221 mice, which overexpress wild-type human tau. When compared to the APP/PS1 mice without human tau, the cross-sectional area of ThioS+ dense core plaques was increased by ~50%. Along with increased plaque size, we observed an increase in plaque-associated dystrophic neurites containing misfolded tau, but there was no exacerbation of neurite curvature or local neuron loss around plaques. Array tomography analysis similarly revealed no worsening of synapse loss around plaques, and no change in the accumulation of Aβ at synapses. Together, these results indicate that adding human wild-type tau exacerbates plaque pathology and neurite deformation but does not exacerbate plaque-associated synapse loss. © 2016 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  20. Presynaptic proteoglycans: sweet organizers of synapse development.

    Science.gov (United States)

    Song, Yoo Sung; Kim, Eunjoon

    2013-08-21

    Synaptic adhesion molecules control neuronal synapse development. In this issue of Neuron, Siddiqui et al. (2013) and de Wit et al. (2013) demonstrate that LRRTM4, a postsynaptic adhesion molecule, trans-synaptically interacts with presynaptic heparan sulfate proteoglycans (HSPGs) to promote synapse development. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Enhanced polychronisation in a spiking network with metaplasticity

    Directory of Open Access Journals (Sweden)

    Mira eGuise

    2015-02-01

    Full Text Available Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002. In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004; Izhikevich, 2006a. Polychronous groups (PNGs develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP, but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.

  2. A novel analytical characterization for short-term plasticity parameters in spiking neural networks.

    Science.gov (United States)

    O'Brien, Michael J; Thibeault, Corey M; Srinivasa, Narayan

    2014-01-01

    Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.

  3. The synchronization of FitzHugh–Nagumo neuron network coupled by gap junction

    International Nuclear Information System (INIS)

    Zhan Yong; Zhang Suhua; Zhao Tongjun; An Hailong; Zhang Zhendong; Han Yingrong; Liu Hui; Zhang Yuhong

    2008-01-01

    It is well known that the strong coupling can synchronize a network of nonlinear oscillators. Synchronization provides the basis of the remarkable computational performance of the brain. In this paper the FitzHugh–Nagumo neuron network is constructed. The dependence of the synchronization on the coupling strength, the noise intensity and the size of the neuron network has been discussed. The results indicate that the coupling among neurons works to improve the synchronization, and noise increases the neuron random dynamics and the local fluctuations; the larger the size of network, the worse the synchronization. The dependence of the synchronization on the strength of the electric synapse coupling and chemical synapse coupling has also been discussed, which proves that electric synapse coupling can enhance the synchronization of the neuron network largely

  4. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses

    Science.gov (United States)

    Ohno, Takeo; Hasegawa, Tsuyoshi; Tsuruoka, Tohru; Terabe, Kazuya; Gimzewski, James K.; Aono, Masakazu

    2011-08-01

    Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs , , , ). In neuromorphic engineering, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag2S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag2S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag2S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.

  5. A phenomenological memristor model for short-term/long-term memory

    International Nuclear Information System (INIS)

    Chen, Ling; Li, Chuandong; Huang, Tingwen; Ahmad, Hafiz Gulfam; Chen, Yiran

    2014-01-01

    Memristor is considered to be a natural electrical synapse because of its distinct memory property and nanoscale. In recent years, more and more similar behaviors are observed between memristors and biological synapse, e.g., short-term memory (STM) and long-term memory (LTM). The traditional mathematical models are unable to capture the new emerging behaviors. In this article, an updated phenomenological model based on the model of the Hewlett–Packard (HP) Labs has been proposed to capture such new behaviors. The new dynamical memristor model with an improved ion diffusion term can emulate the synapse behavior with forgetting effect, and exhibit the transformation between the STM and the LTM. Further, this model can be used in building new type of neural networks with forgetting ability like biological systems, and it is verified by our experiment with Hopfield neural network. - Highlights: • We take the Fick diffusion and the Soret diffusion into account in the ion drift theory. • We develop a new model based on the old HP model. • The new model can describe the forgetting effect and the spike-rate-dependent property of memristor. • The new model can solve the boundary effect of all window functions discussed in [13]. • A new Hopfield neural network with the forgetting ability is built by the new memristor model

  6. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Jan eHahne

    2015-09-01

    Full Text Available Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy...

  7. Meeting the memory challenges of brain-scale network simulation

    Directory of Open Access Journals (Sweden)

    Susanne eKunkel

    2012-01-01

    Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

  8. A cellular model of memory reconsolidation involves reactivation-induced destabilization and restabilization at the sensorimotor synapse in Aplysia.

    Science.gov (United States)

    Lee, Sue-Hyun; Kwak, Chuljung; Shim, Jaehoon; Kim, Jung-Eun; Choi, Sun-Lim; Kim, Hyoung F; Jang, Deok-Jin; Lee, Jin-A; Lee, Kyungmin; Lee, Chi-Hoon; Lee, Young-Don; Miniaci, Maria Concetta; Bailey, Craig H; Kandel, Eric R; Kaang, Bong-Kiun

    2012-08-28

    The memory reconsolidation hypothesis suggests that a memory trace becomes labile after retrieval and needs to be reconsolidated before it can be stabilized. However, it is unclear from earlier studies whether the same synapses involved in encoding the memory trace are those that are destabilized and restabilized after the synaptic reactivation that accompanies memory retrieval, or whether new and different synapses are recruited. To address this issue, we studied a simple nonassociative form of memory, long-term sensitization of the gill- and siphon-withdrawal reflex in Aplysia, and its cellular analog, long-term facilitation at the sensory-to-motor neuron synapse. We found that after memory retrieval, behavioral long-term sensitization in Aplysia becomes labile via ubiquitin/proteasome-dependent protein degradation and is reconsolidated by means of de novo protein synthesis. In parallel, we found that on the cellular level, long-term facilitation at the sensory-to-motor neuron synapse that mediates long-term sensitization is also destabilized by protein degradation and is restabilized by protein synthesis after synaptic reactivation, a procedure that parallels memory retrieval or retraining evident on the behavioral level. These results provide direct evidence that the same synapses that store the long-term memory trace encoded by changes in the strength of synaptic connections critical for sensitization are disrupted and reconstructed after signal retrieval.

  9. Rhythmic changes in synapse numbers in Drosophila melanogaster motor terminals.

    Directory of Open Access Journals (Sweden)

    Santiago Ruiz

    Full Text Available Previous studies have shown that the morphology of the neuromuscular junction of the flight motor neuron MN5 in Drosophila melanogaster undergoes daily rhythmical changes, with smaller synaptic boutons during the night, when the fly is resting, than during the day, when the fly is active. With electron microscopy and laser confocal microscopy, we searched for a rhythmic change in synapse numbers in this neuron, both under light:darkness (LD cycles and constant darkness (DD. We expected the number of synapses to increase during the morning, when the fly has an intense phase of locomotion activity under LD and DD. Surprisingly, only our DD data were consistent with this hypothesis. In LD, we found more synapses at midnight than at midday. We propose that under LD conditions, there is a daily rhythm of formation of new synapses in the dark phase, when the fly is resting, and disassembly over the light phase, when the fly is active. Several parameters appeared to be light dependent, since they were affected differently under LD or DD. The great majority of boutons containing synapses had only one and very few had either two or more, with a 70∶25∶5 ratio (one, two and three or more synapses in LD and 75∶20∶5 in DD. Given the maintenance of this proportion even when both bouton and synapse numbers changed with time, we suggest that there is a homeostatic mechanism regulating synapse distribution among MN5 boutons.

  10. Mathematical modeling of sustainable synaptogenesis by repetitive stimuli suggests signaling mechanisms in vivo.

    Directory of Open Access Journals (Sweden)

    Hiromu Takizawa

    Full Text Available The mechanisms of long-term synaptic maintenance are a key component to understanding the mechanism of long-term memory. From biological experiments, a hypothesis arose that repetitive stimuli with appropriate intervals are essential to maintain new synapses for periods of longer than a few days. We successfully reproduce the time-course of relative numbers of synapses with our mathematical model in the same conditions as biological experiments, which used Adenosine-3', 5'-cyclic monophosphorothioate, Sp-isomer (Sp-cAMPS as external stimuli. We also reproduce synaptic maintenance responsiveness to intervals of Sp-cAMPS treatment accompanied by PKA activation. The model suggests a possible mechanism of sustainable synaptogenesis which consists of two steps. First, the signal transduction from an external stimulus triggers the synthesis of a new signaling protein. Second, the new signaling protein is required for the next signal transduction with the same stimuli. As a result, the network component is modified from the first network, and a different signal is transferred which triggers the synthesis of another new signaling molecule. We refer to this hypothetical mechanism as network succession. We build our model on the basis of two hypotheses: (1 a multi-step network succession induces downregulation of SSH and COFILIN gene expression, which triggers the production of stable F-actin; (2 the formation of a complex of stable F-actin with Drebrin at PSD is the critical mechanism to achieve long-term synaptic maintenance. Our simulation shows that a three-step network succession is sufficient to reproduce sustainable synapses for a period longer than 14 days. When we change the network structure to a single step network, the model fails to follow the exact condition of repetitive signals to reproduce a sufficient number of synapses. Another advantage of the three-step network succession is that this system indicates a greater tolerance of parameter

  11. Cell adhesion and matricellular support by astrocytes of the tripartite synapse

    NARCIS (Netherlands)

    Hillen, Anne E J; Burbach, J Peter H; Hol, Elly M

    2018-01-01

    Astrocytes contribute to the formation, function, and plasticity of synapses. Their processes enwrap the neuronal components of the tripartite synapse, and due to this close interaction they are perfectly positioned to modulate neuronal communication. The interaction between astrocytes and synapses

  12. The influence of single bursts vs. single spikes at excitatory dendrodendritic synapses

    Science.gov (United States)

    Masurkar, Arjun V.; Chen, Wei R.

    2015-01-01

    The synchronization of neuronal activity is thought to enhance information processing. There is much evidence supporting rhythmically bursting external tufted cells (ETCs) of the rodent olfactory bulb glomeruli coordinating the activation of glomerular interneurons and mitral cells via dendrodendritic excitation. However, as bursting has variable significance at axodendritic cortical synapses, it is not clear if ETC bursting imparts a specific functional advantage over the preliminary spike in dendrodendritic synaptic networks. To answer this question, we investigated the influence of single ETC bursts and spikes with the in-vitro rat olfactory bulb preparation at different levels of processing, via calcium imaging of presynaptic ETC dendrites, dual electrical recording of ETC–interneuron synaptic pairs, and multicellular calcium imaging of ETC-induced population activity. Our findings supported single ETC bursts, vs. single spikes, driving robust presynaptic calcium signaling, which in turn was associated with profound extension of the initial monosynaptic spike-driven dendrodendritic excitatory postsynaptic potential. This extension could be driven by either the spike-dependent or spike-independent components of the burst. At the population level, burst-induced excitation was more widespread and reliable compared with single spikes. This further supports the ETC network, in part due to a functional advantage of bursting at excitatory dendrodendritic synapses, coordinating synchronous activity at behaviorally relevant frequencies related to odor processing in vivo. PMID:22277089

  13. Cell Biology of Astrocyte-Synapse Interactions.

    Science.gov (United States)

    Allen, Nicola J; Eroglu, Cagla

    2017-11-01

    Astrocytes, the most abundant glial cells in the mammalian brain, are critical regulators of brain development and physiology through dynamic and often bidirectional interactions with neuronal synapses. Despite the clear importance of astrocytes for the establishment and maintenance of proper synaptic connectivity, our understanding of their role in brain function is still in its infancy. We propose that this is at least in part due to large gaps in our knowledge of the cell biology of astrocytes and the mechanisms they use to interact with synapses. In this review, we summarize some of the seminal findings that yield important insight into the cellular and molecular basis of astrocyte-neuron communication, focusing on the role of astrocytes in the development and remodeling of synapses. Furthermore, we pose some pressing questions that need to be addressed to advance our mechanistic understanding of the role of astrocytes in regulating synaptic development. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  15. Communication, the centrosome and the immunological synapse.

    Science.gov (United States)

    Stinchcombe, Jane C; Griffiths, Gillian M

    2014-09-05

    Recent findings on the behaviour of the centrosome at the immunological synapse suggest a critical role for centrosome polarization in controlling the communication between immune cells required to generate an effective immune response. The features observed at the immunological synapse show parallels to centrosome (basal body) polarization seen in cilia and flagella, and the cellular communication that is now known to occur at all of these sites.

  16. Recruitment of activation receptors at inhibitory NK cell immune synapses.

    Directory of Open Access Journals (Sweden)

    Nicolas Schleinitz

    2008-09-01

    Full Text Available Natural killer (NK cell activation receptors accumulate by an actin-dependent process at cytotoxic immune synapses where they provide synergistic signals that trigger NK cell effector functions. In contrast, NK cell inhibitory receptors, including members of the MHC class I-specific killer cell Ig-like receptor (KIR family, accumulate at inhibitory immune synapses, block actin dynamics, and prevent actin-dependent phosphorylation of activation receptors. Therefore, one would predict inhibition of actin-dependent accumulation of activation receptors when inhibitory receptors are engaged. By confocal imaging of primary human NK cells in contact with target cells expressing physiological ligands of NK cell receptors, we show here that this prediction is incorrect. Target cells included a human cell line and transfected Drosophila insect cells that expressed ligands of NK cell activation receptors in combination with an MHC class I ligand of inhibitory KIR. The two NK cell activation receptors CD2 and 2B4 accumulated and co-localized with KIR at inhibitory immune synapses. In fact, KIR promoted CD2 and 2B4 clustering, as CD2 and 2B4 accumulated more efficiently at inhibitory synapses. In contrast, accumulation of KIR and of activation receptors at inhibitory synapses correlated with reduced density of the integrin LFA-1. These results imply that inhibitory KIR does not prevent CD2 and 2B4 signaling by blocking their accumulation at NK cell immune synapses, but by blocking their ability to signal within inhibitory synapses.

  17. Synaptic energy drives the information processing mechanisms in spiking neural networks.

    Science.gov (United States)

    El Laithy, Karim; Bogdan, Martin

    2014-04-01

    Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.

  18. The cytotoxic T lymphocyte immune synapse at a glance.

    Science.gov (United States)

    Dieckmann, Nele M G; Frazer, Gordon L; Asano, Yukako; Stinchcombe, Jane C; Griffiths, Gillian M

    2016-08-01

    The immune synapse provides an important structure for communication with immune cells. Studies on immune synapses formed by cytotoxic T lymphocytes (CTLs) highlight the dynamic changes and specialised mechanisms required to facilitate focal signalling and polarised secretion in immune cells. In this Cell Science at a Glance article and the accompanying poster, we illustrate the different steps that reveal the specialised mechanisms used to focus secretion at the CTL immune synapse and allow CTLs to be such efficient and precise serial killers. © 2016. Published by The Company of Biologists Ltd.

  19. Defects of the Glycinergic Synapse in Zebrafish

    OpenAIRE

    Ogino, Kazutoyo; Hirata, Hiromi

    2016-01-01

    Glycine mediates fast inhibitory synaptic transmission. Physiological importance of the glycinergic synapse is well established in the brainstem and the spinal cord. In humans, the loss of glycinergic function in the spinal cord and brainstem leads to hyperekplexia, which is characterized by an excess startle reflex to sudden acoustic or tactile stimulation. In addition, glycinergic synapses in this region are also involved in the regulation of respiration and locomotion, and in the nocicepti...

  20. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  1. Single or multiple synchronization transitions in scale-free neuronal networks with electrical or chemical coupling

    International Nuclear Information System (INIS)

    Hao Yinghang; Gong, Yubing; Wang Li; Ma Xiaoguang; Yang Chuanlu

    2011-01-01

    Research highlights: → Single synchronization transition for gap-junctional coupling. → Multiple synchronization transitions for chemical synaptic coupling. → Gap junctions and chemical synapses have different impacts on synchronization transition. → Chemical synapses may play a dominant role in neurons' information processing. - Abstract: In this paper, we have studied time delay- and coupling strength-induced synchronization transitions in scale-free modified Hodgkin-Huxley (MHH) neuron networks with gap-junctions and chemical synaptic coupling. It is shown that the synchronization transitions are much different for these two coupling types. For gap-junctions, the neurons exhibit a single synchronization transition with time delay and coupling strength, while for chemical synapses, there are multiple synchronization transitions with time delay, and the synchronization transition with coupling strength is dependent on the time delay lengths. For short delays we observe a single synchronization transition, whereas for long delays the neurons exhibit multiple synchronization transitions as the coupling strength is varied. These results show that gap junctions and chemical synapses have different impacts on the pattern formation and synchronization transitions of the scale-free MHH neuronal networks, and chemical synapses, compared to gap junctions, may play a dominant and more active function in the firing activity of the networks. These findings would be helpful for further understanding the roles of gap junctions and chemical synapses in the firing dynamics of neuronal networks.

  2. Single or multiple synchronization transitions in scale-free neuronal networks with electrical or chemical coupling

    Energy Technology Data Exchange (ETDEWEB)

    Hao Yinghang [School of Physics, Ludong University, Yantai 264025 (China); Gong, Yubing, E-mail: gongyubing09@hotmail.co [School of Physics, Ludong University, Yantai 264025 (China); Wang Li; Ma Xiaoguang; Yang Chuanlu [School of Physics, Ludong University, Yantai 264025 (China)

    2011-04-15

    Research highlights: Single synchronization transition for gap-junctional coupling. Multiple synchronization transitions for chemical synaptic coupling. Gap junctions and chemical synapses have different impacts on synchronization transition. Chemical synapses may play a dominant role in neurons' information processing. - Abstract: In this paper, we have studied time delay- and coupling strength-induced synchronization transitions in scale-free modified Hodgkin-Huxley (MHH) neuron networks with gap-junctions and chemical synaptic coupling. It is shown that the synchronization transitions are much different for these two coupling types. For gap-junctions, the neurons exhibit a single synchronization transition with time delay and coupling strength, while for chemical synapses, there are multiple synchronization transitions with time delay, and the synchronization transition with coupling strength is dependent on the time delay lengths. For short delays we observe a single synchronization transition, whereas for long delays the neurons exhibit multiple synchronization transitions as the coupling strength is varied. These results show that gap junctions and chemical synapses have different impacts on the pattern formation and synchronization transitions of the scale-free MHH neuronal networks, and chemical synapses, compared to gap junctions, may play a dominant and more active function in the firing activity of the networks. These findings would be helpful for further understanding the roles of gap junctions and chemical synapses in the firing dynamics of neuronal networks.

  3. NKp46 clusters at the immune synapse and regulates NK cell polarization

    Directory of Open Access Journals (Sweden)

    Uzi eHadad

    2015-09-01

    Full Text Available Natural killer cells play an important role in first-line defense against tumor and virus-infected cells. The activity of NK cells is tightly regulated by a repertoire of cell-surface expressed inhibitory and activating receptors. NKp46 is a major NK cell activating receptor that is involved in the elimination of target cells. NK cells form different types of synapses that result in distinct functional outcomes: cytotoxic, inhibitory, and regulatory. Recent studies revealed that complex integration of NK receptor signaling controls cytoskeletal rearrangement and other immune synapse-related events. However the distinct nature by which NKp46 participates in NK immunological synapse formation and function remains unknown. In this study we determined that NKp46 forms microclusters structures at the immune synapse between NK cells and target cells. Over-expression of human NKp46 is correlated with increased accumulation of F-actin mesh at the immune synapse. Concordantly, knock-down of NKp46 in primary human NK cells decreased recruitment of F-actin to the synapse. Live cell imaging experiments showed a linear correlation between NKp46 expression and lytic granules polarization to the immune synapse. Taken together, our data suggest that NKp46 signaling directly regulates the NK lytic immune synapse from early formation to late function.

  4. The 'disector' a tool for quantitative assessment of synaptic plasticity an example on hippocampal synapses and synapse-perforations in ageing rats

    NARCIS (Netherlands)

    Groot, D.M.G. de; Bierman, E.P.B.; Bruijnzeel, P.L.B.; Woutersen, R.A.

    1995-01-01

    The 'disector' method was used to estimate number and size of simple non-perforated and complex 'perforated' synapses and their 'perforations' in the hippocampal CA3 area of 3, 12, 24 and 30 months old rats. A decrease with age from 3 to 24 months of age in the number of non-perforated synapses per

  5. Molecular Mechanisms of Synaptic Specificity: Spotlight on Hippocampal and Cerebellar Synapse Organizers.

    Science.gov (United States)

    Park, Dongseok; Bae, Sungwon; Yoon, Taek Han; Ko, Jaewon

    2018-04-18

    Synapses and neural circuits form with exquisite specificity during brain development to allow the precise and appropriate flow of neural information. Although this property of synapses and neural circuits has been extensively investigated for more than a century, molecular mechanisms underlying this property are only recently being unveiled. Recent studies highlight several classes of cell-surface proteins as organizing hubs in building structural and functional architectures of specific synapses and neural circuits. In the present minireview, we discuss recent findings on various synapse organizers that confer the distinct properties of specific synapse types and neural circuit architectures in mammalian brains, with a particular focus on the hippocampus and cerebellum.

  6. The influence of single bursts versus single spikes at excitatory dendrodendritic synapses.

    Science.gov (United States)

    Masurkar, Arjun V; Chen, Wei R

    2012-02-01

    The synchronization of neuronal activity is thought to enhance information processing. There is much evidence supporting rhythmically bursting external tufted cells (ETCs) of the rodent olfactory bulb glomeruli coordinating the activation of glomerular interneurons and mitral cells via dendrodendritic excitation. However, as bursting has variable significance at axodendritic cortical synapses, it is not clear if ETC bursting imparts a specific functional advantage over the preliminary spike in dendrodendritic synaptic networks. To answer this question, we investigated the influence of single ETC bursts and spikes with the in vitro rat olfactory bulb preparation at different levels of processing, via calcium imaging of presynaptic ETC dendrites, dual electrical recording of ETC -interneuron synaptic pairs, and multicellular calcium imaging of ETC-induced population activity. Our findings supported single ETC bursts, versus single spikes, driving robust presynaptic calcium signaling, which in turn was associated with profound extension of the initial monosynaptic spike-driven dendrodendritic excitatory postsynaptic potential. This extension could be driven by either the spike-dependent or spike-independent components of the burst. At the population level, burst-induced excitation was more widespread and reliable compared with single spikes. This further supports the ETC network, in part due to a functional advantage of bursting at excitatory dendrodendritic synapses, coordinating synchronous activity at behaviorally relevant frequencies related to odor processing in vivo. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  7. Shaping Synapses by the Neural Extracellular Matrix

    Directory of Open Access Journals (Sweden)

    Maura Ferrer-Ferrer

    2018-05-01

    Full Text Available Accumulating data support the importance of interactions between pre- and postsynaptic neuronal elements with astroglial processes and extracellular matrix (ECM for formation and plasticity of chemical synapses, and thus validate the concept of a tetrapartite synapse. Here we outline the major mechanisms driving: (i synaptogenesis by secreted extracellular scaffolding molecules, like thrombospondins (TSPs, neuronal pentraxins (NPs and cerebellins, which respectively promote presynaptic, postsynaptic differentiation or both; (ii maturation of synapses via reelin and integrin ligands-mediated signaling; and (iii regulation of synaptic plasticity by ECM-dependent control of induction and consolidation of new synaptic configurations. Particularly, we focused on potential importance of activity-dependent concerted activation of multiple extracellular proteases, such as ADAMTS4/5/15, MMP9 and neurotrypsin, for permissive and instructive events in synaptic remodeling through localized degradation of perisynaptic ECM and generation of proteolytic fragments as inducers of synaptic plasticity.

  8. Synchrony detection and amplification by silicon neurons with STDP synapses.

    Science.gov (United States)

    Bofill-i-petit, Adria; Murray, Alan F

    2004-09-01

    Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

  9. Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

    Science.gov (United States)

    Srinivasa, Narayan; Cho, Youngkwan

    2014-01-01

    A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  10. Distinct transmitter release properties determine differences in short-term plasticity at functional and silent synapses.

    Science.gov (United States)

    Cabezas, Carolina; Buño, Washington

    2006-05-01

    Recent evidence suggests that functional and silent synapses are not only postsynaptically different but also presynaptically distinct. The presynaptic differences may be of functional importance in memory formation because a proposed mechanism for long-term potentiation is the conversion of silent synapses into functional ones. However, there is little direct experimentally evidence of these differences. We have investigated the transmitter release properties of functional and silent Schaffer collateral synapses and show that on the average functional synapses displayed a lower percentage of failures and higher excitatory postsynaptic current (EPSC) amplitudes than silent synapses at +60 mV. Moreover, functional but not silent synapses show paired-pulse facilitation (PPF) at +60 mV and thus presynaptic short-term plasticity will be distinct in the two types of synapse. We examined whether intraterminal endoplasmic reticulum Ca2+ stores influenced the release properties of these synapses. Ryanodine (100 microM) and thapsigargin (1 microM) increased the percentage of failures and decreased both the EPSC amplitude and PPF in functional synapses. Caffeine (10 mM) had the opposite effects. In contrast, silent synapses were insensitive to both ryanodine and caffeine. Hence we have identified differences in the release properties of functional and silent synapses, suggesting that synaptic terminals of functional synapses express regulatory molecular mechanisms that are absent in silent synapses.

  11. Removal of area CA3 from hippocampal slices induces postsynaptic plasticity at Schaffer collateral synapses that normalizes CA1 pyramidal cell discharge.

    Science.gov (United States)

    Dumas, Theodore C; Uttaro, Michael R; Barriga, Carolina; Brinkley, Tiffany; Halavi, Maryam; Wright, Susan N; Ferrante, Michele; Evans, Rebekah C; Hawes, Sarah L; Sanders, Erin M

    2018-05-05

    Neural networks that undergo acute insults display remarkable reorganization. This injury related plasticity is thought to permit recovery of function in the face of damage that cannot be reversed. Previously, an increase in the transmission strength at Schaffer collateral to CA1 pyramidal cell synapses was observed after long-term activity reduction in organotypic hippocampal slices. Here we report that, following acute preparation of adult rat hippocampal slices and surgical removal of area CA3, input to area CA1 was reduced and Schaffer collateral synapses underwent functional strengthening. This increase in synaptic strength was limited to Schaffer collateral inputs (no alteration to temporoammonic synapses) and acted to normalize postsynaptic discharge, supporting a homeostatic or compensatory response. Short-term plasticity was not altered, but an increase in immunohistochemical labeling of GluA1 subunits was observed in the stratum radiatum (but not stratum moleculare), suggesting increased numbers of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors and a postsynaptic locus of expression. Combined, these data support the idea that, in response to the reduction in presynaptic activity caused by removal of area CA3, Schaffer collateral synapses undergo a relatively rapid increase in functional efficacy likely supported by insertion of more AMPARs, which maintains postsynaptic excitability in CA1 pyramidal neurons. This novel fast compensatory plasticity exhibits properties that would allow it to maintain optimal network activity levels in the hippocampus, a brain structure lauded for its ongoing experience-dependent malleability. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. The demise of the synapse as the locus of memory: A looming paradigm shift?

    Directory of Open Access Journals (Sweden)

    Patrick C. Trettenbrein

    2016-11-01

    Full Text Available Synaptic plasticity is widely considered to be the neurobiological basis of learning and memory by neuroscientists and researchers in adjacent fields, though diverging opinions are increasingly being recognised. From the perspective of what we might call classical cognitive science it has always been understood that the mind/brain is to be considered a computational-representational system. Proponents of the information-processing approach to cognitive science have long been critical of connectionist or network approaches to (neuro-cognitive architecture, pointing to the shortcomings of the associative psychology that underlies Hebbian learning as well as to the fact that synapses are practically unfit to implement symbols. Recent work on memory has been adding fuel to the fire and current findings in neuroscience now provide first tentative neurobiological evidence for the cognitive scientists’ doubts about the synapse as the (sole locus of memory in the brain. This paper briefly considers the history and appeal of synaptic plasticity as a memory mechanism, followed by a summary of the cognitive scientists’ objections regarding these assertions. Next, a variety of tentative neuroscientific evidence that appears to substantiate questioning the idea of the synapse as the locus of memory is presented. On this basis, a novel way of thinking about the role of synaptic plasticity in learning and memory is proposed.

  13. Self-organized critical neural networks

    International Nuclear Information System (INIS)

    Bornholdt, Stefan; Roehl, Torsten

    2003-01-01

    A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters

  14. Astrocytic Ca2+ signals are required for the functional integrity of tripartite synapses

    Directory of Open Access Journals (Sweden)

    Tanaka Mika

    2013-01-01

    Full Text Available Abstract Background Neuronal activity alters calcium ion (Ca2+ dynamics in astrocytes, but the physiologic relevance of these changes is controversial. To examine this issue further, we generated an inducible transgenic mouse model in which the expression of an inositol 1,4,5-trisphosphate absorbent, “IP3 sponge”, attenuates astrocytic Ca2+ signaling. Results Attenuated Ca2+ activity correlated with reduced astrocytic coverage of asymmetric synapses in the hippocampal CA1 region in these animals. The decreased astrocytic ‘protection’ of the synapses facilitated glutamate ‘spillover’, which was reflected by prolonged glutamate transporter currents in stratum radiatum astrocytes and enhanced N-methyl-D-aspartate receptor currents in CA1 pyramidal neurons in response to burst stimulation. These mice also exhibited behavioral impairments in spatial reference memory and remote contextual fear memory, in which hippocampal circuits are involved. Conclusions Our findings suggest that IP3-mediated astrocytic Ca2+ signaling correlates with the formation of functional tripartite synapses in the hippocampus.

  15. Alterations in the properties of neonatal thalamocortical synapses with time in in vitro slices.

    Directory of Open Access Journals (Sweden)

    Liliana L Luz

    Full Text Available New synapses are constantly being generated and lost in the living brain with only a subset of these being stabilized to form an enduring component of neuronal circuitry. The properties of synaptic transmission have primarily been established in a variety of in vitro neuronal preparations. It is not clear, however, if newly-formed and persistent synapses contribute to the results of these studies consistently throughout the lifespan of these preparations. In neonatal somatosensory, barrel, cortex we have previously hypothesized that a population of thalamocortical synapses displaying unusually slow kinetics represent newly-formed, default-transient synapses. This clear phenotype would provide an ideal tool to investigate if such newly formed synapses consistently contribute to synaptic transmission throughout a normal experimental protocol. We show that the proportion of synapses recorded in vitro displaying slow kinetics decreases with time after brain slice preparation. However, slow synapses persist in vitro in the presence of either minocycline, an inhibitor of microglia-mediated synapse elimination, or the TrkB agonist 7,8-dihydroxyflavone a promoter of synapse formation. These findings show that the observed properties of synaptic transmission may systematically change with time in vitro in a standard brain slice preparation.

  16. Localization of mineralocorticoid receptors at mammalian synapses.

    Directory of Open Access Journals (Sweden)

    Eric M Prager

    Full Text Available In the brain, membrane associated nongenomic steroid receptors can induce fast-acting responses to ion conductance and second messenger systems of neurons. Emerging data suggest that membrane associated glucocorticoid and mineralocorticoid receptors may directly regulate synaptic excitability during times of stress when adrenal hormones are elevated. As the key neuron signaling interface, the synapse is involved in learning and memory, including traumatic memories during times of stress. The lateral amygdala is a key site for synaptic plasticity underlying conditioned fear, which can both trigger and be coincident with the stress response. A large body of electrophysiological data shows rapid regulation of neuronal excitability by steroid hormone receptors. Despite the importance of these receptors, to date, only the glucocorticoid receptor has been anatomically localized to the membrane. We investigated the subcellular sites of mineralocorticoid receptors in the lateral amygdala of the Sprague-Dawley rat. Immunoblot analysis revealed the presence of mineralocorticoid receptors in the amygdala. Using electron microscopy, we found mineralocorticoid receptors expressed at both nuclear including: glutamatergic and GABAergic neurons and extra nuclear sites including: presynaptic terminals, neuronal dendrites, and dendritic spines. Importantly we also observed mineralocorticoid receptors at postsynaptic membrane densities of excitatory synapses. These data provide direct anatomical evidence supporting the concept that, at some synapses, synaptic transmission is regulated by mineralocorticoid receptors. Thus part of the stress signaling response in the brain is a direct modulation of the synapse itself by adrenal steroids.

  17. The short- and long-term proteomic effects of sleep deprivation on the cortical and thalamic synapses.

    Science.gov (United States)

    Simor, Attila; Györffy, Balázs András; Gulyássy, Péter; Völgyi, Katalin; Tóth, Vilmos; Todorov, Mihail Ivilinov; Kis, Viktor; Borhegyi, Zsolt; Szabó, Zoltán; Janáky, Tamás; Drahos, László; Juhász, Gábor; Kékesi, Katalin Adrienna

    2017-03-01

    Acute total sleep deprivation (SD) impairs memory consolidation, attention, working memory and perception. Structural, electrophysiological and molecular experimental approaches provided evidences for the involvement of sleep in synaptic functions. Despite the wide scientific interest on the effects of sleep on the synapse, there is a lack of systematic investigation of sleep-related changes in the synaptic proteome. We isolated parietal cortical and thalamic synaptosomes of rats after 8h of total SD by gentle handling and 16h after the end of deprivation to investigate the short- and longer-term effects of SD on the synaptic proteome, respectively. The SD efficiency was verified by electrophysiology. Protein abundance alterations of the synaptosomes were analyzed by fluorescent two-dimensional differential gel electrophoresis and by tandem mass spectrometry. As several altered proteins were found to be involved in synaptic strength regulation, our data can support the synaptic homeostasis hypothesis function of sleep and highlight the long-term influence of SD after the recovery sleep period, mostly on cortical synapses. Furthermore, the large-scale and brain area-specific protein network change in the synapses may support both ideas of sleep-related synaptogenesis and molecular maintenance and reorganization in normal rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Hypoxia-Induced neonatal seizures diminish silent synapses and long-term potentiation in hippocampal CA1 neurons

    Science.gov (United States)

    Zhou, Chengwen; Bell, Jocelyn J. Lippman; Sun, Hongyu; Jensen, Frances E.

    2012-01-01

    Neonatal seizures can lead to epilepsy and long-term cognitive deficits in adulthood. Using a rodent model of the most common form of human neonatal seizures, hypoxia-induced seizures (HS), we aimed to determine whether these seizures modify long-term potentiation (LTP) and “silent” N-methyl-D-aspartate receptor (NMDAR)-only synapses in hippocampal CA1. At 48-72 hours (hrs) post-HS, electrophysiology and immunofluorescent confocal microscopy revealed a significant decrease in the incidence of silent synapses, and an increase in amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) at the synapses. Coincident with this decrease in silent synapses, there was an attenuation of LTP elicited by either tetanic stimulation of Schaffer collaterals or a pairing protocol, and persistent attenuation of LTP in slices removed in later adulthood after P10 HS. Furthermore, post-seizure treatment in vivo with the AMPAR antagonist 2,3-dihydroxy-6-nitro-7-sulfonyl-benzo[f]quinoxaline (NBQX) protected against the HS-induced depletion of silent synapses and preserved LTP. Thus, this study demonstrates a novel mechanism by which early-life seizures could impair synaptic plasticity, suggesting a potential target for therapeutic strategies to prevent long-term cognitive deficits. PMID:22171027

  19. Maternal dietary loads of alpha-tocopherol increase synapse density and glial synaptic coverage in the hippocampus of adult offspring

    Directory of Open Access Journals (Sweden)

    S. Salucci

    2014-05-01

    Full Text Available An increased intake of the antioxidant α-Tocopherol (vitamin E is recommended in complicated pregnancies, to prevent free radical damage to mother and fetus. However, the anti-PKC and antimitotic activity of α-Tocopherol raises concerns about its potential effects on brain development. Recently, we found that maternal dietary loads of α-Tocopherol through pregnancy and lactation cause developmental deficit in hippocampal synaptic plasticity in rat offspring. The defect persisted into adulthood, with behavioral alterations in hippocampus-dependent learning. Here, using the same rat model of maternal supplementation, ultrastructural morphometric studies were carried out to provide mechanistic interpretation to such a functional impairment in adult offspring by the occurrence of long-term changes in density and morphological features of hippocampal synapses. Higher density of axo-spinous synapses was found in CA1 stratum radiatum of α-Tocopherol-exposed rats compared to controls, pointing to a reduced synapse pruning. No morphometric changes were found in synaptic ultrastructural features, i.e., perimeter of axon terminals, length of synaptic specializations, extension of bouton-spine contact. Glia-synapse anatomical relationship was also affected. Heavier astrocytic coverage of synapses was observed in Tocopherol-treated offspring, notably surrounding axon terminals; moreover, the percentage of synapses contacted by astrocytic endfeet at bouton-spine interface (tripartite synapses was increased. These findings indicate that gestational and neonatal exposure to supranutritional tocopherol intake can result in anatomical changes of offspring hippocampus that last through adulthood. These include a surplus of axo-spinous synapses and an aberrant glia-synapse relationship, which may represent the morphological signature of previously described alterations in synaptic plasticity and hippocampus-dependent learning.

  20. A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

    Science.gov (United States)

    Montes, Jesus; Gomez, Elena; Merchán-Pérez, Angel; DeFelipe, Javier; Peña, Jose-Maria

    2013-01-01

    Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is

  1. A machine learning method for the prediction of receptor activation in the simulation of synapses.

    Directory of Open Access Journals (Sweden)

    Jesus Montes

    Full Text Available Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of

  2. The immunological synapse: a focal point for endocytosis and exocytosis.

    Science.gov (United States)

    Griffiths, Gillian M; Tsun, Andy; Stinchcombe, Jane C

    2010-05-03

    There are many different cells in the immune system. To mount an effective immune response, they need to communicate with each other. One way in which this is done is by the formation of immunological synapses between cells. Recent developments show that the immune synapse serves as a focal point for exocytosis and endocytosis, directed by centrosomal docking at the plasma membrane. In this respect, formation of the immunological synapse bears striking similarities to cilia formation and cytokinesis. These intriguing observations suggest that the centrosome may play a conserved role in designating a specialized area of membrane for localized endocytosis and exocytosis.

  3. Presynaptic Membrane Receptors Modulate ACh Release, Axonal Competition and Synapse Elimination during Neuromuscular Junction Development.

    Science.gov (United States)

    Tomàs, Josep; Garcia, Neus; Lanuza, Maria A; Santafé, Manel M; Tomàs, Marta; Nadal, Laura; Hurtado, Erica; Simó, Anna; Cilleros, Víctor

    2017-01-01

    During the histogenesis of the nervous system a lush production of neurons, which establish an excessive number of synapses, is followed by a drop in both neurons and synaptic contacts as maturation proceeds. Hebbian competition between axons with different activities leads to the loss of roughly half of the neurons initially produced so connectivity is refined and specificity gained. The skeletal muscle fibers in the newborn neuromuscular junction (NMJ) are polyinnervated but by the end of the competition, 2 weeks later, the NMJ are innervated by only one axon. This peripheral synapse has long been used as a convenient model for synapse development. In the last few years, we have studied transmitter release and the local involvement of the presynaptic muscarinic acetylcholine autoreceptors (mAChR), adenosine autoreceptors (AR) and trophic factor receptors (TFR, for neurotrophins and trophic cytokines) during the development of NMJ and in the adult. This review article brings together previously published data and proposes a molecular background for developmental axonal competition and loss. At the end of the first week postnatal, these receptors modulate transmitter release in the various nerve terminals on polyinnervated NMJ and contribute to axonal competition and synapse elimination.

  4. Presynaptic Membrane Receptors Modulate ACh Release, Axonal Competition and Synapse Elimination during Neuromuscular Junction Development

    Directory of Open Access Journals (Sweden)

    Josep Tomàs

    2017-05-01

    Full Text Available During the histogenesis of the nervous system a lush production of neurons, which establish an excessive number of synapses, is followed by a drop in both neurons and synaptic contacts as maturation proceeds. Hebbian competition between axons with different activities leads to the loss of roughly half of the neurons initially produced so connectivity is refined and specificity gained. The skeletal muscle fibers in the newborn neuromuscular junction (NMJ are polyinnervated but by the end of the competition, 2 weeks later, the NMJ are innervated by only one axon. This peripheral synapse has long been used as a convenient model for synapse development. In the last few years, we have studied transmitter release and the local involvement of the presynaptic muscarinic acetylcholine autoreceptors (mAChR, adenosine autoreceptors (AR and trophic factor receptors (TFR, for neurotrophins and trophic cytokines during the development of NMJ and in the adult. This review article brings together previously published data and proposes a molecular background for developmental axonal competition and loss. At the end of the first week postnatal, these receptors modulate transmitter release in the various nerve terminals on polyinnervated NMJ and contribute to axonal competition and synapse elimination.

  5. Neuromorphic atomic switch networks.

    Directory of Open Access Journals (Sweden)

    Audrius V Avizienis

    Full Text Available Efforts to emulate the formidable information processing capabilities of the brain through neuromorphic engineering have been bolstered by recent progress in the fabrication of nonlinear, nanoscale circuit elements that exhibit synapse-like operational characteristics. However, conventional fabrication techniques are unable to efficiently generate structures with the highly complex interconnectivity found in biological neuronal networks. Here we demonstrate the physical realization of a self-assembled neuromorphic device which implements basic concepts of systems neuroscience through a hardware-based platform comprised of over a billion interconnected atomic-switch inorganic synapses embedded in a complex network of silver nanowires. Observations of network activation and passive harmonic generation demonstrate a collective response to input stimulus in agreement with recent theoretical predictions. Further, emergent behaviors unique to the complex network of atomic switches and akin to brain function are observed, namely spatially distributed memory, recurrent dynamics and the activation of feedforward subnetworks. These devices display the functional characteristics required for implementing unconventional, biologically and neurally inspired computational methodologies in a synthetic experimental system.

  6. Transglial transmission at the dorsal root ganglion sandwich synapse: glial cell to postsynaptic neuron communication.

    Science.gov (United States)

    Rozanski, Gabriela M; Li, Qi; Stanley, Elise F

    2013-04-01

    The dorsal root ganglion (DRG) contains a subset of closely-apposed neuronal somata (NS) separated solely by a thin satellite glial cell (SGC) membrane septum to form an NS-glial cell-NS trimer. We recently reported that stimulation of one NS with an impulse train triggers a delayed, noisy and long-lasting response in its NS pair via a transglial signaling pathway that we term a 'sandwich synapse' (SS). Transmission could be unidirectional or bidirectional and facilitated in response to a second stimulus train. We have shown that in chick or rat SS the NS-to-SGC leg of the two-synapse pathway is purinergic via P2Y2 receptors but the second SGC-to-NS synapse mechanism remained unknown. A noisy evoked current in the target neuron, a reversal potential close to 0 mV, and insensitivity to calcium scavengers or G protein block favored an ionotropic postsynaptic receptor. Selective block by D-2-amino-5-phosphonopentanoate (AP5) implicated glutamatergic transmission via N-methyl-d-aspartate receptors. This agent also blocked NS responses evoked by puff of UTP, a P2Y2 agonist, directly onto the SGC cell, confirming its action at the second synapse of the SS transmission pathway. The N-methyl-d-aspartate receptor NR2B subunit was implicated by block of transmission with ifenprodil and by its immunocytochemical localization to the NS membrane, abutting the glial septum P2Y2 receptor. Isolated DRG cell clusters exhibited daisy-chain and branching NS-glial cell-NS contacts, suggestive of a network organization within the ganglion. The identification of the glial-to-neuron transmitter and receptor combination provides further support for transglial transmission and completes the DRG SS molecular transmission pathway. © 2013 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  7. Molecular determinants of magnesium-dependent synaptic plasticity at electrical synapses formed by connexin36

    Science.gov (United States)

    Palacios-Prado, Nicolás; Chapuis, Sandrine; Panjkovich, Alejandro; Fregeac, Julien; Nagy, James I.; Bukauskas, Feliksas F.

    2014-08-01

    Neuronal gap junction (GJ) channels composed of connexin36 (Cx36) play an important role in neuronal synchronization and network dynamics. Here we show that Cx36-containing electrical synapses between inhibitory neurons of the thalamic reticular nucleus are bidirectionally modulated by changes in intracellular free magnesium concentration ([Mg2+]i). Chimeragenesis demonstrates that the first extracellular loop of Cx36 contains a Mg2+-sensitive domain, and site-directed mutagenesis shows that the pore-lining residue D47 is critical in determining high Mg2+-sensitivity. Single-channel analysis of Mg2+-sensitive chimeras and mutants reveals that [Mg2+]i controls the strength of electrical coupling mostly via gating mechanisms. In addition, asymmetric transjunctional [Mg2+]i induces strong instantaneous rectification, providing a novel mechanism for electrical rectification in homotypic Cx36 GJs. We suggest that Mg2+-dependent synaptic plasticity of Cx36-containing electrical synapses could underlie neuronal circuit reconfiguration via changes in brain energy metabolism that affects neuronal levels of intracellular ATP and [Mg2+]i.

  8. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    Foster, Philip P

    2015-01-01

    It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  9. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

  10. The State of Synapses in Fragile X Syndrome

    OpenAIRE

    Pfeiffer, Brad E.; Huber, Kimberly M.

    2009-01-01

    Fragile X Syndrome is the most common inherited form of mental retardation and a leading genetic cause of autism. There is increasing evidence in both FXS and other forms of autism that alterations in synapse number, structure and function are associated and contribute to these prevalent diseases. FXS is caused by loss of function of the Fmr1 gene which encodes the RNA binding protein, FMRP. Therefore, FXS is a tractable model to understand synaptic dysfunction in cognitive disorders. FMRP is...

  11. LRRTM3 Regulates Excitatory Synapse Development through Alternative Splicing and Neurexin Binding

    Directory of Open Access Journals (Sweden)

    Ji Won Um

    2016-02-01

    Full Text Available The four members of the LRRTM family (LRRTM1-4 are postsynaptic adhesion molecules essential for excitatory synapse development. They have also been implicated in neuropsychiatric diseases. Here, we focus on LRRTM3, showing that two distinct LRRTM3 variants generated by alternative splicing regulate LRRTM3 interaction with PSD-95, but not its excitatory synapse-promoting activity. Overexpression of either LRRTM3 variant increased excitatory synapse density in dentate gyrus (DG granule neurons, whereas LRRTM3 knockdown decreased it. LRRTM3 also controlled activity-regulated AMPA receptor surface expression in an alternative splicing-dependent manner. Furthermore, Lrrtm3-knockout mice displayed specific alterations in excitatory synapse density, excitatory synaptic transmission and excitability in DG granule neurons but not in CA1 pyramidal neurons. Lastly, LRRTM3 required only specific splice variants of presynaptic neurexins for their synaptogenic activity. Collectively, our data highlight alternative splicing and differential presynaptic ligand utilization in the regulation of LRRTMs, revealing key regulatory mechanisms for excitatory synapse development.

  12. Conceptual Network Model From Sensory Neurons to Astrocytes of the Human Nervous System.

    Science.gov (United States)

    Yang, Yiqun; Yeo, Chai Kiat

    2015-07-01

    From a single-cell animal like paramecium to vertebrates like ape, the nervous system plays an important role in responding to the variations of the environment. Compared to animals, the nervous system in the human body possesses more intricate organization and utility. The nervous system anatomy has been understood progressively, yet the explanation at the cell level regarding complete information transmission is still lacking. Along the signal pathway toward the brain, an external stimulus first activates action potentials in the sensing neuron and these electric pulses transmit along the spinal nerve or cranial nerve to the neurons in the brain. Second, calcium elevation is triggered in the branch of astrocyte at the tripartite synapse. Third, the local calcium wave expands to the entire territory of the astrocyte. Finally, the calcium wave propagates to the neighboring astrocyte via gap junction channel. In our study, we integrate the existing mathematical model and biological experiments in each step of the signal transduction to establish a conceptual network model for the human nervous system. The network is composed of four layers and the communication protocols of each layer could be adapted to entities with different characterizations. We verify our simulation results against the available biological experiments and mathematical models and provide a test case of the integrated network. As the production of conscious episode in the human nervous system is still under intense research, our model serves as a useful tool to facilitate, complement and verify current and future study in human cognition.

  13. Synapse-specific astrocyte gating of amygdala-related behavior.

    Science.gov (United States)

    Martin-Fernandez, Mario; Jamison, Stephanie; Robin, Laurie M; Zhao, Zhe; Martin, Eduardo D; Aguilar, Juan; Benneyworth, Michael A; Marsicano, Giovanni; Araque, Alfonso

    2017-11-01

    The amygdala plays key roles in fear and anxiety. Studies of the amygdala have largely focused on neuronal function and connectivity. Astrocytes functionally interact with neurons, but their role in the amygdala remains largely unknown. We show that astrocytes in the medial subdivision of the central amygdala (CeM) determine the synaptic and behavioral outputs of amygdala circuits. To investigate the role of astrocytes in amygdala-related behavior and identify the underlying synaptic mechanisms, we used exogenous or endogenous signaling to selectively activate CeM astrocytes. Astrocytes depressed excitatory synapses from basolateral amygdala via A 1 adenosine receptor activation and enhanced inhibitory synapses from the lateral subdivision of the central amygdala via A 2A receptor activation. Furthermore, astrocytic activation decreased the firing rate of CeM neurons and reduced fear expression in a fear-conditioning paradigm. Therefore, we conclude that astrocyte activity determines fear responses by selectively regulating specific synapses, which indicates that animal behavior results from the coordinated activity of neurons and astrocytes.

  14. Spatially restricted actin-regulatory signaling contributes to synapse morphology

    Science.gov (United States)

    Nicholson, Daniel A.; Cahill, Michael E.; Tulisiak, Christopher T.; Geinisman, Yuri; Penzes, Peter

    2012-01-01

    The actin cytoskeleton in dendritic spines is organized into microdomains, but how signaling molecules that regulate actin are spatially governed is incompletely understood. Here we examine how the localization of the RacGEF kalirin-7, a well-characterized regulator of actin in spines, varies as a function of postsynaptic density (PSD) area and spine volume. Using serial section electron microscopy (EM), we find that extrasynaptic, but not synaptic, expression of kalirin-7 varies directly with synapse size and spine volume. Moreover, we find that overall expression levels of kalirin-7 differ in spines bearing perforated and non-perforated synapses, due primarily to extrasynaptic pools of kalirin-7 expression in the former. Overall, our findings indicate that kalirin-7 is differentially compartmentalized in spines as a function of both synapse morphology and spine size. PMID:22458534

  15. Dopamine synapse is a neuroligin-2–mediated contact between dopaminergic presynaptic and GABAergic postsynaptic structures

    Science.gov (United States)

    Uchigashima, Motokazu; Ohtsuka, Toshihisa; Kobayashi, Kazuto; Watanabe, Masahiko

    2016-01-01

    Midbrain dopamine neurons project densely to the striatum and form so-called dopamine synapses on medium spiny neurons (MSNs), principal neurons in the striatum. Because dopamine receptors are widely expressed away from dopamine synapses, it remains unclear how dopamine synapses are involved in dopaminergic transmission. Here we demonstrate that dopamine synapses are contacts formed between dopaminergic presynaptic and GABAergic postsynaptic structures. The presynaptic structure expressed tyrosine hydroxylase, vesicular monoamine transporter-2, and plasmalemmal dopamine transporter, which are essential for dopamine synthesis, vesicular filling, and recycling, but was below the detection threshold for molecules involving GABA synthesis and vesicular filling or for GABA itself. In contrast, the postsynaptic structure of dopamine synapses expressed GABAergic molecules, including postsynaptic adhesion molecule neuroligin-2, postsynaptic scaffolding molecule gephyrin, and GABAA receptor α1, without any specific clustering of dopamine receptors. Of these, neuroligin-2 promoted presynaptic differentiation in axons of midbrain dopamine neurons and striatal GABAergic neurons in culture. After neuroligin-2 knockdown in the striatum, a significant decrease of dopamine synapses coupled with a reciprocal increase of GABAergic synapses was observed on MSN dendrites. This finding suggests that neuroligin-2 controls striatal synapse formation by giving competitive advantage to heterologous dopamine synapses over conventional GABAergic synapses. Considering that MSN dendrites are preferential targets of dopamine synapses and express high levels of dopamine receptors, dopamine synapse formation may serve to increase the specificity and potency of dopaminergic modulation of striatal outputs by anchoring dopamine release sites to dopamine-sensing targets. PMID:27035941

  16. Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression

    Directory of Open Access Journals (Sweden)

    Paul eMiller

    2013-05-01

    Full Text Available Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be destabilized with increasing input. This leads to an itinerancy, where with either repeated transient stimuli, or increasing duration of a single stimulus, the network activity advances through sequences of attractor states. We find that the resulting network state, which persists beyond stimulus offset, can encode the number of stimuli presented via a distributed representation of neural activity with non-monotonic tuning curves for most neurons. Increased duration of a single stimulus is encoded via different distributed representations, so unlike an integrator, the network distinguishes separate successive presentations of a short stimulus from a single presentation of a longer stimulus with equal total duration. Moreover, different amplitudes of stimulus cause new, distinct activity patterns, such that changes in stimulus number, duration and amplitude can be distinguished from each other. These properties of the network depend on dynamic depressing synapses, as they disappear if synapses are static. Thus short-term synaptic depression allows a network to store separately the different dynamic properties of a spatially constant stimulus.

  17. Face classification using electronic synapses

    Science.gov (United States)

    Yao, Peng; Wu, Huaqiang; Gao, Bin; Eryilmaz, Sukru Burc; Huang, Xueyao; Zhang, Wenqiang; Zhang, Qingtian; Deng, Ning; Shi, Luping; Wong, H.-S. Philip; Qian, He

    2017-05-01

    Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.

  18. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  19. Role of the MAGUK protein family in synapse formation and function.

    Science.gov (United States)

    Oliva, Carlos; Escobedo, Pía; Astorga, César; Molina, Claudia; Sierralta, Jimena

    2012-01-01

    Synaptic function is crucially dependent on the spatial organization of the presynaptic and postsynaptic apparatuses and the juxtaposition of both membrane compartments. This precise arrangement is achieved by a protein network at the submembrane region of each cell that is built around scaffold proteins. The membrane-associated guanylate kinase (MAGUK) family of proteins is a widely expressed and well-conserved group of proteins that plays an essential role in the formation and regulation of this scaffolding. Here, we review general features of this protein family, focusing on the discs large and calcium/calmodulin-dependent serine protein kinase subfamilies of MAGUKs in the formation, function, and plasticity of synapses. Copyright © 2011 Wiley Periodicals, Inc.

  20. Cell Adhesion, the Backbone of the Synapse: “Vertebrate” and “Invertebrate” Perspectives

    OpenAIRE

    Giagtzoglou, Nikolaos; Ly, Cindy V.; Bellen, Hugo J.

    2009-01-01

    Synapses are asymmetric intercellular junctions that mediate neuronal communication. The number, type, and connectivity patterns of synapses determine the formation, maintenance, and function of neural circuitries. The complexity and specificity of synaptogenesis relies upon modulation of adhesive properties, which regulate contact initiation, synapse formation, maturation, and functional plasticity. Disruption of adhesion may result in structural and functional imbalance that may lead to neu...

  1. Estradiol pretreatment ameliorates impaired synaptic plasticity at synapses of insulted CA1 neurons after transient global ischemia

    Science.gov (United States)

    Takeuchi, Koichi; Yang, Yupeng; Takayasu, Yukihiro; Gertner, Michael; Hwang, Jee-Yeon; Aromolaran, Kelly; Bennett, Michael V.L.; Zukin, R. Suzanne

    2015-01-01

    Global ischemia in humans or induced experimentally in animals causes selective and delayed neuronal death in pyramidal neurons of the hippocampal CA1. The ovarian hormone estradiol administered before or immediately after insult affords histological protection in experimental models of focal and global ischemia and ameliorates the cognitive deficits associated with ischemic cell death. However, the impact of estradiol on the functional integrity of Schaffer collateral to CA1 (Sch-CA1) pyramidal cell synapses following global ischemia is not clear. Here we show that long term estradiol treatment initiated 14 days prior to global ischemia in ovariectomized female rats acts via the IGF-1 receptor to protect the functional integrity of CA1 neurons. Global ischemia impairs basal synaptic transmission, assessed by the input/output relation at Sch-CA1 synapses, and NMDA receptor (NMDAR)-dependent long term potentiation (LTP), assessed at 3 days after surgery. Presynaptic function, assessed by fiber volley and paired pulse facilitation, is unchanged. To our knowledge, our results are the first to demonstrate that estradiol at near physiological concentrations enhances basal excitatory synaptic transmission and ameliorates deficits in LTP at synapses onto CA1 neurons in a clinically-relevant model of global ischemia. Estradiol-induced rescue of LTP requires the IGF-1 receptor, but not the classical estrogen receptors (ER)-α or β. These findings support a model whereby estradiol acts via the IGF-1 receptor to maintain the functional integrity of hippocampal CA1 synapses in the face of global ischemia. PMID:25463028

  2. Chaotic, informational and synchronous behaviour of multiplex networks

    Science.gov (United States)

    Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. De Souza

    2016-03-01

    The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses.

  3. Functional hallmarks of GABAergic synapse maturation and the diverse roles of neurotrophins

    Directory of Open Access Journals (Sweden)

    Rosemarie eGrantyn

    2011-07-01

    Full Text Available Functional impairment of the adult brain can result from deficits in the ontogeny of GABAergic synaptic transmission. Gene defects underlying autism spectrum disorders, Rett’s syndrome or some forms of epilepsy, but also a diverse set of syndromes accompanying perinatal trauma, hormonal imbalances, intake of sleep-inducing or mood-improving drugs or, quite common, alcohol intake during pregnancy can alter GABA signaling early in life. The search for therapeutically relevant endogenous molecules or exogenous compounds able to alleviate the consequences of dysfunction of GABAergic transmission in the embryonic or postnatal brain requires a clear understanding of its site- and state-dependent development. At the level of single synapses, it is necessary to discriminate between presynaptic and postsynaptic alterations, and to define parameters that can be regarded as both suitable and accessible for the quantification of developmental changes. Here we focus on the performance of GABAergic synapses in two brain structures, the hippocampus and the superior colliculus, describe some novel aspects of neurotrophin effects during the development of GABAergic synaptic transmission and examine the applicability of the following rules: 1 Synaptic transmission starts with GABA, 2 Nascent/immature GABAergic synapses operate in a ballistic mode (multivesicular release, 3 Immature synaptic terminals release vesicles with higher probability than mature synapses, 4 Immature GABAergic synapses are prone to paired pulse and tetanic depression, 5 Synapse maturation is characterized by an increasing dominance of synchronous over asynchronous release, 6 In immature neurons GABA acts as a depolarizing transmitter, 7 Synapse maturation implies IPSC shortening due to an increase in alpha1 subunit expression, 8 Extrasynaptic (tonic conductances can inhibit the development of synaptic (phasic GABA actions.

  4. A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

    Directory of Open Access Journals (Sweden)

    Zhekang Dong

    2014-01-01

    Full Text Available In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

  5. A novel memristive multilayer feedforward small-world neural network with its applications in PID control.

    Science.gov (United States)

    Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan; Li, Hai

    2014-01-01

    In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

  6. Orchestrating cytoskeleton and intracellular vesicle traffic to build functional immunological synapses.

    Science.gov (United States)

    Soares, Helena; Lasserre, Rémi; Alcover, Andrés

    2013-11-01

    Immunological synapses are specialized cell-cell contacts formed between T lymphocytes and antigen-presenting cells. They are induced upon antigen recognition and are crucial for T-cell activation and effector functions. The generation and function of immunological synapses depend on an active T-cell polarization process, which results from a finely orchestrated crosstalk between the antigen receptor signal transduction machinery, the actin and microtubule cytoskeletons, and controlled vesicle traffic. Although we understand how some of these particular events are regulated, we still lack knowledge on how these multiple cellular elements are harmonized to ensure appropriate T-cell responses. We discuss here our view on how T-cell receptor signal transduction initially commands cytoskeletal and vesicle traffic polarization, which in turn sets the immunological synapse molecular design that regulates T-cell activation. We also discuss how the human immunodeficiency virus (HIV-1) hijacks some of these processes impairing immunological synapse generation and function. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  7. Memory-Relevant Mushroom Body Output Synapses Are Cholinergic.

    Science.gov (United States)

    Barnstedt, Oliver; Owald, David; Felsenberg, Johannes; Brain, Ruth; Moszynski, John-Paul; Talbot, Clifford B; Perrat, Paola N; Waddell, Scott

    2016-03-16

    Memories are stored in the fan-out fan-in neural architectures of the mammalian cerebellum and hippocampus and the insect mushroom bodies. However, whereas key plasticity occurs at glutamatergic synapses in mammals, the neurochemistry of the memory-storing mushroom body Kenyon cell output synapses is unknown. Here we demonstrate a role for acetylcholine (ACh) in Drosophila. Kenyon cells express the ACh-processing proteins ChAT and VAChT, and reducing their expression impairs learned olfactory-driven behavior. Local ACh application, or direct Kenyon cell activation, evokes activity in mushroom body output neurons (MBONs). MBON activation depends on VAChT expression in Kenyon cells and is blocked by ACh receptor antagonism. Furthermore, reducing nicotinic ACh receptor subunit expression in MBONs compromises odor-evoked activation and redirects odor-driven behavior. Lastly, peptidergic corelease enhances ACh-evoked responses in MBONs, suggesting an interaction between the fast- and slow-acting transmitters. Therefore, olfactory memories in Drosophila are likely stored as plasticity of cholinergic synapses. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Dynamic mobility of functional GABAA receptors at inhibitory synapses.

    Science.gov (United States)

    Thomas, Philip; Mortensen, Martin; Hosie, Alastair M; Smart, Trevor G

    2005-07-01

    Importing functional GABAA receptors into synapses is fundamental for establishing and maintaining inhibitory transmission and for controlling neuronal excitability. By introducing a binding site for an irreversible inhibitor into the GABAA receptor alpha1 subunit channel lining region that can be accessed only when the receptor is activated, we have determined the dynamics of receptor mobility between synaptic and extrasynaptic locations in hippocampal pyramidal neurons. We demonstrate that the cell surface GABAA receptor population shows no fast recovery after irreversible inhibition. In contrast, after selective inhibition, the synaptic receptor population rapidly recovers by the import of new functional entities within minutes. The trafficking pathways that promote rapid importation of synaptic receptors do not involve insertion from intracellular pools, but reflect receptor diffusion within the plane of the membrane. This process offers the synapse a rapid mechanism to replenish functional GABAA receptors at inhibitory synapses and a means to control synaptic efficacy.

  9. An NMDA Receptor-Dependent Mechanism Underlies Inhibitory Synapse Development

    Directory of Open Access Journals (Sweden)

    Xinglong Gu

    2016-01-01

    Full Text Available In the mammalian brain, GABAergic synaptic transmission provides inhibitory balance to glutamatergic excitatory drive and controls neuronal output. The molecular mechanisms underlying the development of GABAergic synapses remain largely unclear. Here, we report that NMDA-type ionotropic glutamate receptors (NMDARs in individual immature neurons are the upstream signaling molecules essential for GABAergic synapse development, which requires signaling via Calmodulin binding motif in the C0 domain of the NMDAR GluN1 subunit. Interestingly, in neurons lacking NMDARs, whereas GABAergic synaptic transmission is strongly reduced, the tonic inhibition mediated by extrasynaptic GABAA receptors is increased, suggesting a compensatory mechanism for the lack of synaptic inhibition. These results demonstrate a crucial role for NMDARs in specifying the development of inhibitory synapses, and suggest an important mechanism for controlling the establishment of the balance between synaptic excitation and inhibition in the developing brain.

  10. Spine Calcium Transients Induced by Synaptically-Evoked Action Potentials Can Predict Synapse Location and Establish Synaptic Democracy

    Science.gov (United States)

    Meredith, Rhiannon M.; van Ooyen, Arjen

    2012-01-01

    CA1 pyramidal neurons receive hundreds of synaptic inputs at different distances from the soma. Distance-dependent synaptic scaling enables distal and proximal synapses to influence the somatic membrane equally, a phenomenon called “synaptic democracy”. How this is established is unclear. The backpropagating action potential (BAP) is hypothesised to provide distance-dependent information to synapses, allowing synaptic strengths to scale accordingly. Experimental measurements show that a BAP evoked by current injection at the soma causes calcium currents in the apical shaft whose amplitudes decay with distance from the soma. However, in vivo action potentials are not induced by somatic current injection but by synaptic inputs along the dendrites, which creates a different excitable state of the dendrites. Due to technical limitations, it is not possible to study experimentally whether distance information can also be provided by synaptically-evoked BAPs. Therefore we adapted a realistic morphological and electrophysiological model to measure BAP-induced voltage and calcium signals in spines after Schaffer collateral synapse stimulation. We show that peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. Peak calcium levels also predicted the attenuation of the EPSP across the dendritic tree. Furthermore, we show that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value. We conclude that information derived from synaptically-generated BAPs can indicate synapse location and can subsequently be utilised to implement a synaptic democracy. PMID:22719238

  11. Capacity analysis in multi-state synaptic models: a retrieval probability perspective.

    Science.gov (United States)

    Huang, Yibi; Amit, Yali

    2011-06-01

    We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485-493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599-611, 2005; Leibold and Kempter, Cereb Cortex 18:67-77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.

  12. Kalirin, a key player in synapse formation, is implicated in human diseases.

    Science.gov (United States)

    Mandela, Prashant; Ma, Xin-Ming

    2012-01-01

    Synapse formation is considered to be crucial for learning and memory. Understanding the underlying molecular mechanisms of synapse formation is a key to understanding learning and memory. Kalirin-7, a major isoform of Kalirin in adult rodent brain, is an essential component of mature excitatory synapses. Kalirin-7 interacts with multiple PDZ-domain-containing proteins including PSD95, spinophilin, and GluR1 through its PDZ-binding motif. In cultured hippocampal/cortical neurons, overexpression of Kalirin-7 increases spine density and spine size whereas reduction of endogenous Kalirin-7 expression decreases synapse number, and spine density. In Kalirin-7 knockout mice, spine length, synapse number, and postsynaptic density (PSD) size are decreased in hippocampal CA1 pyramidal neurons; these morphological alterations are accompanied by a deficiency in long-term potentiation (LTP) and a decreased spontaneous excitatory postsynaptic current (sEPSC) frequency. Human Kalirin-7, also known as Duo or Huntingtin-associated protein-interacting protein (HAPIP), is equivalent to rat Kalirin-7. Recent studies show that Kalirin is relevant to many human diseases such as Huntington's Disease, Alzheimer's Disease, ischemic stroke, schizophrenia, depression, and cocaine addiction. This paper summarizes our recent understanding of Kalirin function.

  13. Modeling the citation network by network cosmology.

    Science.gov (United States)

    Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

    2015-01-01

    Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

  14. Building blocks of temporal filters in retinal synapses.

    Directory of Open Access Journals (Sweden)

    Bongsoo Suh

    2014-10-01

    Full Text Available Sensory systems must be able to extract features of a stimulus to detect and represent properties of the world. Because sensory signals are constantly changing, a critical aspect of this transformation relates to the timing of signals and the ability to filter those signals to select dynamic properties, such as visual motion. At first assessment, one might think that the primary biophysical properties that construct a temporal filter would be dynamic mechanisms such as molecular concentration or membrane electrical properties. However, in the current issue of PLOS Biology, Baden et al. identify a mechanism of temporal filtering in the zebrafish and goldfish retina that is not dynamic but is in fact a structural building block-the physical size of a synapse itself. The authors observe that small, bipolar cell synaptic terminals are fast and highly adaptive, whereas large ones are slower and adapt less. Using a computational model, they conclude that the volume of the synaptic terminal influences the calcium concentration and the number of available vesicles. These results indicate that the size of the presynaptic terminal is an independent control for the dynamics of a synapse and may reveal aspects of synaptic function that can be inferred from anatomical structure.

  15. Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system

    Science.gov (United States)

    Sung, Changhyuck; Lim, Seokjae; Kim, Hyungjun; Kim, Taesu; Moon, Kibong; Song, Jeonghwan; Kim, Jae-Joon; Hwang, Hyunsang

    2018-03-01

    To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.

  16. Current-mode subthreshold MOS implementation of the Herault-Jutten autoadaptive network

    Science.gov (United States)

    Cohen, Marc H.; Andreou, Andreas G.

    1992-05-01

    The translinear circuits in subthreshold MOS technology and current-mode design techniques for the implementation of neuromorphic analog network processing are investigated. The architecture, also known as the Herault-Jutten network, performs an independent component analysis and is essentially a continuous-time recursive linear adaptive filter. Analog I/O interface, weight coefficients, and adaptation blocks are all integrated on the chip. A small network with six neurons and 30 synapses was fabricated in a 2-microns n-well double-polysilicon, double-metal CMOS process. Circuit designs at the transistor level yield area-efficient implementations for neurons, synapses, and the adaptation blocks. The design methodology and constraints as well as test results from the fabricated chips are discussed.

  17. Modelling computer networks

    International Nuclear Information System (INIS)

    Max, G

    2011-01-01

    Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

  18. Super Resolution Imaging of Genetically Labeled Synapses in Drosophila Brain Tissue.

    Science.gov (United States)

    Spühler, Isabelle A; Conley, Gaurasundar M; Scheffold, Frank; Sprecher, Simon G

    2016-01-01

    Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation.

  19. Sleep: The hebbian reinforcement of the local inhibitory synapses.

    Science.gov (United States)

    Touzet, Claude

    2015-09-01

    Sleep is ubiquitous among the animal realm, and represents about 30% of our lives. Despite numerous efforts, the reason behind our need for sleep is still unknown. The Theory of neuronal Cognition (TnC) proposes that sleep is the period of time during which the local inhibitory synapses (in particular the cortical ones) are replenished. Indeed, as long as the active brain stays awake, hebbian learning guarantees that efficient inhibitory synapses lose their efficiency – just because they are efficient at avoiding the activation of the targeted neurons. Since hebbian learning is the only known mechanism of synapse modification, it follows that to replenish the inhibitory synapses' efficiency, source and targeted neurons must be activated together. This is achieved by a local depolarization that may travel (wave). The period of time during which such slow waves are experienced has been named the "slow-wave sleep" (SWS). It is cut into several pieces by shorter periods of paradoxical sleep (REM) which activity resembles that of the awake state. Indeed, SWS – because it only allows local neural activation – decreases the excitatory long distance connections strength. To avoid losing the associations built during the awake state, these long distance activations are played again during the REM sleep. REM and SWS sleeps act together to guarantee that when the subject awakes again, his inhibitory synaptic efficiency is restored and his (excitatory) long distance associations are still there. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Activity-dependent control of NMDA receptor subunit composition at hippocampal mossy fibre synapses.

    Science.gov (United States)

    Carta, Mario; Srikumar, Bettadapura N; Gorlewicz, Adam; Rebola, Nelson; Mulle, Christophe

    2018-02-15

    CA3 pyramidal cells display input-specific differences in the subunit composition of synaptic NMDA receptors (NMDARs). Although at low density, GluN2B contributes significantly to NMDAR-mediated EPSCs at mossy fibre synapses. Long-term potentiation (LTP) of NMDARs triggers a modification in the subunit composition of synaptic NMDARs by insertion of GluN2B. GluN2B subunits are essential for the expression of LTP of NMDARs at mossy fibre synapses. Single neurons express NMDA receptors (NMDARs) with distinct subunit composition and biophysical properties that can be segregated in an input-specific manner. The dynamic control of the heterogeneous distribution of synaptic NMDARs is crucial to control input-dependent synaptic integration and plasticity. In hippocampal CA3 pyramidal cells from mice of both sexes, we found that mossy fibre (MF) synapses display a markedly lower proportion of GluN2B-containing NMDARs than associative/commissural synapses. The mechanism involved in such heterogeneous distribution of GluN2B subunits is not known. Here we show that long-term potentiation (LTP) of NMDARs, which is selectively expressed at MF-CA3 pyramidal cell synapses, triggers a modification in the subunit composition of synaptic NMDARs by insertion of GluN2B. This activity-dependent recruitment of GluN2B at mature MF-CA3 pyramidal cell synapses contrasts with the removal of GluN2B subunits at other glutamatergic synapses during development and in response to activity. Furthermore, although expressed at low levels, GluN2B is necessary for the expression of LTP of NMDARs at MF-CA3 pyramidal cell synapses. Altogether, we reveal a previously unknown activity-dependent regulation and function of GluN2B subunits that may contribute to the heterogeneous plasticity induction rules in CA3 pyramidal cells. © 2017 Centre Nationnal de la Recherche Scientifique. The Journal of Physiology © 2017 The Physiological Society.

  1. Eph receptors and ephrins in neuron-astrocyte communication at synapses.

    Science.gov (United States)

    Murai, Keith K; Pasquale, Elena B

    2011-11-01

    Neuron-glia communication is essential for regulating the properties of synaptic connections in the brain. Astrocytes, in particular, play a critical and complex role in synapse development, maintenance, and plasticity. Likewise, neurons reciprocally influence astrocyte physiology. However, the molecular signaling events that enable astrocytes and neurons to effectively communicate with each other are only partially defined. Recent findings have revealed that Eph receptor tyrosine kinases and ephrins play an important role in contact-dependent neuron-glia communication at synapses. Upon binding, these two families of cell surface-associated proteins trigger bidirectional signaling events that regulate the structural and physiological properties of both neurons and astrocytes. This review will focus on the emerging role of Eph receptors and ephrins in neuron-astrocyte interaction at synapses and discuss implications for synaptic plasticity, behavior, and disease. Copyright © 2011 Wiley-Liss, Inc.

  2. Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation.

    Directory of Open Access Journals (Sweden)

    Sergio Verduzco-Flores

    2009-08-01

    Full Text Available Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1 persistent fixed-frequency elevated rates above baseline, 2 elevated rates that decay throughout the tasks memory period, 3 rates that accelerate throughout the delay, and 4 patterns of inhibited firing (below baseline analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

  3. Self-organized criticality in a network of interacting neurons

    NARCIS (Netherlands)

    Cowan, J.D.; Neuman, J.; Kiewiet, B.; van Drongelen, W.

    2013-01-01

    This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway.

  4. Constancy and variability in cortical structure. A study on synapses and dendritic spines in hedgehog and monkey.

    Science.gov (United States)

    Schüz, A; Demianenko, G P

    1995-01-01

    Synapses and dendritic spines were investigated in the parietal cortex of the hedgehog (Erinaceus europaeus) and the monkey (Macaca mulatta). There was no significant difference in the density of synapses between the two species (14 synapses/100 microns2 in the hedgehog, 15/100 microns2 in the monkey), neither in the size of the synaptic junctions, in the proportion of Type I and Type II synapses (8-10% were of Type II in the hedgehog, 10-14% in the monkey) nor in the proportion of perforated synapses (8% in the hedgehog, 5% in the monkey). The only striking difference at the electron microscopic level concerned the frequency of synapses in which the postsynaptic profile was deeply indented into the presynaptic terminal. Such synapses were 10 times more frequent in the monkey. Dendritic spines were investigated in Golgi-preparations. The density of spines along dendrites was similar in both species. The results are discussed with regard to connectivity in the cortex of small and large brains.

  5. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of energy cost-driven small-world network disorder as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement produces a reconfiguration of brain networks into greater small-worldness. Creation of synaptic connections in a macro-network, and, at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF. The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  6. Genetic targeting of NRXN2 in mice unveils role in excitatory cortical synapse function and social behaviors

    Directory of Open Access Journals (Sweden)

    Gesche eBorn

    2015-02-01

    Full Text Available Human genetics has identified rare copy number variations and deleterious mutations for all neurexin genes (NRXN1-3 in patients with neurodevelopmental diseases, and electrophysiological recordings in animal brains have shown that Nrxns are important for synaptic transmission. While several mouse models for Nrxn1α inactivation have previously been studied for behavioral changes, very little information is available for other variants. Here, we validate that mice lacking Nrxn2α exhibit behavioral abnormalities, characterized by social interaction deficits and increased anxiety-like behavior, which partially overlap, partially differ from Nrxn1α mutant behaviors. Using patch-clamp recordings in Nrxn2α knockout brains, we observe reduced spontaneous transmitter release at excitatory synapses in the neocortex. We also analyse at this cellular level a novel NRXN2 mouse model that carries a combined deletion of Nrxn2α and Nrxn2β. Electrophysiological analysis of this Nrxn2-mutant mouse shows surprisingly similar defects of excitatory release to Nrxn2α, indicating that the β-variant of Nrxn2 has no strong function in basic transmission at these synapses. Inhibitory transmission as well as synapse densities and ultrastructure remain unchanged in the neocortex of both models. Furthermore, at Nrxn2α and Nrxn2-mutant excitatory synapses we find an altered facilitation and N-methyl-D-aspartate receptor (NMDAR function because NMDAR-dependent decay time and NMDAR-mediated responses are reduced. As Nrxn can indirectly be linked to NMDAR via neuroligin and PSD-95, the trans-synaptic nature of this complex may help to explain occurrence of presynaptic and postsynaptic effects. Since excitatory/inhibitory imbalances and impairment of NMDAR function are alledged to have a role in autism and schizophrenia, our results support the idea of a related pathomechanism in these disorders.

  7. GABAergic activities control spike timing- and frequency-dependent long-term depression at hippocampal excitatory synapses

    Directory of Open Access Journals (Sweden)

    Makoto Nishiyama

    2010-06-01

    Full Text Available GABAergic interneuronal network activities in the hippocampus control a variety of neural functions, including learning and memory, by regulating θ and γ oscillations. How these GABAergic activities at pre- and post-synaptic sites of hippocampal CA1 pyramidal cells differentially contribute to synaptic function and plasticity during their repetitive pre- and post-synaptic spiking at θ and γ oscillations is largely unknown. We show here that activities mediated by postsynaptic GABAARs and presynaptic GABABRs determine, respectively, the spike timing- and frequency-dependence of activity-induced synaptic modifications at Schaffer collateral-CA1 excitatory synapses. We demonstrate that both feedforward and feedback GABAAR-mediated inhibition in the postsynaptic cell controls the spike timing-dependent long-term depression of excitatory inputs (“e-LTD” at the θ frequency. We also show that feedback postsynaptic inhibition specifically causes e-LTD of inputs that induce small postsynaptic currents (<70 pA with LTP timing, thus enforcing the requirement of cooperativity for induction of long-term potentiation at excitatory inputs (“e-LTP”. Furthermore, under spike-timing protocols that induce e-LTP and e-LTD at excitatory synapses, we observed parallel induction of LTP and LTD at inhibitory inputs (“i-LTP” and “i-LTD” to the same postsynaptic cells. Finally, we show that presynaptic GABABR-mediated inhibition plays a major role in the induction of frequency-dependent e-LTD at α and β frequencies. These observations demonstrate the critical influence of GABAergic interneuronal network activities in regulating the spike timing and frequency dependences of long-term synaptic modifications in the hippocampus.

  8. Changes in rat hippocampal CA1 synapses following imipramine treatment

    DEFF Research Database (Denmark)

    Chen, Fenghua; Madsen, Torsten M; Wegener, Gregers

    2008-01-01

    Neuronal plasticity in hippocampus is hypothesized to play an important role in both the pathophysiology of depressive disorders and the treatment. In this study, we investigated the consequences of imipramine treatment on neuroplasticity (including neurogenesis, synaptogenesis, and remodelling...... and number of neurons of hippocampal subregions following imipramine treatment were found. However, the number and percentage of CA1 asymmetric spine synapses increased significantly and, conversely, the percentage of asymmetric shaft synapses significantly decreased in the imipramine treated group. Our...

  9. Fear extinction causes target-specific remodeling of perisomatic inhibitory synapses

    Science.gov (United States)

    Trouche, Stéphanie; Sasaki, Jennifer M.; Tu, Tiffany; Reijmers, Leon G.

    2013-01-01

    SUMMARY A more complete understanding of how fear extinction alters neuronal activity and connectivity within fear circuits may aid in the development of strategies to treat human fear disorders. Using a c-fos based transgenic mouse, we found that contextual fear extinction silenced basal amygdala (BA) excitatory neurons that had been previously activated during fear conditioning. We hypothesized that the silencing of BA fear neurons was caused by an action of extinction on BA inhibitory synapses. In support of this hypothesis, we found extinction-induced target-specific remodeling of BA perisomatic inhibitory synapses originating from parvalbumin and cholecystokinin-positive interneurons. Interestingly, the predicted changes in the balance of perisomatic inhibition matched the silent and active states of the target BA fear neurons. These observations suggest that target-specific changes in perisomatic inhibitory synapses represent a mechanism through which experience can sculpt the activation patterns within a neural circuit. PMID:24183705

  10. NeuroD2 regulates the development of hippocampal mossy fiber synapses

    Directory of Open Access Journals (Sweden)

    Wilke Scott A

    2012-02-01

    Full Text Available Abstract Background The assembly of neural circuits requires the concerted action of both genetically determined and activity-dependent mechanisms. Calcium-regulated transcription may link these processes, but the influence of specific transcription factors on the differentiation of synapse-specific properties is poorly understood. Here we characterize the influence of NeuroD2, a calcium-dependent transcription factor, in regulating the structural and functional maturation of the hippocampal mossy fiber (MF synapse. Results Using NeuroD2 null mice and in vivo lentivirus-mediated gene knockdown, we demonstrate a critical role for NeuroD2 in the formation of CA3 dendritic spines receiving MF inputs. We also use electrophysiological recordings from CA3 neurons while stimulating MF axons to show that NeuroD2 regulates the differentiation of functional properties at the MF synapse. Finally, we find that NeuroD2 regulates PSD95 expression in hippocampal neurons and that PSD95 loss of function in vivo reproduces CA3 neuron spine defects observed in NeuroD2 null mice. Conclusion These experiments identify NeuroD2 as a key transcription factor that regulates the structural and functional differentiation of MF synapses in vivo.

  11. Super resolution imaging of genetically labelled synapses in Drosophila brain tissue

    Directory of Open Access Journals (Sweden)

    Isabelle Ayumi Spühler

    2016-05-01

    Full Text Available Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labelled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation

  12. Non-linear feedback neural networks VLSI implementations and applications

    CERN Document Server

    Ansari, Mohd Samar

    2014-01-01

    This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

  13. Dynamic Observation of Brain-Like Learning in a Ferroelectric Synapse Device

    Science.gov (United States)

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito; Fujii, Eiji; Tsujimura, Ayumu

    2013-04-01

    A brain-like learning function was implemented in an electronic synapse device using a ferroelectric-gate field effect transistor (FeFET). The FeFET was a bottom-gate type FET with a ZnO channel and a ferroelectric Pb(Zr,Ti)O3 (PZT) gate insulator. The synaptic weight, which is represented by the channel conductance of the FeFET, is updated by applying a gate voltage through a change in the ferroelectric polarization in the PZT. A learning function based on the symmetric spike-timing dependent synaptic plasticity was implemented in the synapse device using the multilevel weight update by applying a pulse gate voltage. The dynamic weighting and learning behavior in the synapse device was observed as a change in the membrane potential in a spiking neuron circuit.

  14. Neurobeachin regulates neurotransmitter receptor trafficking to synapses

    NARCIS (Netherlands)

    Nair, R.; Lauks, J.; Jung, S; Cooke, N.E.; de Wit, H.; Brose, N.; Kilimann, M.W.; Verhage, M.; Rhee, J.

    2013-01-01

    The surface density of neurotransmitter receptors at synapses is a key determinant of synaptic efficacy. Synaptic receptor accumulation is regulated by the transport, postsynaptic anchoring, and turnover of receptors, involving multiple trafficking, sorting, motor, and scaffold proteins. We found

  15. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  16. Fear extinction causes target-specific remodeling of perisomatic inhibitory synapses.

    Science.gov (United States)

    Trouche, Stéphanie; Sasaki, Jennifer M; Tu, Tiffany; Reijmers, Leon G

    2013-11-20

    A more complete understanding of how fear extinction alters neuronal activity and connectivity within fear circuits may aid in the development of strategies to treat human fear disorders. Using a c-fos-based transgenic mouse, we found that contextual fear extinction silenced basal amygdala (BA) excitatory neurons that had been previously activated during fear conditioning. We hypothesized that the silencing of BA fear neurons was caused by an action of extinction on BA inhibitory synapses. In support of this hypothesis, we found extinction-induced target-specific remodeling of BA perisomatic inhibitory synapses originating from parvalbumin and cholecystokinin-positive interneurons. Interestingly, the predicted changes in the balance of perisomatic inhibition matched the silent and active states of the target BA fear neurons. These observations suggest that target-specific changes in perisomatic inhibitory synapses represent a mechanism through which experience can sculpt the activation patterns within a neural circuit. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Optimal and Local Connectivity Between Neuron and Synapse Array in the Quantum Dot/Silicon Brain

    Science.gov (United States)

    Duong, Tuan A.; Assad, Christopher; Thakoor, Anikumar P.

    2010-01-01

    This innovation is used to connect between synapse and neuron arrays using nanowire in quantum dot and metal in CMOS (complementary metal oxide semiconductor) technology to enable the density of a brain-like connection in hardware. The hardware implementation combines three technologies: 1. Quantum dot and nanowire-based compact synaptic cell (50x50 sq nm) with inherently low parasitic capacitance (hence, low dynamic power approx.l0(exp -11) watts/synapse), 2. Neuron and learning circuits implemented in 50-nm CMOS technology, to be integrated with quantum dot and nanowire synapse, and 3. 3D stacking approach to achieve the overall numbers of high density O(10(exp 12)) synapses and O(10(exp 8)) neurons in the overall system. In a 1-sq cm of quantum dot layer sitting on a 50-nm CMOS layer, innovators were able to pack a 10(exp 6)-neuron and 10(exp 10)-synapse array; however, the constraint for the connection scheme is that each neuron will receive a non-identical 10(exp 4)-synapse set, including itself, via its efficacy of the connection. This is not a fully connected system where the 100x100 synapse array only has a 100-input data bus and 100-output data bus. Due to the data bus sharing, it poses a great challenge to have a complete connected system, and its constraint within the quantum dot and silicon wafer layer. For an effective connection scheme, there are three conditions to be met: 1. Local connection. 2. The nanowire should be connected locally, not globally from which it helps to maximize the data flow by sharing the same wire space location. 3. Each synapse can have an alternate summation line if needed (this option is doable based on the simple mask creation). The 10(exp 3)x10(exp 3)-neuron array was partitioned into a 10-block, 10(exp 2)x10(exp 3)-neuron array. This building block can be completely mapped within itself (10,000 synapses to a neuron).

  18. NMDA receptor content of synapses in stratum radiatum of the hippocampal CA1 area.

    Science.gov (United States)

    Racca, C; Stephenson, F A; Streit, P; Roberts, J D; Somogyi, P

    2000-04-01

    Glutamate receptors activated by NMDA (NMDARs) or AMPA (AMPARs) are clustered on dendritic spines of pyramidal cells. Both the AMPAR-mediated postsynaptic responses and the synaptic AMPAR immunoreactivity show a large intersynapse variability. Postsynaptic responses mediated by NMDARs show less variability. To assess the variability in NMDAR content and the extent of their coexistence with AMPARs in Schaffer collateral-commissural synapses of adult rat CA1 pyramidal cells, electron microscopic immunogold localization of receptors has been used. Immunoreactivity of NMDARs was detected in virtually all synapses on spines, but AMPARs were undetectable, on average, in 12% of synapses. A proportion of synapses had a very high AMPAR content relative to the mean content, resulting in a distribution more skewed toward larger values than that of NMDARs. The variability of synaptic NMDAR content [coefficient of variation (CV), 0.64-0.70] was much lower than that of the AMPAR content (CV, 1.17-1.45). Unlike the AMPAR content, the NMDAR content showed only a weak correlation with synapse size. As reported previously for AMPARs, the immunoreactivity of NMDARs was also associated with the spine apparatus within spines. The results demonstrate that the majority of the synapses made by CA3 pyramidal cells onto spines of CA1 pyramids express both NMDARs and AMPARs, but with variable ratios. A less-variable NMDAR content is accompanied by a wide variability of AMPAR content, indicating that the regulation of expression of the two receptors is not closely linked. These findings support reports that fast excitatory transmission at some of these synapses is mediated by activation mainly of NMDARs.

  19. Simbrain 3.0: A flexible, visually-oriented neural network simulator.

    Science.gov (United States)

    Tosi, Zachary; Yoshimi, Jeffrey

    2016-11-01

    Simbrain 3.0 is a software package for neural network design and analysis, which emphasizes flexibility (arbitrarily complex networks can be built using a suite of basic components) and a visually rich, intuitive interface. These features support both students and professionals. Students can study all of the major classes of neural networks in a familiar graphical setting, and can easily modify simulations, experimenting with networks and immediately seeing the results of their interventions. With the 3.0 release, Simbrain supports models on the order of thousands of neurons and a million synapses. This allows the same features that support education to support research professionals, who can now use the tool to quickly design, run, and analyze the behavior of large, highly customizable simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. A systematic random sampling scheme optimized to detect the proportion of rare synapses in the neuropil.

    Science.gov (United States)

    da Costa, Nuno Maçarico; Hepp, Klaus; Martin, Kevan A C

    2009-05-30

    Synapses can only be morphologically identified by electron microscopy and this is often a very labor-intensive and time-consuming task. When quantitative estimates are required for pathways that contribute a small proportion of synapses to the neuropil, the problems of accurate sampling are particularly severe and the total time required may become prohibitive. Here we present a sampling method devised to count the percentage of rarely occurring synapses in the neuropil using a large sample (approximately 1000 sampling sites), with the strong constraint of doing it in reasonable time. The strategy, which uses the unbiased physical disector technique, resembles that used in particle physics to detect rare events. We validated our method in the primary visual cortex of the cat, where we used biotinylated dextran amine to label thalamic afferents and measured the density of their synapses using the physical disector method. Our results show that we could obtain accurate counts of the labeled synapses, even when they represented only 0.2% of all the synapses in the neuropil.

  1. Synapse Formation in Monosynaptic Sensory–Motor Connections Is Regulated by Presynaptic Rho GTPase Cdc42

    Science.gov (United States)

    Imai, Fumiyasu; Ladle, David R.; Leslie, Jennifer R.; Duan, Xin; Rizvi, Tilat A.; Ciraolo, Georgianne M.; Zheng, Yi

    2016-01-01

    Spinal reflex circuit development requires the precise regulation of axon trajectories, synaptic specificity, and synapse formation. Of these three crucial steps, the molecular mechanisms underlying synapse formation between group Ia proprioceptive sensory neurons and motor neurons is the least understood. Here, we show that the Rho GTPase Cdc42 controls synapse formation in monosynaptic sensory–motor connections in presynaptic, but not postsynaptic, neurons. In mice lacking Cdc42 in presynaptic sensory neurons, proprioceptive sensory axons appropriately reach the ventral spinal cord, but significantly fewer synapses are formed with motor neurons compared with wild-type mice. Concordantly, electrophysiological analyses show diminished EPSP amplitudes in monosynaptic sensory–motor circuits in these mutants. Temporally targeted deletion of Cdc42 in sensory neurons after sensory–motor circuit establishment reveals that Cdc42 does not affect synaptic transmission. Furthermore, addition of the synaptic organizers, neuroligins, induces presynaptic differentiation of wild-type, but not Cdc42-deficient, proprioceptive sensory neurons in vitro. Together, our findings demonstrate that Cdc42 in presynaptic neurons is required for synapse formation in monosynaptic sensory–motor circuits. SIGNIFICANCE STATEMENT Group Ia proprioceptive sensory neurons form direct synapses with motor neurons, but the molecular mechanisms underlying synapse formation in these monosynaptic sensory–motor connections are unknown. We show that deleting Cdc42 in sensory neurons does not affect proprioceptive sensory axon targeting because axons reach the ventral spinal cord appropriately, but these neurons form significantly fewer presynaptic terminals on motor neurons. Electrophysiological analysis further shows that EPSPs are decreased in these mice. Finally, we demonstrate that Cdc42 is involved in neuroligin-dependent presynaptic differentiation of proprioceptive sensory neurons in vitro

  2. Neural circuit rewiring: insights from DD synapse remodeling.

    Science.gov (United States)

    Kurup, Naina; Jin, Yishi

    2016-01-01

    Nervous systems exhibit many forms of neuronal plasticity during growth, learning and memory consolidation, as well as in response to injury. Such plasticity can occur across entire nervous systems as with the case of insect metamorphosis, in individual classes of neurons, or even at the level of a single neuron. A striking example of neuronal plasticity in C. elegans is the synaptic rewiring of the GABAergic Dorsal D-type motor neurons during larval development, termed DD remodeling. DD remodeling entails multi-step coordination to concurrently eliminate pre-existing synapses and form new synapses on different neurites, without changing the overall morphology of the neuron. This mini-review focuses on recent advances in understanding the cellular and molecular mechanisms driving DD remodeling.

  3. Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

    Science.gov (United States)

    Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

    2016-12-01

    Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

  4. The chemical component of the mixed GF-TTMn synapse in Drosophila melanogaster uses acetylcholine as its neurotransmitter.

    Science.gov (United States)

    Allen, Marcus J; Murphey, R K

    2007-07-01

    The largest central synapse in adult Drosophila is a mixed electro-chemical synapse whose gap junctions require the product of the shaking-B (shak-B) gene. Shak-B(2) mutant flies lack gap junctions at this synapse, which is between the giant fibre (GF) and the tergotrochanteral motor neuron (TTMn), but it still exhibits a long latency response upon GF stimulation. We have targeted the expression of the light chain of tetanus toxin to the GF, to block chemical transmission, in shak-B(2) flies. The long latency response in the tergotrochanteral muscle (TTM) was abolished indicating that the chemical component of the synapse mediates this response. Attenuation of GAL4-mediated labelling by a cha-GAL80 transgene, reveals the GF to be cholinergic. We have used a temperature-sensitive allele of the choline acetyltransferase gene (cha(ts2)) to block cholinergic synapses in adult flies and this also abolished the long latency response in shak-B(2) flies. Taken together the data provide evidence that both components of this mixed synapse are functional and that the chemical neurotransmitter between the GF and the TTMn is acetylcholine. Our findings show that the two components of this synapse can be separated to allow further studies into the mechanisms by which mixed synapses are built and function.

  5. Efficient computation in networks of spiking neurons: simulations and theory

    International Nuclear Information System (INIS)

    Natschlaeger, T.

    1999-01-01

    One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so called action potentials or spikes. In this thesis we investigate possible mechanisms which can in principle explain how complex computations in spiking neural networks (SNN) can be performed very fast, i.e. within a few 10 milliseconds. Some of these models are based on the assumption that relevant information is encoded by the timing of individual spikes (temporal coding). We will also discuss a model which is based on a population code and still is able to perform fast complex computations. In their natural environment biological neural systems have to process signals with a rich temporal structure. Hence it is an interesting question how neural systems process time series. In this context we explore possible links between biophysical characteristics of single neurons (refractory behavior, connectivity, time course of postsynaptic potentials) and synapses (unreliability, dynamics) on the one hand and possible computations on times series on the other hand. Furthermore we describe a general model of computation that exploits dynamic synapses. This model provides a general framework for understanding how neural systems process time-varying signals. (author)

  6. A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

    Directory of Open Access Journals (Sweden)

    Alireza Alemi

    2015-08-01

    Full Text Available Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the

  7. An introduction to modeling neuronal dynamics

    CERN Document Server

    Börgers, Christoph

    2017-01-01

    This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book. .

  8. Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

    Science.gov (United States)

    Bhaduri, Aritra; Banerjee, Amitava; Roy, Subhrajit; Kar, Sougata; Basu, Arindam

    2018-03-01

    We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

  9. ON Cone Bipolar Cell Axonal Synapses in the OFF Inner Plexiform Layer of the Rabbit Retina

    Science.gov (United States)

    Lauritzen, J. Scott; Anderson, James R.; Jones, Bryan W.; Watt, Carl B.; Mohammed, Shoeb; Hoang, John V.; Marc, Robert E.

    2012-01-01

    Analysis of the rabbit retinal connectome RC1 reveals that the division between the ON and OFF inner plexiform layer (IPL) is not structurally absolute. ON cone bipolar cells make non-canonical axonal synapses onto specific targets and receive amacrine cell synapses in the nominal OFF layer, creating novel motifs, including inhibitory crossover networks. Automated transmission electron microscope (ATEM) imaging, molecular tagging, tracing, and rendering of ≈ 400 bipolar cells reveals axonal ribbons in 36% of ON cone bipolar cells, throughout the OFF IPL. The targets include GABA-positive amacrine cells (γACs), glycine-positive amacrine cells (GACs) and ganglion cells. Most ON cone bipolar cell axonal contacts target GACs driven by OFF cone bipolar cells, forming new architectures for generating ON-OFF amacrine cells. Many of these ON-OFF GACs target ON cone bipolar cell axons, ON γACs and/or ON-OFF ganglion cells, representing widespread mechanisms for OFF to ON crossover inhibition. Other targets include OFF γACs presynaptic to OFF bipolar cells, forming γAC-mediated crossover motifs. ON cone bipolar cell axonal ribbons drive bistratified ON-OFF ganglion cells in the OFF layer and provide ON drive to polarity-appropriate targets such as bistratified diving ganglion cells (bsdGCs). The targeting precision of ON cone bipolar cell axonal synapses shows that this drive incidence is necessarily a joint distribution of cone bipolar cell axonal frequency and target cell trajectories through a given volume of the OFF layer. Such joint distribution sampling is likely common when targets are sparser than sources and when sources are coupled, as are ON cone bipolar cells. PMID:23042441

  10. Altered Intrinsic Pyramidal Neuron Properties and Pathway-Specific Synaptic Dysfunction Underlie Aberrant Hippocampal Network Function in a Mouse Model of Tauopathy.

    Science.gov (United States)

    Booth, Clair A; Witton, Jonathan; Nowacki, Jakub; Tsaneva-Atanasova, Krasimira; Jones, Matthew W; Randall, Andrew D; Brown, Jonathan T

    2016-01-13

    The formation and deposition of tau protein aggregates is proposed to contribute to cognitive impairments in dementia by disrupting neuronal function in brain regions, including the hippocampus. We used a battery of in vivo and in vitro electrophysiological recordings in the rTg4510 transgenic mouse model, which overexpresses a mutant form of human tau protein, to investigate the effects of tau pathology on hippocampal neuronal function in area CA1 of 7- to 8-month-old mice, an age point at which rTg4510 animals exhibit advanced tau pathology and progressive neurodegeneration. In vitro recordings revealed shifted theta-frequency resonance properties of CA1 pyramidal neurons, deficits in synaptic transmission at Schaffer collateral synapses, and blunted plasticity and imbalanced inhibition at temporoammonic synapses. These changes were associated with aberrant CA1 network oscillations, pyramidal neuron bursting, and spatial information coding in vivo. Our findings relate tauopathy-associated changes in cellular neurophysiology to altered behavior-dependent network function. Dementia is characterized by the loss of learning and memory ability. The deposition of tau protein aggregates in the brain is a pathological hallmark of dementia; and the hippocampus, a brain structure known to be critical in processing learning and memory, is one of the first and most heavily affected regions. Our results show that, in area CA1 of hippocampus, a region involved in spatial learning and memory, tau pathology is associated with specific disturbances in synaptic, cellular, and network-level function, culminating in the aberrant encoding of spatial information and spatial memory impairment. These studies identify several novel ways in which hippocampal information processing may be disrupted in dementia, which may provide targets for future therapeutic intervention. Copyright © 2016 Booth, Witton et al.

  11. Witnessing stressful events induces glutamatergic synapse pathway alterations and gene set enrichment of positive EPSP regulation within the VTA of adult mice: An ontology based approach

    Science.gov (United States)

    Brewer, Jacob S.

    It is well known that exposure to severe stress increases the risk for developing mood disorders. Currently, the neurobiological and genetic mechanisms underlying the functional effects of psychological stress are poorly understood. Presenting a major obstacle to the study of psychological stress is the inability of current animal models of stress to distinguish between physical and psychological stressors. A novel paradigm recently developed by Warren et al., is able to tease apart the effects of physical and psychological stress in adult mice by allowing these mice to "witness," the social defeat of another mouse thus removing confounding variables associated with physical stressors. Using this 'witness' model of stress and RNA-Seq technology, the current study aims to study the genetic effects of psychological stress. After, witnessing the social defeat of another mouse, VTA tissue was extracted, sequenced, and analyzed for differential expression. Since genes often work together in complex networks, a pathway and gene ontology (GO) analysis was performed using data from the differential expression analysis. The pathway and GO analyzes revealed a perturbation of the glutamatergic synapse pathway and an enrichment of positive excitatory post-synaptic potential regulation. This is consistent with the excitatory synapse theory of depression. Together these findings demonstrate a dysregulation of the mesolimbic reward pathway at the gene level as a result of psychological stress potentially contributing to depressive like behaviors.

  12. Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks.

    Science.gov (United States)

    Lombardi, F; Herrmann, H J; de Arcangelis, L

    2017-04-01

    The 1/f-like decay observed in the power spectrum of electro-physiological signals, along with scale-free statistics of the so-called neuronal avalanches, constitutes evidence of criticality in neuronal systems. Recent in vitro studies have shown that avalanche dynamics at criticality corresponds to some specific balance of excitation and inhibition, thus suggesting that this is a basic feature of the critical state of neuronal networks. In particular, a lack of inhibition significantly alters the temporal structure of the spontaneous avalanche activity and leads to an anomalous abundance of large avalanches. Here, we study the relationship between network inhibition and the scaling exponent β of the power spectral density (PSD) of avalanche activity in a neuronal network model inspired in Self-Organized Criticality. We find that this scaling exponent depends on the percentage of inhibitory synapses and tends to the value β = 1 for a percentage of about 30%. More specifically, β is close to 2, namely, Brownian noise, for purely excitatory networks and decreases towards values in the interval [1, 1.4] as the percentage of inhibitory synapses ranges between 20% and 30%, in agreement with experimental findings. These results indicate that the level of inhibition affects the frequency spectrum of resting brain activity and suggest the analysis of the PSD scaling behavior as a possible tool to study pathological conditions.

  13. Collaborative networks: Reference modeling

    NARCIS (Netherlands)

    Camarinha-Matos, L.M.; Afsarmanesh, H.

    2008-01-01

    Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

  14. Differential regulation of polarized synaptic vesicle trafficking and synapse stability in neural circuit rewiring in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Naina Kurup

    2017-06-01

    Full Text Available Neural circuits are dynamic, with activity-dependent changes in synapse density and connectivity peaking during different phases of animal development. In C. elegans, young larvae form mature motor circuits through a dramatic switch in GABAergic neuron connectivity, by concomitant elimination of existing synapses and formation of new synapses that are maintained throughout adulthood. We have previously shown that an increase in microtubule dynamics during motor circuit rewiring facilitates new synapse formation. Here, we further investigate cellular control of circuit rewiring through the analysis of mutants obtained in a forward genetic screen. Using live imaging, we characterize novel mutations that alter cargo binding in the dynein motor complex and enhance anterograde synaptic vesicle movement during remodeling, providing in vivo evidence for the tug-of-war between kinesin and dynein in fast axonal transport. We also find that a casein kinase homolog, TTBK-3, inhibits stabilization of nascent synapses in their new locations, a previously unexplored facet of structural plasticity of synapses. Our study delineates temporally distinct signaling pathways that are required for effective neural circuit refinement.

  15. Anergic CD4+ T cells form mature immunological synapses with enhanced accumulation of c-Cbl and Cbl-b1

    Science.gov (United States)

    Doherty, Melissa; Osborne, Douglas G.; Browning, Diana L.; Parker, David C.; Wetzel, Scott A.

    2010-01-01

    CD4+ T cell recognition of MHC:peptide complexes in the context of a costimulatory signal results in the large-scale redistribution of molecules at the T-APC interface to form the immunological synapse. The immunological synapse is the location of sustained TCR signaling and delivery of a subset of effector functions. T cells activated in the absence of costimulation are rendered anergic and are hyporesponsive when presented with antigen in the presence of optimal costimulation. Several previous studies have looked at aspects of immunological synapses formed by anergic T cells, but it remains unclear whether there are differences in the formation or composition of anergic immunological synapses. In this study we anergized primary murine CD4+ T cells by incubation of costimulation-deficient, transfected fibroblast APC. Using a combination of TCR, MHC:peptide, and ICAM-1 staining, we found that anergic T cells make mature immunological synapses with characteristic cSMAC and pSMAC domains that were indistinguishable from control synapses. There were small increases in total phosphotyrosine at the anergic synapse along with significant decreases in phosphorylated ERK 1/2 accumulation. Most striking, there was specific accumulation of c-Cbl and Cbl-b to the anergic synapses. Cbl-b, previously shown to be essential in anergy induction, was found in both the pSMAC and the cSMAC of the anergic synapse. This Cbl-b (and c-Cbl) accumulation at the anergic synapse may play an important role in anergy maintenance and/or induction. PMID:20207996

  16. New players tip the scales in the balance between excitatory and inhibitory synapses

    Directory of Open Access Journals (Sweden)

    El-Husseini Alaa

    2005-03-01

    Full Text Available Abstract Synaptogenesis is a highly controlled process, involving a vast array of players which include cell adhesion molecules, scaffolding and signaling proteins, neurotransmitter receptors and proteins associated with the synaptic vesicle machinery. These molecules cooperate in an intricate manner on both the pre- and postsynaptic sides to orchestrate the precise assembly of neuronal contacts. This is an amazing feat considering that a single neuron receives tens of thousands of synaptic inputs but virtually no mismatch between pre- and postsynaptic components occur in vivo. One crucial aspect of synapse formation is whether a nascent synapse will develop into an excitatory or inhibitory contact. The tight control of a balance between the types of synapses formed regulates the overall neuronal excitability, and is thus critical for normal brain function and plasticity. However, little is known about how this balance is achieved. This review discusses recent findings which provide clues to how neurons may control excitatory and inhibitory synapse formation, with focus on the involvement of the neuroligin family and PSD-95 in this process.

  17. A recurrent neural network with ever changing synapses

    NARCIS (Netherlands)

    Heerema, M.; van Leeuwen, W.A.

    2000-01-01

    A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the

  18. An Incremental Time-delay Neural Network for Dynamical Recurrent Associative Memory

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.

  19. Whisker Deprivation Drives Two Phases of Inhibitory Synapse Weakening in Layer 4 of Rat Somatosensory Cortex.

    Directory of Open Access Journals (Sweden)

    Melanie A Gainey

    Full Text Available Inhibitory synapse development in sensory neocortex is experience-dependent, with sustained sensory deprivation yielding fewer and weaker inhibitory synapses. Whether this represents arrest of synapse maturation, or a more complex set of processes, is unclear. To test this, we measured the dynamics of inhibitory synapse development in layer 4 of rat somatosensory cortex (S1 during continuous whisker deprivation from postnatal day 7, and in age-matched controls. In deprived columns, spontaneous miniature inhibitory postsynaptic currents (mIPSCs and evoked IPSCs developed normally until P15, when IPSC amplitude transiently decreased, recovering by P16 despite ongoing deprivation. IPSCs remained normal until P22, when a second, sustained phase of weakening began. Delaying deprivation onset by 5 days prevented the P15 weakening. Both early and late phase weakening involved measurable reduction in IPSC amplitude relative to prior time points. Thus, deprivation appears to drive two distinct phases of active IPSC weakening, rather than simple arrest of synapse maturation.

  20. Nanogranular SiO{sub 2} proton gated silicon layer transistor mimicking biological synapses

    Energy Technology Data Exchange (ETDEWEB)

    Liu, M. J.; Huang, G. S., E-mail: gshuang@fudan.edu.cn, E-mail: pfeng@nju.edu.cn; Guo, Q. L.; Tian, Z. A.; Li, G. J.; Mei, Y. F. [Department of Materials Science, Fudan University, Shanghai 200433 (China); Feng, P., E-mail: gshuang@fudan.edu.cn, E-mail: pfeng@nju.edu.cn; Shao, F.; Wan, Q. [School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093 (China)

    2016-06-20

    Silicon on insulator (SOI)-based transistors gated by nanogranular SiO{sub 2} proton conducting electrolytes were fabricated to mimic synapse behaviors. This SOI-based device has both top proton gate and bottom buried oxide gate. Electrical transfer properties of top proton gate show hysteresis curves different from those of bottom gate, and therefore, excitatory post-synaptic current and paired pulse facilitation (PPF) behavior of biological synapses are mimicked. Moreover, we noticed that PPF index can be effectively tuned by the spike interval applied on the top proton gate. Synaptic behaviors and functions, like short-term memory, and its properties are also experimentally demonstrated in our device. Such SOI-based electronic synapses are promising for building neuromorphic systems.

  1. Abundance of gap junctions at glutamatergic mixed synapses in adult Mosquitofish spinal cord neurons

    Directory of Open Access Journals (Sweden)

    Jose L Serrano-Velez

    2014-06-01

    Full Text Available Dye-coupling, whole-mount immunohistochemistry for gap junction channel protein connexin 35 (Cx35, and freeze-fracture replica immunogold labeling (FRIL reveal an abundance of electrical synapses/gap junctions at glutamatergic mixed synapses in the 14th spinal segment that innervates the adult male gonopodium of Western Mosquitofish, Gambusia affinis (Mosquitofish.To study gap junctions’ role in fast motor behavior, we used a minimally-invasive neural-tract-tracing technique to introduce gap junction-permeant or -impermeant dyes into deep muscles controlling the gonopodium of the adult male Mosquitofish, a teleost fish that rapidly transfers (complete in 50 of the 62 gap junctions at mixed synapses are in the 14th spinal segment.Our results support and extend studies showing gap junctions at mixed synapses in spinal cord segments involved in control of genital reflexes in rodents, and they suggest a link between mixed synapses and fast motor behavior. The findings provide a basis for studies of specific roles of spinal neurons in the generation/regulation of sex-specific behavior and for studies of gap junctions’ role in regulating fast motor behavior. Finally, the CoPA IN provides a novel candidate neuron for future studies of gap junctions and neural control of fast motor behaviors.

  2. Neurotrophin-3 Regulates Synapse Development by Modulating TrkC-PTPσ Synaptic Adhesion and Intracellular Signaling Pathways.

    Science.gov (United States)

    Han, Kyung Ah; Woo, Doyeon; Kim, Seungjoon; Choii, Gayoung; Jeon, Sangmin; Won, Seoung Youn; Kim, Ho Min; Heo, Won Do; Um, Ji Won; Ko, Jaewon

    2016-04-27

    Neurotrophin-3 (NT-3) is a secreted neurotrophic factor that binds neurotrophin receptor tyrosine kinase C (TrkC), which in turn binds to presynaptic protein tyrosine phosphatase σ (PTPσ) to govern excitatory synapse development. However, whether and how NT-3 cooperates with the TrkC-PTPσ synaptic adhesion pathway and TrkC-mediated intracellular signaling pathways in rat cultured neurons has remained unclear. Here, we report that NT-3 enhances TrkC binding affinity for PTPσ. Strikingly, NT-3 treatment bidirectionally regulates the synaptogenic activity of TrkC: at concentrations of 10-25 ng/ml, NT-3 further enhanced the increase in synapse density induced by TrkC overexpression, whereas at higher concentrations, NT-3 abrogated TrkC-induced increases in synapse density. Semiquantitative immunoblotting and optogenetics-based imaging showed that 25 ng/ml NT-3 or light stimulation at a power that produced a comparable level of NT-3 (6.25 μW) activated only extracellular signal-regulated kinase (ERK) and Akt, whereas 100 ng/ml NT-3 (light intensity, 25 μW) further triggered the activation of phospholipase C-γ1 and CREB independently of PTPσ. Notably, disruption of TrkC intracellular signaling pathways, extracellular ligand binding, or kinase activity by point mutations compromised TrkC-induced increases in synapse density. Furthermore, only sparse, but not global, TrkC knock-down in cultured rat neurons significantly decreased synapse density, suggesting that intercellular differences in TrkC expression level are critical for its synapse-promoting action. Together, our data demonstrate that NT-3 is a key factor in excitatory synapse development that may direct higher-order assembly of the TrkC/PTPσ complex and activate distinct intracellular signaling cascades in a concentration-dependent manner to promote competition-based synapse development processes. In this study, we present several lines of experimental evidences to support the conclusion that

  3. Long-term potentiation expands information content of hippocampal dentate gyrus synapses.

    Science.gov (United States)

    Bromer, Cailey; Bartol, Thomas M; Bowden, Jared B; Hubbard, Dusten D; Hanka, Dakota C; Gonzalez, Paola V; Kuwajima, Masaaki; Mendenhall, John M; Parker, Patrick H; Abraham, Wickliffe C; Sejnowski, Terrence J; Harris, Kristen M

    2018-03-06

    An approach combining signal detection theory and precise 3D reconstructions from serial section electron microscopy (3DEM) was used to investigate synaptic plasticity and information storage capacity at medial perforant path synapses in adult hippocampal dentate gyrus in vivo. Induction of long-term potentiation (LTP) markedly increased the frequencies of both small and large spines measured 30 minutes later. This bidirectional expansion resulted in heterosynaptic counterbalancing of total synaptic area per unit length of granule cell dendrite. Control hemispheres exhibited 6.5 distinct spine sizes for 2.7 bits of storage capacity while LTP resulted in 12.9 distinct spine sizes (3.7 bits). In contrast, control hippocampal CA1 synapses exhibited 4.7 bits with much greater synaptic precision than either control or potentiated dentate gyrus synapses. Thus, synaptic plasticity altered total capacity, yet hippocampal subregions differed dramatically in their synaptic information storage capacity, reflecting their diverse functions and activation histories.

  4. Multiple cell adhesion molecules shaping a complex nicotinic synapse on neurons.

    Science.gov (United States)

    Triana-Baltzer, Gallen B; Liu, Zhaoping; Gounko, Natalia V; Berg, Darwin K

    2008-09-01

    Neuroligin, SynCAM, and L1-CAM are cell adhesion molecules with synaptogenic roles in glutamatergic pathways. We show here that SynCAM is expressed in the chick ciliary ganglion, embedded in a nicotinic pathway, and, as shown previously for neuroligin and L1-CAM, acts transcellularly to promote synaptic maturation on the neurons in culture. Moreover, we show that electroporation of chick embryos with dominant negative constructs disrupting any of the three molecules in vivo reduces the total amount of presynaptic SV2 overlaying the neurons expressing the constructs. Only disruption of L1-CAM and neuroligin, however, reduces the number of SV2 puncta specifically overlaying nicotinic receptor clusters. Disrupting L1-CAM and neuroligin together produces no additional decrement, indicating that they act on the same subset of synapses. SynCAM may affect synaptic maturation rather than synapse formation. The results indicate that individual neurons can express multiple synaptogenic molecules with different effects on the same class of nicotinic synapses.

  5. Thyroid hormone is required for pruning, functioning and long-term maintenance of afferent inner hair cell synapses.

    Science.gov (United States)

    Sundaresan, Srividya; Kong, Jee-Hyun; Fang, Qing; Salles, Felipe T; Wangsawihardja, Felix; Ricci, Anthony J; Mustapha, Mirna

    2016-01-01

    Functional maturation of afferent synaptic connections to inner hair cells (IHCs) involves pruning of excess synapses formed during development, as well as the strengthening and survival of the retained synapses. These events take place during the thyroid hormone (TH)-critical period of cochlear development, which is in the perinatal period for mice and in the third trimester for humans. Here, we used the hypothyroid Snell dwarf mouse (Pit1(dw)) as a model to study the role of TH in afferent type I synaptic refinement and functional maturation. We observed defects in afferent synaptic pruning and delays in calcium channel clustering in the IHCs of Pit1(dw) mice. Nevertheless, calcium currents and capacitance reached near normal levels in Pit1(dw) IHCs by the age of onset of hearing, despite the excess number of retained synapses. We restored normal synaptic pruning in Pit1(dw) IHCs by supplementing with TH from postnatal day (P)3 to P8, establishing this window as being critical for TH action on this process. Afferent terminals of older Pit1(dw) IHCs showed evidence of excitotoxic damage accompanied by a concomitant reduction in the levels of the glial glutamate transporter, GLAST. Our results indicate that a lack of TH during a critical period of inner ear development causes defects in pruning and long-term homeostatic maintenance of afferent synapses. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  6. The sticky synapse

    DEFF Research Database (Denmark)

    Owczarek, Sylwia Elzbieta; Kristiansen, Lars Villiam; Hortsch, Michael

    NCAM-type proteins modulate multiple neuronal functions, including the outgrowth and guidance of neurites, the formation, maturation, and plasticity of synapses, and the induction of both long-term potentiation and long-term depression. The ectodomains of NCAM proteins have a basic structure...... mediate cell-cell adhesion through homophilic interactions and bind to growth factors, growth factor receptors, glutamate receptors, other CAMs, and components of the extracellular matrix. Intracellularly, NCAM-type proteins interact with various cytoskeletal proteins and regulators of intracellular...... signal transduction. A central feature of the synaptic function of NCAM proteins is the regulation of their extracellular interactions by adhesion-modulating glycoepitopes, their removal from the cell surface by endocytosis, and the elimination of their adhesion-mediating interactions by the proteolytic...

  7. Effects of Estradiol on Learned Helplessness and Associated Remodeling of Hippocampal Spine Synapses in Female Rats

    Science.gov (United States)

    Hajszan, Tibor; Szigeti-Buck, Klara; Sallam, Nermin L; Bober, Jeremy; Parducz, Arpad; MacLusky, Neil J; Leranth, Csaba; Duman, Ronald S

    2009-01-01

    Background Despite the fact that women are twice as likely to develop depression as men, our understanding of depression neurobiology in females is limited. We have recently reported in male rats that development of helpless behavior is associated with a severe loss of hippocampal spine synapses, which is reversed by treatment with the antidepressant, desipramine. Considering the fact that estradiol has a hippocampal synaptogenic effect similar to those of antidepressants, the presence of estradiol during the female reproductive life may influence behavioral and synaptic responses to stress and depression. Methods Using electron microscopic stereology, we analyzed hippocampal spine synapses in association with helpless behavior in ovariectomized female rats (n=70), under different conditions of estradiol exposure. Results Stress induced an acute and persistent loss of hippocampal spine synapses, while subchronic treatment with desipramine reversed the stress-induced synaptic loss. Estradiol supplementation given either prior to stress or prior to escape testing of nonstressed animals both increased the number of hippocampal spine synapses. Correlation analysis demonstrated a statistically significant negative correlation between the severity of helpless behavior and hippocampal spine synapse numbers. Conclusions These findings suggest that hippocampal spine synapse remodeling may be a critical factor underlying learned helplessness and, possibly, the neurobiology of depression. PMID:19811775

  8. Extracellular proteolysis in structural and functional plasticity of mossy fiber synapses in hippocampus

    Directory of Open Access Journals (Sweden)

    Grzegorz eWiera

    2015-11-01

    Full Text Available Brain is continuously altered in response to experience and environmental changes. One of the underlying mechanisms is synaptic plasticity, which is manifested by modification of synapse structure and function. It is becoming clear that regulated extracellular proteolysis plays a pivotal role in the structural and functional remodeling of synapses during brain development, learning and memory formation. Clearly, plasticity mechanisms may substantially differ between projections. Mossy fiber synapses onto CA3 pyramidal cells display several unique functional features, including pronounced short-term facilitation, a presynaptically expressed LTP that is independent of NMDAR activation, and NMDA-dependent metaplasticity. Moreover, structural plasticity at mossy fiber synapses ranges from the reorganization of projection topology after hippocampus-dependent learning, through intrinsically different dynamic properties of synaptic boutons to pre- and postsynaptic structural changes accompanying LTP induction. Although concomitant functional and structural plasticity in this pathway strongly suggests a role of extracellular proteolysis, its impact only starts to be investigated in this projection. In the present report, we review the role of extracellular proteolysis in various aspects of synaptic plasticity in hippocampal mossy fiber synapses. A growing body of evidence demonstrates that among perisynaptic proteases, tPA/plasmin system, β-site amyloid precursor protein-cleaving enzyme 1 (BACE1 and metalloproteinases play a crucial role in shaping plastic changes in this projection. We discuss recent advances and emerging hypotheses on the roles of proteases in mechanisms underlying mossy fiber target specific synaptic plasticity and memory formation.

  9. Memristor-based neural networks: Synaptic versus neuronal stochasticity

    KAUST Repository

    Naous, Rawan

    2016-11-02

    In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

  10. Aging-related impairments of hippocampal mossy fibers synapses on CA3 pyramidal cells.

    Science.gov (United States)

    Villanueva-Castillo, Cindy; Tecuatl, Carolina; Herrera-López, Gabriel; Galván, Emilio J

    2017-01-01

    The network interaction between the dentate gyrus and area CA3 of the hippocampus is responsible for pattern separation, a process that underlies the formation of new memories, and which is naturally diminished in the aged brain. At the cellular level, aging is accompanied by a progression of biochemical modifications that ultimately affects its ability to generate and consolidate long-term potentiation. Although the synapse between dentate gyrus via the mossy fibers (MFs) onto CA3 neurons has been subject of extensive studies, the question of how aging affects the MF-CA3 synapse is still unsolved. Extracellular and whole-cell recordings from acute hippocampal slices of aged Wistar rats (34 ± 2 months old) show that aging is accompanied by a reduction in the interneuron-mediated inhibitory mechanisms of area CA3. Several MF-mediated forms of short-term plasticity, MF long-term potentiation and at least one of the critical signaling cascades necessary for potentiation are also compromised in the aged brain. An analysis of the spontaneous glutamatergic and gamma-aminobutyric acid-mediated currents on CA3 cells reveal a dramatic alteration in amplitude and frequency of the nonevoked events. CA3 cells also exhibited increased intrinsic excitability. Together, these results demonstrate that aging is accompanied by a decrease in the GABAergic inhibition, reduced expression of short- and long-term forms of synaptic plasticity, and increased intrinsic excitability. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Astrocyte lipid metabolism is critical for synapse development and function in vivo.

    Science.gov (United States)

    van Deijk, Anne-Lieke F; Camargo, Nutabi; Timmerman, Jaap; Heistek, Tim; Brouwers, Jos F; Mogavero, Floriana; Mansvelder, Huibert D; Smit, August B; Verheijen, Mark H G

    2017-04-01

    The brain is considered to be autonomous in lipid synthesis with astrocytes producing lipids far more efficiently than neurons. Accordingly, it is generally assumed that astrocyte-derived lipids are taken up by neurons to support synapse formation and function. Initial confirmation of this assumption has been obtained in cell cultures, but whether astrocyte-derived lipids support synapses in vivo is not known. Here, we address this issue and determined the role of astrocyte lipid metabolism in hippocampal synapse formation and function in vivo. Hippocampal protein expression for the sterol regulatory element-binding protein (SREBP) and its target gene fatty acid synthase (Fasn) was found in astrocytes but not in neurons. Diminishing SREBP activity in astrocytes using mice in which the SREBP cleavage-activating protein (SCAP) was deleted from GFAP-expressing cells resulted in decreased cholesterol and phospholipid secretion by astrocytes. Interestingly, SCAP mutant mice showed more immature synapses, lower presynaptic protein SNAP-25 levels as well as reduced numbers of synaptic vesicles, indicating impaired development of the presynaptic terminal. Accordingly, hippocampal short-term and long-term synaptic plasticity were defective in mutant mice. These findings establish a critical role for astrocyte lipid metabolism in presynaptic terminal development and function in vivo. GLIA 2017;65:670-682. © 2017 Wiley Periodicals, Inc.

  12. A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing.

    Science.gov (United States)

    Sillin, Henry O; Aguilera, Renato; Shieh, Hsien-Hang; Avizienis, Audrius V; Aono, Masakazu; Stieg, Adam Z; Gimzewski, James K

    2013-09-27

    Atomic switch networks (ASNs) have been shown to generate network level dynamics that resemble those observed in biological neural networks. To facilitate understanding and control of these behaviors, we developed a numerical model based on the synapse-like properties of individual atomic switches and the random nature of the network wiring. We validated the model against various experimental results highlighting the possibility to functionalize the network plasticity and the differences between an atomic switch in isolation and its behaviors in a network. The effects of changing connectivity density on the nonlinear dynamics were examined as characterized by higher harmonic generation in response to AC inputs. To demonstrate their utility for computation, we subjected the simulated network to training within the framework of reservoir computing and showed initial evidence of the ASN acting as a reservoir which may be optimized for specific tasks by adjusting the input gain. The work presented represents steps in a unified approach to experimentation and theory of complex systems to make ASNs a uniquely scalable platform for neuromorphic computing.

  13. Synapse formation and maintenance by C1q family proteins: a new class of secreted synapse organizers.

    Science.gov (United States)

    Yuzaki, Michisuke

    2010-07-01

    Several C1q family members, especially the Cbln and C1q-like subfamilies, are highly and predominantly expressed in the central nervous system. Cbln1, a member of the Cbln subfamily, plays two unique roles at parallel fiber (PF)-Purkinje cell synapses in the cerebellum: the formation and stabilization of synaptic contact, and the control of functional synaptic plasticity by regulating the postsynaptic endocytotic pathway. The delta2 glutamate receptor (GluD2), which is predominantly expressed in Purkinje cells, plays similar critical roles in the cerebellum. In addition, viral expression of GluD2 or the application of recombinant Cbln1 induces PF-Purkinje cell synaptogenesis in vitro and in vivo. Antigen-unmasking methods were necessary to reveal the immunoreactivities for endogenous Cbln1 and GluD2 at the synaptic junction of PF synapses. We propose that Cbln1 and GluD2 are located at the synaptic cleft, where various proteins undergo intricate molecular interactions with each other, and serve as a bidirectional synaptic organizer. © The Author (2010). Journal Compilation © Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  14. Phase-locking and bistability in neuronal networks with synaptic depression

    Science.gov (United States)

    Akcay, Zeynep; Huang, Xinxian; Nadim, Farzan; Bose, Amitabha

    2018-02-01

    We consider a recurrent network of two oscillatory neurons that are coupled with inhibitory synapses. We use the phase response curves of the neurons and the properties of short-term synaptic depression to define Poincaré maps for the activity of the network. The fixed points of these maps correspond to phase-locked modes of the network. Using these maps, we analyze the conditions that allow short-term synaptic depression to lead to the existence of bistable phase-locked, periodic solutions. We show that bistability arises when either the phase response curve of the neuron or the short-term depression profile changes steeply enough. The results apply to any Type I oscillator and we illustrate our findings using the Quadratic Integrate-and-Fire and Morris-Lecar neuron models.

  15. Alpha-Bungarotoxin labeling and acetylcholinesterase localization at the Mauthner fiber giant synapse in the hatchetfish

    International Nuclear Information System (INIS)

    Day, J.W.; Hall, D.H.; Hall, L.M.; Bennett, M.V.

    1983-01-01

    Autoradiographic and histochemical techniques have been used to characterize further the pharmacology of transmission at the Mauthner fiber giant synapse of the South American hatchetfish. [ 125 I]alpha-Bungarotoxin was applied to hatchetfish medullae and a standard autoradiographic procedure was carried out on 3- to 4-microns sections of glutaraldehyde-fixed tissue. All Mauthner fiber giant synapses, as identified by light microscopic criteria, had closely associated silver grains. Labeling was blocked by d-tubocurarine. Glutaraldehyde-fixed slices of hatchetfish medulla were stained histochemically for acetylcholinesterase; all giant synapses that could be identified in the light microscope showed heavy deposits of reaction product. Staining was blocked by diisopropyl-fluorophosphate, which inhibits both pseudocholinesterase and acetylcholinesterase, but was not blocked by tetraisopropylpyrophosphoramide, a specific pseudocholinesterase inhibitor. This evidence strongly supports the suggestion that the Mauthner fiber giant synapse is nicotinic cholinergic

  16. alpha-Bungarotoxin labeling and acetylcholinesterase localization at the Mauthner fiber giant synapse in the hatchetfish

    International Nuclear Information System (INIS)

    Day, J.W.; Hall, D.H.; Hall, L.M.; Bennett, M.V.

    1983-01-01

    Autoradiographic and histochemical techniques have been used to characterize further the pharmacology of transmission at the Mauthner fiber giant synapse of the South American hatchetfish. [ 125 I]alpha-Bungarotoxin was applied to hatchetfish medullae and a standard autoradiographic procedure was carried out on 3- to 4-microns sections of glutaraldehyde-fixed tissue. All Mauthner fiber giant synapses, as identified by light microscopic criteria, had closely associated silver grains. Labeling was blocked by d-tubocurarine. Glutaraldehyde-fixed slices of hatchetfish medulla were stained histochemically for acetylcholinesterase; all giant synapses that could be identified in the light microscope showed heavy deposits of reaction product. Staining was blocked by diisopropyl-fluorophosphate, which inhibits both pseudocholinesterase and acetylcholinesterase, but was not blocked by tetraisopropylpyrophosphoramide, a specific pseudocholinesterase inhibitor. This evidence strongly supports the suggestion that the Mauthner fiber giant synapse is nicotinic cholinergic

  17. Alpha-Bungarotoxin labeling and acetylcholinesterase localization at the Mauthner fiber giant synapse in the hatchetfish

    Energy Technology Data Exchange (ETDEWEB)

    Day, J.W.; Hall, D.H.; Hall, L.M.; Bennett, M.V.

    1983-02-01

    Autoradiographic and histochemical techniques have been used to characterize further the pharmacology of transmission at the Mauthner fiber giant synapse of the South American hatchetfish. (/sup 125/I)alpha-Bungarotoxin was applied to hatchetfish medullae and a standard autoradiographic procedure was carried out on 3- to 4-microns sections of glutaraldehyde-fixed tissue. All Mauthner fiber giant synapses, as identified by light microscopic criteria, had closely associated silver grains. Labeling was blocked by d-tubocurarine. Glutaraldehyde-fixed slices of hatchetfish medulla were stained histochemically for acetylcholinesterase; all giant synapses that could be identified in the light microscope showed heavy deposits of reaction product. Staining was blocked by diisopropyl-fluorophosphate, which inhibits both pseudocholinesterase and acetylcholinesterase, but was not blocked by tetraisopropylpyrophosphoramide, a specific pseudocholinesterase inhibitor. This evidence strongly supports the suggestion that the Mauthner fiber giant synapse is nicotinic cholinergic.

  18. alpha-Bungarotoxin labeling and acetylcholinesterase localization at the Mauthner fiber giant synapse in the hatchetfish

    Energy Technology Data Exchange (ETDEWEB)

    Day, J.W.; Hall, D.H.; Hall, L.M.; Bennett, M.V.

    1983-02-01

    Autoradiographic and histochemical techniques have been used to characterize further the pharmacology of transmission at the Mauthner fiber giant synapse of the South American hatchetfish. (/sup 125/I)alpha-Bungarotoxin was applied to hatchetfish medullae and a standard autoradiographic procedure was carried out on 3- to 4-microns sections of glutaraldehyde-fixed tissue. All Mauthner fiber giant synapses, as identified by light microscopic criteria, had closely associated silver grains. Labeling was blocked by d-tubocurarine. Glutaraldehyde-fixed slices of hatchetfish medulla were stained histochemically for acetylcholinesterase; all giant synapses that could be identified in the light microscope showed heavy deposits of reaction product. Staining was blocked by diisopropyl-fluorophosphate, which inhibits both pseudocholinesterase and acetylcholinesterase, but was not blocked by tetraisopropylpyrophosphoramide, a specific pseudocholinesterase inhibitor. This evidence strongly supports the suggestion that the Mauthner fiber giant synapse is nicotinic cholinergic.

  19. Endocannabinoid release modulates electrical coupling between CCK cells connected via chemical and electrical synapses in CA1

    Directory of Open Access Journals (Sweden)

    Jonathan eIball

    2011-11-01

    Full Text Available Electrical coupling between some subclasses of interneurons is thought to promote coordinated firing that generates rhythmic synchronous activity in cortical regions. Synaptic activity of cholesystokinin (CCK interneurons which co-express cannbinoid type-1 (CB1 receptors are powerful modulators of network activity via the actions of endocannabinoids. We investigated the modulatory actions of endocannabinoids between chemically and electrically connected synapses of CCK cells using paired whole-cell recordings combined with biocytin and double immunofluorescence labelling in acute slices of rat hippocampus at P18-20 days. CA1 stratum radiatum CCK Schaffer collateral associated (SCA cells were coupled electrically with each other as well as CCK basket cells and CCK cells with axonal projections expanding to dentate gyrus. Approximately 50% of electrically coupled cells received facilitating, asynchronously released IPSPs that curtailed the steady-state coupling coefficient by 57%. Tonic CB1 receptor activity which reduces inhibition enhanced electrical coupling between cells that were connected via chemical and electrical synapses. Blocking CB1 receptors with antagonist, AM-251 (5M resulted in the synchronized release of larger IPSPs and this enhanced inhibition further reduced the steady-state coupling coefficient by 85%. Depolarization induced suppression of inhibition (DSI, maintained the asynchronicity of IPSP latency, but reduced IPSP amplitudes by 95% and enhanced the steady-state coupling coefficient by 104% and IPSP duration by 200%. However, DSI did not did not enhance electrical coupling at purely electrical synapses. These data suggest that different morphological subclasses of CCK interneurons are interconnected via gap junctions. The synergy between the chemical and electrical coupling between CCK cells probably plays a role in activity-dependent endocannabinoid modulation of rhythmic synchronization.

  20. Emerging roles of the neurotrophin receptor TrkC in synapse organization.

    Science.gov (United States)

    Naito, Yusuke; Lee, Alfred Kihoon; Takahashi, Hideto

    2017-03-01

    Tropomyosin-receptor-kinase (Trk) receptors have been extensively studied for their roles in kinase-dependent signaling cascades in nervous system development. Synapse organization is coordinated by trans-synaptic interactions of various cell adhesion proteins, a representative example of which is the neurexin-neuroligin complex. Recently, a novel role for TrkC as a synapse organizing protein has been established. Post-synaptic TrkC binds to pre-synaptic type-IIa receptor-type protein tyrosine phosphatase sigma (PTPσ). TrkC-PTPσ specifically induces excitatory synapses in a kinase domain-independent manner. TrkC has distinct extracellular domains for PTPσ- and NT-3-binding and thus may bind both ligands simultaneously. Indeed, NT-3 enhances the TrkC-PTPσ interaction, thus facilitating synapse induction at the pre-synaptic side and increasing pre-synaptic vesicle recycling in a kinase-independent fashion. A crystal structure study has revealed the detailed structure of the TrkC-PTPσ complex as well as competitive modulation of TrkC-mediated synaptogenesis by heparan sulfate proteoglycans (HSPGs), which bind the same domain of TrkC as PTPσ. Thus, there is strong evidence supporting a role for the TrkC-PTPσ complex in mechanisms underlying the fine turning of neural connectivity. Furthermore, disruption of the TrkC-PTPσ complex may be the underlying cause of certain psychiatric disorders caused by mutations in the gene encoding TrkC (NTRK3), supporting its role in cognitive functions. Copyright © 2016 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

  1. Monoacylated Cellular Prion Proteins Reduce Amyloid-β-Induced Activation of Cytoplasmic Phospholipase A2 and Synapse Damage

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    Ewan West

    2015-06-01

    Full Text Available Alzheimer’s disease (AD is a progressive neurodegenerative disease characterized by the accumulation of amyloid-β (Aβ and the loss of synapses. Aggregation of the cellular prion protein (PrPC by Aβ oligomers induced synapse damage in cultured neurons. PrPC is attached to membranes via a glycosylphosphatidylinositol (GPI anchor, the composition of which affects protein targeting and cell signaling. Monoacylated PrPC incorporated into neurons bound “natural Aβ”, sequestering Aβ outside lipid rafts and preventing its accumulation at synapses. The presence of monoacylated PrPC reduced the Aβ-induced activation of cytoplasmic phospholipase A2 (cPLA2 and Aβ-induced synapse damage. This protective effect was stimulus specific, as treated neurons remained sensitive to α-synuclein, a protein associated with synapse damage in Parkinson’s disease. In synaptosomes, the aggregation of PrPC by Aβ oligomers triggered the formation of a signaling complex containing the cPLA2.a process, disrupted by monoacylated PrPC. We propose that monoacylated PrPC acts as a molecular sponge, binding Aβ oligomers at the neuronal perikarya without activating cPLA2 or triggering synapse damage.

  2. Espina: A Tool for the Automated Segmentation and Counting of Synapses in Large Stacks of Electron Microscopy Images

    Science.gov (United States)

    Morales, Juan; Alonso-Nanclares, Lidia; Rodríguez, José-Rodrigo; DeFelipe, Javier; Rodríguez, Ángel; Merchán-Pérez, Ángel

    2011-01-01

    The synapses in the cerebral cortex can be classified into two main types, Gray's type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes. PMID:21633491

  3. ESPINA: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images

    Directory of Open Access Journals (Sweden)

    Juan eMorales

    2011-03-01

    Full Text Available The synapses in the cerebral cortex can be classified into two main types, Gray’s type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory and symmetric (inhibitory GABAergic synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze three-dimensional samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using FIB/SEM microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed and quantified from large three-dimensional tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

  4. When is an Inhibitory Synapse Effective?

    Science.gov (United States)

    Qian, Ning; Sejnowski, Terrence J.

    1990-10-01

    Interactions between excitatory and inhibitory synaptic inputs on dendrites determine the level of activity in neurons. Models based on the cable equation predict that silent shunting inhibition can strongly veto the effect of an excitatory input. The cable model assumes that ionic concentrations do not change during the electrical activity, which may not be a valid assumption, especially for small structures such as dendritic spines. We present here an analysis and computer simulations to show that for large Cl^- conductance changes, the more general Nernst-Planck electrodiffusion model predicts that shunting inhibition on spines should be much less effective than that predicted by the cable model. This is a consequence of the large changes in the intracellular ionic concentration of Cl^- that can occur in small structures, which would alter the reversal potential and reduce the driving force for Cl^-. Shunting inhibition should therefore not be effective on spines, but it could be significantly more effective on the dendritic shaft at the base of the spine. In contrast to shunting inhibition, hyperpolarizing synaptic inhibition mediated by K^+ currents can be very effective in reducing the excitatory synaptic potentials on the same spine if the excitatory conductance change is less than 10 nS. We predict that if the inhibitory synapses found on cortical spines are to be effective, then they should be mediated by K^+ through GABA_B receptors.

  5. The therapeutic effect of memantine through the stimulation of synapse formation and dendritic spine maturation in autism and fragile X syndrome.

    Directory of Open Access Journals (Sweden)

    Hongen Wei

    Full Text Available Although the pathogenic mechanisms that underlie autism are not well understood, there is evidence showing that metabotropic and ionotropic glutamate receptors are hyper-stimulated and the GABAergic system is hypo-stimulated in autism. Memantine is an uncompetitive antagonist of NMDA receptors and is widely prescribed for treatment of Alzheimer's disease treatment. Recently, it has been shown to improve language function, social behavior, and self-stimulatory behaviors of some autistic subjects. However the mechanism by which memantine exerts its effect remains to be elucidated. In this study, we used cultured cerebellar granule cells (CGCs from Fmr1 knockout (KO mice, a mouse model for fragile X syndrome (FXS and syndromic autism, to examine the effects of memantine on dendritic spine development and synapse formation. Our results show that the maturation of dendritic spines is delayed in Fmr1-KO CGCs. We also detected reduced excitatory synapse formation in Fmr1-KO CGCs. Memantine treatment of Fmr1-KO CGCs promoted cell adhesion properties. Memantine also stimulated the development of mushroom-shaped mature dendritic spines and restored dendritic spine to normal levels in Fmr1-KO CGCs. Furthermore, we demonstrated that memantine treatment promoted synapse formation and restored the excitatory synapses to a normal range in Fmr1-KO CGCs. These findings suggest that memantine may exert its therapeutic capacity through a stimulatory effect on dendritic spine maturation and excitatory synapse formation, as well as promoting adhesion of CGCs.

  6. SynDIG4/Prrt1 Is Required for Excitatory Synapse Development and Plasticity Underlying Cognitive Function

    Directory of Open Access Journals (Sweden)

    Lucas Matt

    2018-02-01

    Full Text Available Altering AMPA receptor (AMPAR content at synapses is a key mechanism underlying the regulation of synaptic strength during learning and memory. Previous work demonstrated that SynDIG1 (synapse differentiation-induced gene 1 encodes a transmembrane AMPAR-associated protein that regulates excitatory synapse strength and number. Here we show that the related protein SynDIG4 (also known as Prrt1 modifies AMPAR gating properties in a subunit-dependent manner. Young SynDIG4 knockout (KO mice have weaker excitatory synapses, as evaluated by immunocytochemistry and electrophysiology. Adult SynDIG4 KO mice show complete loss of tetanus-induced long-term potentiation (LTP, while mEPSC amplitude is reduced by only 25%. Furthermore, SynDIG4 KO mice exhibit deficits in two independent cognitive assays. Given that SynDIG4 colocalizes with the AMPAR subunit GluA1 at non-synaptic sites, we propose that SynDIG4 maintains a pool of extrasynaptic AMPARs necessary for synapse development and function underlying higher-order cognitive plasticity.

  7. Cognon Neural Model Software Verification and Hardware Implementation Design

    Science.gov (United States)

    Haro Negre, Pau

    Little is known yet about how the brain can recognize arbitrary sensory patterns within milliseconds using neural spikes to communicate information between neurons. In a typical brain there are several layers of neurons, with each neuron axon connecting to ˜104 synapses of neurons in an adjacent layer. The information necessary for cognition is contained in theses synapses, which strengthen during the learning phase in response to newly presented spike patterns. Continuing on the model proposed in "Models for Neural Spike Computation and Cognition" by David H. Staelin and Carl H. Staelin, this study seeks to understand cognition from an information theoretic perspective and develop potential models for artificial implementation of cognition based on neuronal models. To do so we focus on the mathematical properties and limitations of spike-based cognition consistent with existing neurological observations. We validate the cognon model through software simulation and develop concepts for an optical hardware implementation of a network of artificial neural cognons.

  8. Synapses between parallel fibres and stellate cells express long-term changes in synaptic efficacy in rat cerebellum.

    Science.gov (United States)

    Rancillac, Armelle; Crépel, Francis

    2004-02-01

    Various forms of synaptic plasticity underlying motor learning have already been well characterized at cerebellar parallel fibre (PF)-Purkinje cell (PC) synapses. Inhibitory interneurones play an important role in controlling the excitability and synchronization of PCs. We have therefore tested the possibility that excitatory synapses between PFs and stellate cells (SCs) are also able to exhibit long-term changes in synaptic efficacy. In the present study, we show that long-term potentiation (LTP) and long-term depression (LTD) were induced at these synapses by a low frequency stimulation protocol (2 Hz for 60 s) and that pairing this low frequency stimulation protocol with postsynaptic depolarization induced a marked shift of synaptic plasticity in favour of LTP. This LTP was cAMP independent, but required nitric oxide (NO) production from pre- and/or postsynaptic elements, depending on the stimulation or pairing protocol used, respectively. In contrast, LTD was not dependent on NO production but it required activation of postsynaptic group II and possibly of group I metabotropic glutamate receptors. Finally, stimulation of PFs at 8 Hz for 15 s also induced LTP at PF-SC synapses. But in this case, LTP was cAMP dependent, as was also observed at PF-PC synapses for presynaptic LTP induced in the same conditions. Thus, long-term changes in synaptic efficacy can be accomplished by PF-SCs synapses as well as by PF-PC synapses, suggesting that both types of plasticity might co-operate during cerebellar motor learning.

  9. Involvement of intracellular Zn2+ signaling in LTP at perforant pathway-CA1 pyramidal cell synapse.

    Science.gov (United States)

    Tamano, Haruna; Nishio, Ryusuke; Takeda, Atsushi

    2017-07-01

    Physiological significance of synaptic Zn 2+ signaling was examined at perforant pathway-CA1 pyramidal cell synapses. In vivo long-term potentiation (LTP) at perforant pathway-CA1 pyramidal cell synapses was induced using a recording electrode attached to a microdialysis probe and the recording region was locally perfused with artificial cerebrospinal fluid (ACSF) via the microdialysis probe. Perforant pathway LTP was not attenuated under perfusion with CaEDTA (10 mM), an extracellular Zn 2+ chelator, but attenuated under perfusion with ZnAF-2DA (50 μM), an intracellular Zn 2+ chelator, suggesting that intracellular Zn 2+ signaling is required for perforant pathway LTP. Even in rat brain slices bathed in CaEDTA in ACSF, intracellular Zn 2+ level, which was measured with intracellular ZnAF-2, was increased in the stratum lacunosum-moleculare where perforant pathway-CA1 pyramidal cell synapses were contained after tetanic stimulation. These results suggest that intracellular Zn 2+ signaling, which originates in internal stores/proteins, is involved in LTP at perforant pathway-CA1 pyramidal cell synapses. Because the influx of extracellular Zn 2+ , which originates in presynaptic Zn 2+ release, is involved in LTP at Schaffer collateral-CA1 pyramidal cell synapses, synapse-dependent Zn 2+ dynamics may be involved in plasticity of postsynaptic CA1 pyramidal cells. © 2017 Wiley Periodicals, Inc.

  10. Fine structure of synapses on dendritic spines

    Directory of Open Access Journals (Sweden)

    Michael eFrotscher

    2014-09-01

    Full Text Available Camillo Golgi’s Reazione Nera led to the discovery of dendritic spines, small appendages originating from dendritic shafts. With the advent of electron microscopy (EM they were identified as sites of synaptic contact. Later it was found that changes in synaptic strength were associated with changes in the shape of dendritic spines. While live-cell imaging was advantageous in monitoring the time course of such changes in spine structure, EM is still the best method for the simultaneous visualization of all cellular components, including actual synaptic contacts, at high resolution. Immunogold labeling for EM reveals the precise localization of molecules in relation to synaptic structures. Previous EM studies of spines and synapses were performed in tissue subjected to aldehyde fixation and dehydration in ethanol, which is associated with protein denaturation and tissue shrinkage. It has remained an issue to what extent fine structural details are preserved when subjecting the tissue to these procedures. In the present review, we report recent studies on the fine structure of spines and synapses using high-pressure freezing (HPF, which avoids protein denaturation by aldehydes and results in an excellent preservation of ultrastructural detail. In these studies, HPF was used to monitor subtle fine-structural changes in spine shape associated with chemically induced long-term potentiation (cLTP at identified hippocampal mossy fiber synapses. Changes in spine shape result from reorganization of the actin cytoskeleton. We report that cLTP was associated with decreased immunogold labeling for phosphorylated cofilin (p-cofilin, an actin-depolymerizing protein. Phosphorylation of cofilin renders it unable to depolymerize F-actin, which stabilizes the actin cytoskeleton. Decreased levels of p-cofilin, in turn, suggest increased actin turnover, possibly underlying the changes in spine shape associated with cLTP. The findings reviewed here establish HPF as

  11. A neighbourhood evolving network model

    International Nuclear Information System (INIS)

    Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

    2006-01-01

    Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

  12. Emergent Synapse Organizers: LAR-RPTPs and Their Companions.

    Science.gov (United States)

    Han, K A; Jeon, S; Um, J W; Ko, J

    2016-01-01

    Leukocyte common antigen-related receptor tyrosine phosphatases (LAR-RPTPs) have emerged as key players that organize various aspects of neuronal development, including axon guidance, neurite extension, and synapse formation and function. Recent research has highlighted the roles of LAR-RPTPs at neuronal synapses in mediating distinct synaptic adhesion pathways through interactions with a host of extracellular ligands and in governing a variety of intracellular signaling cascades through binding to various scaffolds and signaling proteins. In this chapter, we review and update current research progress on the extracellular ligands of LAR-RPTPs, regulation of their extracellular interactions by alternative splicing and heparan sulfates, and their intracellular signaling machineries. In particular, we review structural insights on complexes of LAR-RPTPs with their various ligands. These studies lend support to general molecular mechanisms underlying LAR-RPTP-mediated synaptic adhesion and signaling pathways. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. A neural network model of semantic memory linking feature-based object representation and words.

    Science.gov (United States)

    Cuppini, C; Magosso, E; Ursino, M

    2009-06-01

    Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

  14. Mechanisms of input and output synaptic specificity: finding partners, building synapses, and fine-tuning communication.

    Science.gov (United States)

    Rawson, Randi L; Martin, E Anne; Williams, Megan E

    2017-08-01

    For most neurons to function properly, they need to develop synaptic specificity. This requires finding specific partner neurons, building the correct types of synapses, and fine-tuning these synapses in response to neural activity. Synaptic specificity is common at both a neuron's input and output synapses, whereby unique synapses are built depending on the partnering neuron. Neuroscientists have long appreciated the remarkable specificity of neural circuits but identifying molecular mechanisms mediating synaptic specificity has only recently accelerated. Here, we focus on recent progress in understanding input and output synaptic specificity in the mammalian brain. We review newly identified circuit examples for both and the latest research identifying molecular mediators including Kirrel3, FGFs, and DGLα. Lastly, we expect the pace of research on input and output specificity to continue to accelerate with the advent of new technologies in genomics, microscopy, and proteomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Binding and segmentation via a neural mass model trained with Hebbian and anti-Hebbian mechanisms.

    Science.gov (United States)

    Cona, Filippo; Zavaglia, Melissa; Ursino, Mauro

    2012-04-01

    Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

  16. Mitochondria and Neurotransmission: Evacuating the Synapse

    OpenAIRE

    Hollenbeck, Peter J.

    2005-01-01

    An abundance of mitochondria has been the hallmark of synapses since their first ultrastructural description 50 years ago. Mitochondria have been shown to be essential for synaptic form and function in many systems, but until recently it has not been clear exactly what role(s) they play in neurotransmission. Now, evidence from the nervous system of Drosophila identifies the specific subcellular events that are most dependent upon nearby mitochondria.

  17. Cadmium action in synapses in the brain

    International Nuclear Information System (INIS)

    Minami, Akira; Takeda, Atsushi; Nishibaba, Daisuke; Tekefuta, Sachiyo; Oku, Naoto

    2001-01-01

    Chronic exposure to cadmium causes central nervous system disorders, e.g., olfactory dysfunction. To clarify cadmium toxicity in synaptic neurotransmission in the brain, the movement and action of cadmium in the synapses was examined using in vivo microdialysis. One and 24 h after injection of 109 CdCl 2 into the amygdala of rats, 109 Cd release into the extracellular space was facilitated by stimulation with high K + , suggesting that cadmium taken up in amygdalar neurons is released into the synaptic clefts in a calcium- and impulse-dependent manner. To examine the action of cadmium in the synapses, the amygdala was perfused with artificial cerebrospinal fluid containing 10-30 μM CdCl 2 . The release of excitatory neurotransmitters, i.e., glutamate and aspartate, into the extracellular space was decreased during perfusion with cadmium, while the release of inhibitory neurotransmitters, i.e., glycine and γ-amino butyric acid (GABA), into the extracellular space was increased during the period. These results suggest that cadmium released from the amygdalar neuron terminals affects the degree and balance of excitation-inhibition in synaptic neurotransmission. (author)

  18. Global sensitivity analysis of a model related to memory formation in synapses: Model reduction based on epistemic parameter uncertainties and related issues.

    Science.gov (United States)

    Kulasiri, Don; Liang, Jingyi; He, Yao; Samarasinghe, Sandhya

    2017-04-21

    We investigate the epistemic uncertainties of parameters of a mathematical model that describes the dynamics of CaMKII-NMDAR complex related to memory formation in synapses using global sensitivity analysis (GSA). The model, which was published in this journal, is nonlinear and complex with Ca 2+ patterns with different level of frequencies as inputs. We explore the effects of parameter on the key outputs of the model to discover the most sensitive ones using GSA and partial ranking correlation coefficient (PRCC) and to understand why they are sensitive and others are not based on the biology of the problem. We also extend the model to add presynaptic neurotransmitter vesicles release to have action potentials as inputs of different frequencies. We perform GSA on this extended model to show that the parameter sensitivities are different for the extended model as shown by PRCC landscapes. Based on the results of GSA and PRCC, we reduce the original model to a less complex model taking the most important biological processes into account. We validate the reduced model against the outputs of the original model. We show that the parameter sensitivities are dependent on the inputs and GSA would make us understand the sensitivities and the importance of the parameters. A thorough phenomenological understanding of the relationships involved is essential to interpret the results of GSA and hence for the possible model reduction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Local dynamics of gap-junction-coupled interneuron networks

    International Nuclear Information System (INIS)

    Lau, Troy; Zochowski, Michal; Gage, Gregory J; Berke, Joshua D

    2010-01-01

    Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making

  20. Spontaneous Vesicle Fusion Is Differentially Regulated at Cholinergic and GABAergic Synapses

    Directory of Open Access Journals (Sweden)

    Haowen Liu

    2018-02-01

    Full Text Available The locomotion of C. elegans is balanced by excitatory and inhibitory neurotransmitter release at neuromuscular junctions. However, the molecular mechanisms that maintain the balance of synaptic transmission remain enigmatic. Here, we investigated the function of voltage-gated Ca2+ channels in triggering spontaneous release at cholinergic and GABAergic synapses. Recordings of the miniature excitatory/inhibitory postsynaptic currents (mEPSCs and mIPSCs, respectively showed that UNC-2/CaV2 and EGL-19/CaV1 channels are the two major triggers for spontaneous release. Notably, however, Ca2+-independent spontaneous release was observed at GABAergic but not cholinergic synapses. Functional screening led to the identification of hypomorphic unc-64/Syntaxin-1A and snb-1/VAMP2 mutants in which mEPSCs are severely impaired, whereas mIPSCs remain unaltered, indicating differential regulation of these currents at cholinergic and GABAergic synapses. Moreover, Ca2+-independent spontaneous GABA release was nearly abolished in the hypomorphic unc-64 and snb-1 mutants, suggesting distinct mechanisms for Ca2+-dependent and Ca2+-independent spontaneous release.

  1. Blocking p75 (NTR) receptors alters polyinnervationz of neuromuscular synapses during development.

    Science.gov (United States)

    Garcia, Neus; Tomàs, Marta; Santafe, Manel M; Lanuza, Maria A; Besalduch, Nuria; Tomàs, Josep

    2011-09-01

    High-resolution immunohistochemistry shows that the receptor protein p75(NTR) is present in the nerve terminal, muscle cell, and glial Schwann cell at the neuromuscular junction (NMJ) of postnatal rats (P4-P6) during the synapse elimination period. Blocking the receptor with the antibody anti-p75-192-IgG (1-5 μg/ml, 1 hr) results in reduced endplate potentials (EPPs) in mono- and polyinnervated synapses ex vivo, but the mean number of functional inputs per NMJ does not change for as long as 3 hr. Incubation with exogenous brain-derived neurotrophic factor (BDNF) for 1 hr (50 nM) resulted in a significant increase in the size of the EPPs in all nerve terminals, and preincubation with anti-p75-192-IgG prevented this potentiation. Long exposure (24 hr) in vivo of the NMJs to the antibody anti-p75-192-IgG (1-2 μg/ml) results in a delay of postnatal synapse elimination and even some regrowth of previously withdrawn axons, but also in some acceleration of the morphologic maturation of the postsynaptic nicotinic acetylcholine receptor (nAChR) clusters. The results indicate that p75(NTR) is involved in both ACh release and axonal retraction during postnatal axonal competition and synapse elimination. Copyright © 2011 Wiley-Liss, Inc.

  2. Neuron-NG2 Cell Synapses: Novel Functions for Regulating NG2 Cell Proliferation and Differentiation

    Directory of Open Access Journals (Sweden)

    Qian-Kun Yang

    2013-01-01

    Full Text Available NG2 cells are a population of CNS cells that are distinct from neurons, mature oligodendrocytes, astrocytes, and microglia. These cells can be identified by their NG2 proteoglycan expression. NG2 cells have a highly branched morphology, with abundant processes radiating from the cell body, and express a complex set of voltage-gated channels, AMPA/kainate, and GABA receptors. Neurons notably form classical and nonclassical synapses with NG2 cells, which have varied characteristics and functions. Neuron-NG2 cell synapses could fine-tune NG2 cell activities, including the NG2 cell cycle, differentiation, migration, and myelination, and may be a novel potential therapeutic target for NG2 cell-related diseases, such as hypoxia-ischemia injury and periventricular leukomalacia. Furthermore, neuron-NG2 cell synapses may be correlated with the plasticity of CNS in adulthood with the synaptic contacts passing onto their progenies during proliferation, and synaptic contacts decrease rapidly upon NG2 cell differentiation. In this review, we highlight the characteristics of classical and nonclassical neuron-NG2 cell synapses, the potential functions, and the fate of synaptic contacts during proliferation and differentiation, with the emphasis on the regulation of the NG2 cell cycle by neuron-NG2 cell synapses and their potential underlying mechanisms.

  3. Telecommunications network modelling, planning and design

    CERN Document Server

    Evans, Sharon

    2003-01-01

    Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

  4. Inverse stochastic resonance in networks of spiking neurons.

    Science.gov (United States)

    Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J

    2017-07-01

    Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

  5. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  6. Early Seizures Prematurely Unsilence Auditory Synapses to Disrupt Thalamocortical Critical Period Plasticity

    Directory of Open Access Journals (Sweden)

    Hongyu Sun

    2018-05-01

    Full Text Available Heightened neural excitability in infancy and childhood results in increased susceptibility to seizures. Such early-life seizures are associated with language deficits and autism that can result from aberrant development of the auditory cortex. Here, we show that early-life seizures disrupt a critical period (CP for tonotopic map plasticity in primary auditory cortex (A1. We show that this CP is characterized by a prevalence of “silent,” NMDA-receptor (NMDAR-only, glutamate receptor synapses in auditory cortex that become “unsilenced” due to activity-dependent AMPA receptor (AMPAR insertion. Induction of seizures prior to this CP occludes tonotopic map plasticity by prematurely unsilencing NMDAR-only synapses. Further, brief treatment with the AMPAR antagonist NBQX following seizures, prior to the CP, prevents synapse unsilencing and permits subsequent A1 plasticity. These findings reveal that early-life seizures modify CP regulators and suggest that therapeutic targets for early post-seizure treatment can rescue CP plasticity.

  7. Mixed electrical-chemical synapses in adult rat hippocampus are primarily glutamatergic and coupled by connexin-36

    Directory of Open Access Journals (Sweden)

    Farid eHamzei-Sichani

    2012-05-01

    Full Text Available Dendrodendritic electrical signaling via gap junctions is now an accepted feature of neuronal communication in the mammalian brain, whereas axodendritic and axosomatic gap junctions have rarely been described. We present ultrastructural, immunocytochemical, and dye-coupling evidence for mixed (electrical/chemical synapses in adult rat hippocampus on both principal cells and interneurons. Thin-section electron microscopic images of small gap junction-like appositions were found at mossy fiber (MF terminals on thorny excrescences of CA3 pyramidal neurons (CA3pyr, apparently forming glutamatergic mixed synapses. Lucifer Yellow injected into four weakly-fixed CA3pyr was detected in MF axons that contacted the injected CA3pyr, supporting gap junction-mediated coupling between those two types of principal cells. Freeze-fracture replica immunogold-labeling revealed diverse sizes and morphologies of connexin36-containing gap junctions throughout hippocampus. Of 20 immunogold-labeled gap junctions, seven were large (328-1140 connexons, three of which were consistent with electrical synapses between interneurons; but nine were at axon terminal synapses, three of which were immediately adjacent to distinctive glutamate receptor-containing postsynaptic densities, forming mixed glutamatergic synapses. Four others were adjacent to small clusters of immunogold-labeled 10-nm E-face intramembrane particles, apparently representing extrasynaptic glutamate receptor particles. Gap junctions also were on spines in stratum lucidum, stratum oriens, dentate gyrus, and hilus, on both interneurons and unidentified neurons. In addition, one putative GABAergic mixed synapse was found in thin section images of a CA3pyr, but none found by immunogold-labeling were at GABAergic mixed synapses, suggesting their rarity. Cx36-containing gap junctions throughout hippocampus suggest the possibility of reciprocal modulation of electrical and chemical signals in diverse hippocampal

  8. Real-Time Million-Synapse Simulation of Rat Barrel Cortex

    Directory of Open Access Journals (Sweden)

    Thomas eSharp

    2014-05-01

    Full Text Available Simulations of neural circuits are bounded in scale and speed by available computing resources, and particularly by the differences in parallelism and communication patterns between the brain and high-performance computers. SpiNNaker is a computer architecture designed to address this problem by emulating the structure and function of neural tissue, using very many low-power processors and an interprocessor communication mechanism inspired by axonal arbors. Here we demonstrate that thousand-processor SpiNNaker prototypes can simulate models of the rodent barrel system comprising fifty thousand neurons and fifty million synapses. We use the PyNN library to specify models, and the intrinsic features of Python to control experimental procedures and analysis. The models reproduce known thalamocortical response transformations, exhibit known, balanced dynamics of excitation and inhibition, and show a spatiotemporal spread of activity though the superficial cortical layers. These demonstrations are a significant step towards tractable simulations of entire cortical areas on the million-processor SpiNNaker machines in development.

  9. Real-time million-synapse simulation of rat barrel cortex.

    Science.gov (United States)

    Sharp, Thomas; Petersen, Rasmus; Furber, Steve

    2014-01-01

    Simulations of neural circuits are bounded in scale and speed by available computing resources, and particularly by the differences in parallelism and communication patterns between the brain and high-performance computers. SpiNNaker is a computer architecture designed to address this problem by emulating the structure and function of neural tissue, using very many low-power processors and an interprocessor communication mechanism inspired by axonal arbors. Here we demonstrate that thousand-processor SpiNNaker prototypes can simulate models of the rodent barrel system comprising 50,000 neurons and 50 million synapses. We use the PyNN library to specify models, and the intrinsic features of Python to control experimental procedures and analysis. The models reproduce known thalamocortical response transformations, exhibit known, balanced dynamics of excitation and inhibition, and show a spatiotemporal spread of activity though the superficial cortical layers. These demonstrations are a significant step toward tractable simulations of entire cortical areas on the million-processor SpiNNaker machines in development.

  10. Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity.

    Directory of Open Access Journals (Sweden)

    Felix Effenberger

    2015-09-01

    Full Text Available Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.

  11. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim

    2014-12-03

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  12. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

    2014-01-01

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  13. Natural killer cell signal integration balances synapse symmetry and migration.

    Directory of Open Access Journals (Sweden)

    Fiona J Culley

    2009-07-01

    Full Text Available Natural killer (NK cells discern the health of other cells by recognising the balance of activating and inhibitory ligands expressed by each target cell. However, how the integration of activating and inhibitory signals relates to formation of the NK cell immune synapse remains a central question in our understanding of NK cell recognition. Here we report that ligation of LFA-1 on NK cells induced asymmetrical cell spreading and migration. In contrast, ligation of the activating receptor NKG2D induced symmetrical spreading of ruffled lamellipodia encompassing a dynamic ring of f-actin, concurrent with polarization towards a target cell and a "stop" signal. Ligation of both LFA-1 and NKG2D together resulted in symmetrical spreading but co-ligation of inhibitory receptors reverted NK cells to an asymmetrical migratory configuration leading to inhibitory synapses being smaller and more rapidly disassembled. Using micropatterned activating and inhibitory ligands, signals were found to be continuously and locally integrated during spreading. Together, these data demonstrate that NK cells spread to form large, stable, symmetrical synapses if activating signals dominate, whereas asymmetrical migratory "kinapses" are favoured if inhibitory signals dominate. This clarifies how the integration of activating and inhibitory receptor signals is translated to an appropriate NK cell response.

  14. Large-scale modelling of neuronal systems

    International Nuclear Information System (INIS)

    Castellani, G.; Verondini, E.; Giampieri, E.; Bersani, F.; Remondini, D.; Milanesi, L.; Zironi, I.

    2009-01-01

    The brain is, without any doubt, the most, complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this, learning rule can be a support For network generation.

  15. Endocannabinoid Release Modulates Electrical Coupling between CCK Cells Connected via Chemical and Electrical Synapses in CA1

    Science.gov (United States)

    Iball, Jonathan; Ali, Afia B.

    2011-01-01

    Electrical coupling between some subclasses of interneurons is thought to promote coordinated firing that generates rhythmic synchronous activity in cortical regions. Synaptic activity of cholecystokinin (CCK) interneurons which co-express cannabinoid type-1 (CB1) receptors are powerful modulators of network activity via the actions of endocannabinoids. We investigated the modulatory actions of endocannabinoids between chemically and electrically connected synapses of CCK cells using paired whole-cell recordings combined with biocytin and double immunofluorescence labeling in acute slices of rat hippocampus at P18–20 days. CA1 stratum radiatum CCK Schaffer collateral-associated cells were coupled electrically with each other as well as CCK basket cells and CCK cells with axonal projections expanding to dentate gyrus. Approximately 50% of electrically coupled cells received facilitating, asynchronously released inhibitory postsynaptic potential (IPSPs) that curtailed the steady-state coupling coefficient by 57%. Tonic CB1 receptor activity which reduces inhibition enhanced electrical coupling between cells that were connected via chemical and electrical synapses. Blocking CB1 receptors with antagonist, AM-251 (5 μM) resulted in the synchronized release of larger IPSPs and this enhanced inhibition further reduced the steady-state coupling coefficient by 85%. Depolarization induced suppression of inhibition (DSI), maintained the asynchronicity of IPSP latency, but reduced IPSP amplitudes by 95% and enhanced the steady-state coupling coefficient by 104% and IPSP duration by 200%. However, DSI did not did not enhance electrical coupling at purely electrical synapses. These data suggest that different morphological subclasses of CCK interneurons are interconnected via gap junctions. The synergy between the chemical and electrical coupling between CCK cells probably plays a role in activity-dependent endocannabinoid modulation of rhythmic synchronization. PMID

  16. Toward Petascale Biologically Plausible Neural Networks

    Science.gov (United States)

    Long, Lyle

    This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

  17. Modeling online social signed networks

    Science.gov (United States)

    Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

    2018-04-01

    People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

  18. Eigenanalysis of a neural network for optic flow processing

    International Nuclear Information System (INIS)

    Weber, F; Eichner, H; Borst, A; Cuntz, H

    2008-01-01

    Flies gain information about self-motion during free flight by processing images of the environment moving across their retina. The visual course control center in the brain of the blowfly contains, among others, a population of ten neurons, the so-called vertical system (VS) cells that are mainly sensitive to downward motion. VS cells are assumed to encode information about rotational optic flow induced by self-motion (Krapp and Hengstenberg 1996 Nature 384 463-6). Recent evidence supports a connectivity scheme between the VS cells where neurons with neighboring receptive fields are connected to each other by electrical synapses at the axonal terminals, whereas the boundary neurons in the network are reciprocally coupled via inhibitory synapses (Haag and Borst 2004 Nat. Neurosci. 7 628-34; Farrow et al 2005 J. Neurosci. 25 3985-93; Cuntz et al 2007 Proc. Natl Acad. Sci. USA). Here, we investigate the functional properties of the VS network and its connectivity scheme by reducing a biophysically realistic network to a simplified model, where each cell is represented by a dendritic and axonal compartment only. Eigenanalysis of this model reveals that the whole population of VS cells projects the synaptic input provided from local motion detectors on to its behaviorally relevant components. The two major eigenvectors consist of a horizontal and a slanted line representing the distribution of vertical motion components across the fly's azimuth. They are, thus, ideally suited for reliably encoding translational and rotational whole-field optic flow induced by respective flight maneuvers. The dimensionality reduction compensates for the contrast and texture dependence of the local motion detectors of the correlation-type, which becomes particularly pronounced when confronted with natural images and their highly inhomogeneous contrast distribution

  19. T cells' immunological synapses induce polarization of brain astrocytes in vivo and in vitro: a novel astrocyte response mechanism to cellular injury.

    Science.gov (United States)

    Barcia, Carlos; Sanderson, Nicholas S R; Barrett, Robert J; Wawrowsky, Kolja; Kroeger, Kurt M; Puntel, Mariana; Liu, Chunyan; Castro, Maria G; Lowenstein, Pedro R

    2008-08-20

    Astrocytes usually respond to trauma, stroke, or neurodegeneration by undergoing cellular hypertrophy, yet, their response to a specific immune attack by T cells is poorly understood. Effector T cells establish specific contacts with target cells, known as immunological synapses, during clearance of virally infected cells from the brain. Immunological synapses mediate intercellular communication between T cells and target cells, both in vitro and in vivo. How target virally infected astrocytes respond to the formation of immunological synapses established by effector T cells is unknown. Herein we demonstrate that, as a consequence of T cell attack, infected astrocytes undergo dramatic morphological changes. From normally multipolar cells, they become unipolar, extending a major protrusion towards the immunological synapse formed by the effector T cells, and withdrawing most of their finer processes. Thus, target astrocytes become polarized towards the contacting T cells. The MTOC, the organizer of cell polarity, is localized to the base of the protrusion, and Golgi stacks are distributed throughout the protrusion, reaching distally towards the immunological synapse. Thus, rather than causing astrocyte hypertrophy, antiviral T cells cause a major structural reorganization of target virally infected astrocytes. Astrocyte polarization, as opposed to hypertrophy, in response to T cell attack may be due to T cells providing a very focused attack, and thus, astrocytes responding in a polarized manner. A similar polarization of Golgi stacks towards contacting T cells was also detected using an in vitro allogeneic model. Thus, different T cells are able to induce polarization of target astrocytes. Polarization of target astrocytes in response to immunological synapses may play an important role in regulating the outcome of the response of astrocytes to attacking effector T cells, whether during antiviral (e.g. infected during HIV, HTLV-1, HSV-1 or LCMV infection), anti

  20. T cells' immunological synapses induce polarization of brain astrocytes in vivo and in vitro: a novel astrocyte response mechanism to cellular injury.

    Directory of Open Access Journals (Sweden)

    Carlos Barcia

    2008-08-01

    Full Text Available Astrocytes usually respond to trauma, stroke, or neurodegeneration by undergoing cellular hypertrophy, yet, their response to a specific immune attack by T cells is poorly understood. Effector T cells establish specific contacts with target cells, known as immunological synapses, during clearance of virally infected cells from the brain. Immunological synapses mediate intercellular communication between T cells and target cells, both in vitro and in vivo. How target virally infected astrocytes respond to the formation of immunological synapses established by effector T cells is unknown.Herein we demonstrate that, as a consequence of T cell attack, infected astrocytes undergo dramatic morphological changes. From normally multipolar cells, they become unipolar, extending a major protrusion towards the immunological synapse formed by the effector T cells, and withdrawing most of their finer processes. Thus, target astrocytes become polarized towards the contacting T cells. The MTOC, the organizer of cell polarity, is localized to the base of the protrusion, and Golgi stacks are distributed throughout the protrusion, reaching distally towards the immunological synapse. Thus, rather than causing astrocyte hypertrophy, antiviral T cells cause a major structural reorganization of target virally infected astrocytes.Astrocyte polarization, as opposed to hypertrophy, in response to T cell attack may be due to T cells providing a very focused attack, and thus, astrocytes responding in a polarized manner. A similar polarization of Golgi stacks towards contacting T cells was also detected using an in vitro allogeneic model. Thus, different T cells are able to induce polarization of target astrocytes. Polarization of target astrocytes in response to immunological synapses may play an important role in regulating the outcome of the response of astrocytes to attacking effector T cells, whether during antiviral (e.g. infected during HIV, HTLV-1, HSV-1 or LCMV

  1. Stability and Function of Hippocampal Mossy Fiber Synapses Depend on Bcl11b/Ctip2

    Directory of Open Access Journals (Sweden)

    Elodie De Bruyckere

    2018-04-01

    Full Text Available Structural and functional plasticity of synapses are critical neuronal mechanisms underlying learning and memory. While activity-dependent regulation of synaptic strength has been extensively studied, much less is known about the transcriptional control of synapse maintenance and plasticity. Hippocampal mossy fiber (MF synapses connect dentate granule cells to CA3 pyramidal neurons and are important for spatial memory formation and consolidation. The transcription factor Bcl11b/Ctip2 is expressed in dentate granule cells and required for postnatal hippocampal development. Ablation of Bcl11b/Ctip2 in the adult hippocampus results in impaired adult neurogenesis and spatial memory. The molecular mechanisms underlying the behavioral impairment remained unclear. Here we show that selective deletion of Bcl11b/Ctip2 in the adult mouse hippocampus leads to a rapid loss of excitatory synapses in CA3 as well as reduced ultrastructural complexity of remaining mossy fiber boutons (MFBs. Moreover, a dramatic decline of long-term potentiation (LTP of the dentate gyrus-CA3 (DG-CA3 projection is caused by adult loss of Bcl11b/Ctip2. Differential transcriptomics revealed the deregulation of genes associated with synaptic transmission in mutants. Together, our data suggest Bcl11b/Ctip2 to regulate maintenance and function of MF synapses in the adult hippocampus.

  2. Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

    Directory of Open Access Journals (Sweden)

    Zedong Bi

    2016-08-01

    Full Text Available Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded, by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy.

  3. Delayed switching applied to memristor neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Frank Z.; Yang Xiao; Lim Guan [Future Computing Group, School of Computing, University of Kent, Canterbury (United Kingdom); Helian Na [School of Computer Science, University of Hertfordshire, Hatfield (United Kingdom); Wu Sining [Xyratex, Havant (United Kingdom); Guo Yike [Department of Computing, Imperial College, London (United Kingdom); Rashid, Md Mamunur [CERN, Geneva (Switzerland)

    2012-04-01

    Magnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the ''delayed switching effect.'' In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neurons.

  4. Delayed switching applied to memristor neural networks

    International Nuclear Information System (INIS)

    Wang, Frank Z.; Yang Xiao; Lim Guan; Helian Na; Wu Sining; Guo Yike; Rashid, Md Mamunur

    2012-01-01

    Magnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the ''delayed switching effect.'' In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neurons.

  5. Statistical Models for Social Networks

    NARCIS (Netherlands)

    Snijders, Tom A. B.; Cook, KS; Massey, DS

    2011-01-01

    Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

  6. Cadmium action in synapses in the brain

    Energy Technology Data Exchange (ETDEWEB)

    Minami, Akira; Takeda, Atsushi; Nishibaba, Daisuke; Tekefuta, Sachiyo; Oku, Naoto [Department of Radiobiochemistry, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka (Japan)

    2001-05-01

    Chronic exposure to cadmium causes central nervous system disorders, e.g., olfactory dysfunction. To clarify cadmium toxicity in synaptic neurotransmission in the brain, the movement and action of cadmium in the synapses was examined using in vivo microdialysis. One and 24 h after injection of {sup 109}CdCl{sub 2} into the amygdala of rats, {sup 109}Cd release into the extracellular space was facilitated by stimulation with high K{sup +}, suggesting that cadmium taken up in amygdalar neurons is released into the synaptic clefts in a calcium- and impulse-dependent manner. To examine the action of cadmium in the synapses, the amygdala was perfused with artificial cerebrospinal fluid containing 10-30 {mu}M CdCl{sub 2}. The release of excitatory neurotransmitters, i.e., glutamate and aspartate, into the extracellular space was decreased during perfusion with cadmium, while the release of inhibitory neurotransmitters, i.e., glycine and {gamma}-amino butyric acid (GABA), into the extracellular space was increased during the period. These results suggest that cadmium released from the amygdalar neuron terminals affects the degree and balance of excitation-inhibition in synaptic neurotransmission. (author)

  7. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  8. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

  9. Spatial distribution of excitatory synapses on the dendrites of ganglion cells in the mouse retina.

    Directory of Open Access Journals (Sweden)

    Yin-Peng Chen

    Full Text Available Excitatory glutamatergic inputs from bipolar cells affect the physiological properties of ganglion cells in the mammalian retina. The spatial distribution of these excitatory synapses on the dendrites of retinal ganglion cells thus may shape their distinct functions. To visualize the spatial pattern of excitatory glutamatergic input into the ganglion cells in the mouse retina, particle-mediated gene transfer of plasmids expressing postsynaptic density 95-green fluorescent fusion protein (PSD95-GFP was used to label the excitatory synapses. Despite wide variation in the size and morphology of the retinal ganglion cells, the expression of PSD95 puncta was found to follow two general rules. Firstly, the PSD95 puncta are regularly spaced, at 1-2 µm intervals, along the dendrites, whereby the presence of an excitatory synapse creates an exclusion zone that rules out the presence of other glutamatergic synaptic inputs. Secondly, the spatial distribution of PSD95 puncta on the dendrites of diverse retinal ganglion cells are similar in that the number of excitatory synapses appears to be less on primary dendrites and to increase to a plateau on higher branch order dendrites. These observations suggest that synaptogenesis is spatially regulated along the dendritic segments and that the number of synaptic contacts is relatively constant beyond the primary dendrites. Interestingly, we also found that the linear puncta density is slightly higher in large cells than in small cells. This may suggest that retinal ganglion cells with a large dendritic field tend to show an increased connectivity of excitatory synapses that makes up for their reduced dendrite density. Mapping the spatial distribution pattern of the excitatory synapses on retinal ganglion cells thus provides explicit structural information that is essential for our understanding of how excitatory glutamatergic inputs shape neuronal responses.

  10. DLGS97/SAP97 is developmentally upregulated and is required for complex adult behaviors and synapse morphology and function.

    Science.gov (United States)

    Mendoza-Topaz, Carolina; Urra, Francisco; Barría, Romina; Albornoz, Valeria; Ugalde, Diego; Thomas, Ulrich; Gundelfinger, Eckart D; Delgado, Ricardo; Kukuljan, Manuel; Sanxaridis, Parthena D; Tsunoda, Susan; Ceriani, M Fernanda; Budnik, Vivian; Sierralta, Jimena

    2008-01-02

    The synaptic membrane-associated guanylate kinase (MAGUK) scaffolding protein family is thought to play key roles in synapse assembly and synaptic plasticity. Evidence supporting these roles in vivo is scarce, as a consequence of gene redundancy in mammals. The genome of Drosophila contains only one MAGUK gene, discs large (dlg), from which two major proteins originate: DLGA [PSD95 (postsynaptic density 95)-like] and DLGS97 [SAP97 (synapse-associated protein)-like]. These differ only by the inclusion in DLGS97 of an L27 domain, important for the formation of supramolecular assemblies. Known dlg mutations affect both forms and are lethal at larval stages attributable to tumoral overgrowth of epithelia. We generated independent null mutations for each, dlgA and dlgS97. These allowed unveiling of a shift in expression during the development of the nervous system: predominant expression of DLGA in the embryo, balanced expression of both during larval stages, and almost exclusive DLGS97 expression in the adult brain. Loss of embryonic DLGS97 does not alter the development of the nervous system. At larval stages, DLGA and DLGS97 fulfill both unique and partially redundant functions in the neuromuscular junction. Contrary to dlg and dlgA mutants, dlgS97 mutants are viable to adulthood, but they exhibit marked alterations in complex behaviors such as phototaxis, circadian activity, and courtship, whereas simpler behaviors like locomotion and odor and light perception are spared. We propose that the increased repertoire of associations of a synaptic scaffold protein given by an additional domain of protein-protein interaction underlies its ability to integrate molecular networks required for complex functions in adult synapses.

  11. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  12. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  13. Network bandwidth utilization forecast model on high bandwidth networks

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-03-30

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  14. NMDAR-mediated calcium transients elicited by glutamate co-release at developing inhibitory synapses

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    Abigail Kalmbach

    2010-07-01

    Full Text Available Before hearing onset, the topographic organization of the inhibitory sound localization pathway from the medial nucleus of the trapezoid body (MNTB to the lateral superior olive (LSO is refined by means of synaptic silencing and strengthening. During this refinement period MNTB-LSO synapses not only release GABA and glycine but also release glutamate. This co-released glutamate can elicit postsynaptic currents that are predominantly mediated by NMDA receptors (NMDARs. To gain a better understanding of how glutamate contributes to synaptic signaling at developing MNTB-LSO inhibitory synapse, we investigated to what degree and under what conditions NMDARs contribute to postsynaptic calcium responses. Our results demonstrate that MNTB-LSO synapses can elicit compartmentalized calcium responses along aspiny LSO dendrites. These responses are significantly attenuated by the NMDARs antagonist APV. APV, however, has no effect on somatically recorded electrical postsynaptic responses, indicating little, if any, contribution of NMDARs to spike generation. Small NMDAR-mediated calcium responses were also observed under physiological levels of extracellular magnesium concentrations indicating that MNTB-LSO synapses activate magnesium sensitive NMDAR on immature LSO dendrites. In Fura-2 AM loaded neurons, blocking GABAA and glycine receptors decreased NMDAR contribution to somatic calcium responses suggesting that GABA and glycine, perhaps by shunting backpropagating action potentials, decrease the level of NMDAR activation under strong stimulus conditions.

  15. RMBNToolbox: random models for biochemical networks

    Directory of Open Access Journals (Sweden)

    Niemi Jari

    2007-05-01

    Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

  16. Learning Discloses Abnormal Structural and Functional Plasticity at Hippocampal Synapses in the APP23 Mouse Model of Alzheimer's Disease

    Science.gov (United States)

    Middei, Silvia; Roberto, Anna; Berretta, Nicola; Panico, Maria Beatrice; Lista, Simone; Bernardi, Giorgio; Mercuri, Nicola B.; Ammassari-Teule, Martine; Nistico, Robert

    2010-01-01

    B6-Tg/Thy1APP23Sdz (APP23) mutant mice exhibit neurohistological hallmarks of Alzheimer's disease but show intact basal hippocampal neurotransmission and synaptic plasticity. Here, we examine whether spatial learning differently modifies the structural and electrophysiological properties of hippocampal synapses in APP23 and wild-type mice. While…

  17. In Vitro Reconstruction of Neuronal Networks Derived from Human iPS Cells Using Microfabricated Devices.

    Directory of Open Access Journals (Sweden)

    Yuzo Takayama

    Full Text Available Morphology and function of the nervous system is maintained via well-coordinated processes both in central and peripheral nervous tissues, which govern the homeostasis of organs/tissues. Impairments of the nervous system induce neuronal disorders such as peripheral neuropathy or cardiac arrhythmia. Although further investigation is warranted to reveal the molecular mechanisms of progression in such diseases, appropriate model systems mimicking the patient-specific communication between neurons and organs are not established yet. In this study, we reconstructed the neuronal network in vitro either between neurons of the human induced pluripotent stem (iPS cell derived peripheral nervous system (PNS and central nervous system (CNS, or between PNS neurons and cardiac cells in a morphologically and functionally compartmentalized manner. Networks were constructed in photolithographically microfabricated devices with two culture compartments connected by 20 microtunnels. We confirmed that PNS and CNS neurons connected via synapses and formed a network. Additionally, calcium-imaging experiments showed that the bundles originating from the PNS neurons were functionally active and responded reproducibly to external stimuli. Next, we confirmed that CNS neurons showed an increase in calcium activity during electrical stimulation of networked bundles from PNS neurons in order to demonstrate the formation of functional cell-cell interactions. We also confirmed the formation of synapses between PNS neurons and mature cardiac cells. These results indicate that compartmentalized culture devices are promising tools for reconstructing network-wide connections between PNS neurons and various organs, and might help to understand patient-specific molecular and functional mechanisms under normal and pathological conditions.

  18. The space where aging acts: focus on the GABAergic synapse.

    Science.gov (United States)

    Rozycka, Aleksandra; Liguz-Lecznar, Monika

    2017-08-01

    As it was established that aging is not associated with massive neuronal loss, as was believed in the mid-20th Century, scientific interest has addressed the influence of aging on particular neuronal subpopulations and their synaptic contacts, which constitute the substrate for neural plasticity. Inhibitory neurons represent the most complex and diverse group of neurons, showing distinct molecular and physiological characteristics and possessing a compelling ability to control the physiology of neural circuits. This review focuses on the aging of GABAergic neurons and synapses. Understanding how aging affects synapses of particular neuronal subpopulations may help explain the heterogeneity of aging-related effects. We reviewed the literature concerning the effects of aging on the numbers of GABAergic neurons and synapses as well as aging-related alterations in their presynaptic and postsynaptic components. Finally, we discussed the influence of those changes on the plasticity of the GABAergic system, highlighting our results concerning aging in mouse somatosensory cortex and linking them to plasticity impairments and brain disorders. We posit that aging-induced impairments of the GABAergic system lead to an inhibitory/excitatory imbalance, thereby decreasing neuron's ability to respond with plastic changes to environmental and cellular challenges, leaving the brain more vulnerable to cognitive decline and damage by synaptopathic diseases. © 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

  19. Microorganism and filamentous fungi drive evolution of plant synapses.

    Science.gov (United States)

    Baluška, František; Mancuso, Stefano

    2013-01-01

    In the course of plant evolution, there is an obvious trend toward an increased complexity of plant bodies, as well as an increased sophistication of plant behavior and communication. Phenotypic plasticity of plants is based on the polar auxin transport machinery that is directly linked with plant sensory systems impinging on plant behavior and adaptive responses. Similar to the emergence and evolution of eukaryotic cells, evolution of land plants was also shaped and driven by infective and symbiotic microorganisms. These microorganisms are the driving force behind the evolution of plant synapses and other neuronal aspects of higher plants; this is especially pronounced in the root apices. Plant synapses allow synaptic cell-cell communication and coordination in plants, as well as sensory-motor integration in root apices searching for water and mineral nutrition. These neuronal aspects of higher plants are closely linked with their unique ability to adapt to environmental changes.

  20. Simulations of centriole of polarized centrosome as a monopole antenna in immune and viral synapses.

    Science.gov (United States)

    Dvorak, Josef; Melichar, Bohuslav; Filipova, Alzbeta; Grimova, Jana; Grimova, Nela; Rozsypalova, Aneta; Buka, David; Voboril, Rene; Zapletal, Radek; Buchler, Tomas; Richter, Igor; Buka, David

    2018-01-01

    The immune synapse (IS) is a temporary interface between an antigen-presenting cell and an effector lymphocyte. Viral synapse is a molecularly organized cellular junction that is structurally similar to the IS. Primary cilium is considered as a functional homologue of the IS due to the morphological and functional similarities in architecture between both micotubule structures. It has been hypothesized that endogenous electromagnetic field in the cell is generated by a unique cooperating system between mitochondria and microtubules. We are extending this prior hypothesis of the endogenous electromagnetic field in the cell postulating that polarized centriole in immune and viral synapse could serve as a monopole antenna. This is an addition to our hypothesis that primary cilium could serve as a monopole antenna. We simulated the distribution of electric field of centriole of polarized centrosome as a monopole antenna in immune and viral synapse. Very weak electromagnetic field of polarized centriole of CD8+ T lymphocyte in IS can contribute to the transport of cytolytic granules into the attacked (cancer) cell. Analogically, very weak electromagnetic field of polarized centriole in viral synapse of infected CD4 cells can aid the transport of viruses (human immunodeficiency virus) to non-infected CD4 cells. We hypothesized that healthy organisms need these monopole antennas. If, during the neoplastic transformation, healthy cells lose monopole antennas in form of primary cilia, the IS aims to replace them by monopole antennas of polarized centrioles in IS to restore homeostasis.

  1. A Markovian event-based framework for stochastic spiking neural networks.

    Science.gov (United States)

    Touboul, Jonathan D; Faugeras, Olivier D

    2011-11-01

    In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

  2. Despite disorganized synapse structure, Th2 cells maintain directional delivery of CD40L to antigen-presenting B cells.

    Science.gov (United States)

    Gardell, Jennifer L; Parker, David C

    2017-01-01

    Upon recognition of peptide displayed on MHC molecules, Th1 and Th2 cells form distinct immunological synapse structures. Th1 cells have a bull's eye synapse structure with TCR/ MHC-peptide interactions occurring central to a ring of adhesion molecules, while Th2 cells have a multifocal synapse with small clusters of TCR/MHC interactions throughout the area of T cell/antigen-presenting cell interaction. In this study, we investigated whether this structural difference in the immunological synapse affects delivery of T cell help. The immunological synapse is thought to ensure antigen-specific delivery of cytolytic granules and killing of target cells by NK cells and cytolytic T cells. In helper T cells, it has been proposed that the immunological synapse may direct delivery of other effector molecules including cytokines. CD40 ligand (CD40L) is a membrane-bound cytokine essential for antigen-specific T cell help for B cells in the antibody response. We incubated Th1 and Th2 cells overnight with a mixture of antigen-presenting and bystander B cells, and the delivery of CD40L to B cells and subsequent B cell responses were compared. Despite distinct immunological synapse structures, Th1 and Th2 cell do not differ in their ability to deliver CD40L and T cell help in an antigen-specific fashion, or in their susceptibility to inhibition of help by a blocking anti-CD40L antibody.

  3. Visualization by high resolution immunoelectron microscopy of the transient receptor potential vanilloid-1 at inhibitory synapses of the mouse dentate gyrus.

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    Miren-Josune Canduela

    Full Text Available We have recently shown that the transient receptor potential vanilloid type 1 (TRPV1, a non-selective cation channel in the peripheral and central nervous system, is localized at postsynaptic sites of the excitatory perforant path synapses in the hippocampal dentate molecular layer (ML. In the present work, we have studied the distribution of TRPV1 at inhibitory synapses in the ML. With this aim, a preembedding immunogold method for high resolution electron microscopy was applied to mouse hippocampus. About 30% of the inhibitory synapses in the ML are TRPV1 immunopositive, which is mostly localized perisynaptically (∼60% of total immunoparticles at postsynaptic dendritic membranes receiving symmetric synapses in the inner 1/3 of the layer. This TRPV1 pattern distribution is not observed in the ML of TRPV1 knock-out mice. These findings extend the knowledge of the subcellular localization of TRPV1 to inhibitory synapses of the dentate molecular layer where the channel, in addition to excitatory synapses, is present.

  4. Label-free visualization of ultrastructural features of artificial synapses via cryo-EM.

    Science.gov (United States)

    Gopalakrishnan, Gopakumar; Yam, Patricia T; Madwar, Carolin; Bostina, Mihnea; Rouiller, Isabelle; Colman, David R; Lennox, R Bruce

    2011-12-21

    The ultrastructural details of presynapses formed between artificial substrates of submicrometer silica beads and hippocampal neurons are visualized via cryo-electron microscopy (cryo-EM). The silica beads are derivatized by poly-d-lysine or lipid bilayers. Molecular features known to exist at presynapses are clearly present at these artificial synapses, as visualized by cryo-EM. Key synaptic features such as the membrane contact area at synaptic junctions, the presynaptic bouton containing presynaptic vesicles, as well as microtubular structures can be identified. This is the first report of the direct, label-free observation of ultrastructural details of artificial synapses.

  5. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  6. Synapses of the rat end brain in response to flight effects

    International Nuclear Information System (INIS)

    Antipov, V.V.; Tikhonchuk, V.S.; Ushakov, I.B.; Fedorov, V.P.

    1988-01-01

    Using electron microscopy, synapses of different structures of the rat end brain related to cognitive and motor acts (sensorimotor cortex, caudate nucleus) as well as memory and behavior (hippocampus) were examined. Rats were exposed to ionizing radiation, superhigh frequency, hypoxia, hyperoxia, vibration and acceleration (applied separately or in combination) which have been traditionally in the focus of space and aviation medicine. Brain internuronal junctions were found to be very sensitive to the above effects, particularly ionizing radiation and hypoxia. Conversely, synapses were shown to be highly resistant to short-term hyperoxia and electromagnetic radiation. When combined effects were used, response of interneuronal junctions depended on the irradiation dose and order of application of radiation and other flight factors

  7. Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

    Directory of Open Access Journals (Sweden)

    Saket Navlakha

    2015-07-01

    Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

  8. The effect of STDP temporal kernel structure on the learning dynamics of single excitatory and inhibitory synapses.

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    Yotam Luz

    Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.

  9. SynGAP regulates protein synthesis and homeostatic synaptic plasticity in developing cortical networks.

    Directory of Open Access Journals (Sweden)

    Chih-Chieh Wang

    Full Text Available Disrupting the balance between excitatory and inhibitory neurotransmission in the developing brain has been causally linked with intellectual disability (ID and autism spectrum disorders (ASD. Excitatory synapse strength is regulated in the central nervous system by controlling the number of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs. De novo genetic mutations of the synaptic GTPase-activating protein (SynGAP are associated with ID and ASD. SynGAP is enriched at excitatory synapses and genetic suppression of SynGAP increases excitatory synaptic strength. However, exactly how SynGAP acts to maintain synaptic AMPAR content is unclear. We show here that SynGAP limits excitatory synaptic strength, in part, by suppressing protein synthesis in cortical neurons. The data presented here from in vitro, rat and mouse cortical networks, demonstrate that regulation of translation by SynGAP involves ERK, mTOR, and the small GTP-binding protein Rheb. Furthermore, these data show that GluN2B-containing NMDARs and the cognitive kinase CaMKII act upstream of SynGAP and that this signaling cascade is required for proper translation-dependent homeostatic synaptic plasticity of excitatory synapses in developing cortical networks.

  10. Generalized Network Psychometrics : Combining Network and Latent Variable Models

    NARCIS (Netherlands)

    Epskamp, S.; Rhemtulla, M.; Borsboom, D.

    2017-01-01

    We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

  11. Changes in Properties of Auditory Nerve Synapses following Conductive Hearing Loss.

    Science.gov (United States)

    Zhuang, Xiaowen; Sun, Wei; Xu-Friedman, Matthew A

    2017-01-11

    Auditory activity plays an important role in the development of the auditory system. Decreased activity can result from conductive hearing loss (CHL) associated with otitis media, which may lead to long-term perceptual deficits. The effects of CHL have been mainly studied at later stages of the auditory pathway, but early stages remain less examined. However, changes in early stages could be important because they would affect how information about sounds is conveyed to higher-order areas for further processing and localization. We examined the effects of CHL at auditory nerve synapses onto bushy cells in the mouse anteroventral cochlear nucleus following occlusion of the ear canal. These synapses, called endbulbs of Held, normally show strong depression in voltage-clamp recordings in brain slices. After 1 week of CHL, endbulbs showed even greater depression, reflecting higher release probability. We observed no differences in quantal size between control and occluded mice. We confirmed these observations using mean-variance analysis and the integration method, which also revealed that the number of release sites decreased after occlusion. Consistent with this, synaptic puncta immunopositive for VGLUT1 decreased in area after occlusion. The level of depression and number of release sites both showed recovery after returning to normal conditions. Finally, bushy cells fired fewer action potentials in response to evoked synaptic activity after occlusion, likely because of increased depression and decreased input resistance. These effects appear to reflect a homeostatic, adaptive response of auditory nerve synapses to reduced activity. These effects may have important implications for perceptual changes following CHL. Normal hearing is important to everyday life, but abnormal auditory experience during development can lead to processing disorders. For example, otitis media reduces sound to the ear, which can cause long-lasting deficits in language skills and verbal

  12. Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

    Science.gov (United States)

    Hjorth, Johannes J J; Dawitz, Julia; Kroon, Tim; Pires, Johny; Dassen, Valerie J; Berkhout, Janna A; Emperador Melero, Javier; Nadadhur, Aish G; Alevra, Mihai; Toonen, Ruud F; Heine, Vivi M; Mansvelder, Huibert D; Meredith, Rhiannon M

    2016-04-01

    Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers. © 2015 Wiley Periodicals, Inc.

  13. Centriole polarisation to the immunological synapse directs secretion from cytolytic cells of both the innate and adaptive immune systems

    Directory of Open Access Journals (Sweden)

    Arico Maurizo

    2011-06-01

    Full Text Available Abstract Background Cytolytic cells of the immune system destroy pathogen-infected cells by polarised exocytosis of secretory lysosomes containing the pore-forming protein perforin. Precise delivery of this lethal hit is essential to ensuring that only the target cell is destroyed. In cytotoxic T lymphocytes (CTLs, this is accomplished by an unusual movement of the centrosome to contact the plasma membrane at the centre of the immunological synapse formed between killer and target cells. Secretory lysosomes are directed towards the centrosome along microtubules and delivered precisely to the point of target cell recognition within the immunological synapse, identified by the centrosome. We asked whether this mechanism of directing secretory lysosome release is unique to CTL or whether natural killer (NK and invariant NKT (iNKT cytolytic cells of the innate immune system use a similar mechanism to focus perforin-bearing lysosome release. Results NK cells were conjugated with B-cell targets lacking major histocompatibility complex class I 721.221 cells, and iNKT cells were conjugated with glycolipid-pulsed CD1-bearing targets, then prepared for thin-section electron microscopy. High-resolution electron micrographs of the immunological synapse formed between NK and iNKT cytolytic cells with their targets revealed that in both NK and iNKT cells, the centrioles could be found associated (or 'docked' with the plasma membrane within the immunological synapse. Secretory clefts were visible within the synapses formed by both NK and iNKT cells, and secretory lysosomes were polarised along microtubules leading towards the docked centrosome. The Golgi apparatus and recycling endosomes were also polarised towards the centrosome at the plasma membrane within the synapse. Conclusions These results reveal that, like CTLs of the adaptive immune system, the centrosomes of NK and iNKT cells (cytolytic cells of the innate immune system direct secretory lysosomes to

  14. Centriole polarisation to the immunological synapse directs secretion from cytolytic cells of both the innate and adaptive immune systems.

    Science.gov (United States)

    Stinchcombe, Jane C; Salio, Mariolina; Cerundolo, Vincenzo; Pende, Daniela; Arico, Maurizo; Griffiths, Gillian M

    2011-06-28

    Cytolytic cells of the immune system destroy pathogen-infected cells by polarised exocytosis of secretory lysosomes containing the pore-forming protein perforin. Precise delivery of this lethal hit is essential to ensuring that only the target cell is destroyed. In cytotoxic T lymphocytes (CTLs), this is accomplished by an unusual movement of the centrosome to contact the plasma membrane at the centre of the immunological synapse formed between killer and target cells. Secretory lysosomes are directed towards the centrosome along microtubules and delivered precisely to the point of target cell recognition within the immunological synapse, identified by the centrosome. We asked whether this mechanism of directing secretory lysosome release is unique to CTL or whether natural killer (NK) and invariant NKT (iNKT) cytolytic cells of the innate immune system use a similar mechanism to focus perforin-bearing lysosome release. NK cells were conjugated with B-cell targets lacking major histocompatibility complex class I 721.221 cells, and iNKT cells were conjugated with glycolipid-pulsed CD1-bearing targets, then prepared for thin-section electron microscopy. High-resolution electron micrographs of the immunological synapse formed between NK and iNKT cytolytic cells with their targets revealed that in both NK and iNKT cells, the centrioles could be found associated (or 'docked') with the plasma membrane within the immunological synapse. Secretory clefts were visible within the synapses formed by both NK and iNKT cells, and secretory lysosomes were polarised along microtubules leading towards the docked centrosome. The Golgi apparatus and recycling endosomes were also polarised towards the centrosome at the plasma membrane within the synapse. These results reveal that, like CTLs of the adaptive immune system, the centrosomes of NK and iNKT cells (cytolytic cells of the innate immune system) direct secretory lysosomes to the immunological synapse. Morphologically, the

  15. GLUT4 Mobilization Supports Energetic Demands of Active Synapses.

    Science.gov (United States)

    Ashrafi, Ghazaleh; Wu, Zhuhao; Farrell, Ryan J; Ryan, Timothy A

    2017-02-08

    The brain is highly sensitive to proper fuel availability as evidenced by the rapid decline in neuronal function during ischemic attacks and acute severe hypoglycemia. We previously showed that sustained presynaptic function requires activity-driven glycolysis. Here, we provide strong evidence that during action potential (AP) firing, nerve terminals rely on the glucose transporter GLUT4 as a glycolytic regulatory system to meet the activity-driven increase in energy demands. Activity at synapses triggers insertion of GLUT4 into the axonal plasma membrane driven by activation of the metabolic sensor AMP kinase. Furthermore, we show that genetic ablation of GLUT4 leads to an arrest of synaptic vesicle recycling during sustained AP firing, similar to what is observed during acute glucose deprivation. The reliance on this biochemical regulatory system for "exercising" synapses is reminiscent of that occurring in exercising muscle to sustain cellular function and identifies nerve terminals as critical sites of proper metabolic control. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Positioning of AMPA Receptor-Containing Endosomes Regulates Synapse Architecture

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    Marta Esteves da Silva

    2015-11-01

    Full Text Available Lateral diffusion in the membrane and endosomal trafficking both contribute to the addition and removal of AMPA receptors (AMPARs at postsynaptic sites. However, the spatial coordination between these mechanisms has remained unclear, because little is known about the dynamics of AMPAR-containing endosomes. In addition, how the positioning of AMPAR-containing endosomes affects synapse organization and functioning has never been directly explored. Here, we used live-cell imaging in hippocampal neuron cultures to show that intracellular AMPARs are transported in Rab11-positive recycling endosomes, which frequently enter dendritic spines and depend on the microtubule and actin cytoskeleton. By using chemically induced dimerization systems to recruit kinesin (KIF1C or myosin (MyosinV/VI motors to Rab11-positive recycling endosomes, we controlled their trafficking and found that induced removal of recycling endosomes from spines decreases surface AMPAR expression and PSD-95 clusters at synapses. Our data suggest a mechanistic link between endosome positioning and postsynaptic structure and composition.

  17. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    Science.gov (United States)

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  18. Regulation of Brain-Derived Neurotrophic Factor Exocytosis and Gamma-Aminobutyric Acidergic Interneuron Synapse by the Schizophrenia Susceptibility Gene Dysbindin-1.

    Science.gov (United States)

    Yuan, Qiang; Yang, Feng; Xiao, Yixin; Tan, Shawn; Husain, Nilofer; Ren, Ming; Hu, Zhonghua; Martinowich, Keri; Ng, Julia S; Kim, Paul J; Han, Weiping; Nagata, Koh-Ichi; Weinberger, Daniel R; Je, H Shawn

    2016-08-15

    Genetic variations in dystrobrevin binding protein 1 (DTNBP1 or dysbindin-1) have been implicated as risk factors in the pathogenesis of schizophrenia. The encoded protein dysbindin-1 functions in the regulation of synaptic activity and synapse development. Intriguingly, a loss of function mutation in Dtnbp1 in mice disrupted both glutamatergic and gamma-aminobutyric acidergic transmission in the cerebral cortex; pyramidal neurons displayed enhanced excitability due to reductions in inhibitory synaptic inputs. However, the mechanism by which reduced dysbindin-1 activity causes inhibitory synaptic deficits remains unknown. We investigated the role of dysbindin-1 in the exocytosis of brain-derived neurotrophic factor (BDNF) from cortical excitatory neurons, organotypic brain slices, and acute slices from dysbindin-1 mutant mice and determined how this change in BDNF exocytosis transsynaptically affected the number of inhibitory synapses formed on excitatory neurons via whole-cell recordings, immunohistochemistry, and live-cell imaging using total internal reflection fluorescence microscopy. A decrease in dysbindin-1 reduces the exocytosis of BDNF from cortical excitatory neurons, and this reduction in BDNF exocytosis transsynaptically resulted in reduced inhibitory synapse numbers formed on excitatory neurons. Furthermore, application of exogenous BDNF rescued the inhibitory synaptic deficits caused by the reduced dysbindin-1 level in both cultured cortical neurons and slice cultures. Taken together, our results demonstrate that these two genes linked to risk for schizophrenia (BDNF and dysbindin-1) function together to regulate interneuron development and cortical network activity. This evidence supports the investigation of the association between dysbindin-1 and BDNF in humans with schizophrenia. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  19. Eight challenges for network epidemic models

    Directory of Open Access Journals (Sweden)

    Lorenzo Pellis

    2015-03-01

    Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

  20. Complex networks-based energy-efficient evolution model for wireless sensor networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)

    2009-08-30

    Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

  1. Complex networks-based energy-efficient evolution model for wireless sensor networks

    International Nuclear Information System (INIS)

    Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun

    2009-01-01

    Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

  2. MET receptor tyrosine kinase controls dendritic complexity, spine morphogenesis, and glutamatergic synapse maturation in the hippocampus.

    Science.gov (United States)

    Qiu, Shenfeng; Lu, Zhongming; Levitt, Pat

    2014-12-03

    The MET receptor tyrosine kinase (RTK), implicated in risk for autism spectrum disorder (ASD) and in functional and structural circuit integrity in humans, is a temporally and spatially regulated receptor enriched in dorsal pallial-derived structures during mouse forebrain development. Here we report that loss or gain of function of MET in vitro or in vivo leads to changes, opposite in nature, in dendritic complexity, spine morphogenesis, and the timing of glutamatergic synapse maturation onto hippocampus CA1 neurons. Consistent with the morphological and biochemical changes, deletion of Met in mutant mice results in precocious maturation of excitatory synapse, as indicated by a reduction of the proportion of silent synapses, a faster GluN2A subunit switch, and an enhanced acquisition of AMPA receptors at synaptic sites. Thus, MET-mediated signaling appears to serve as a mechanism for controlling the timing of neuronal growth and functional maturation. These studies suggest that mistimed maturation of glutamatergic synapses leads to the aberrant neural circuits that may be associated with ASD risk. Copyright © 2014 the authors 0270-6474/14/3416166-14$15.00/0.

  3. Brand Marketing Model on Social Networks

    Directory of Open Access Journals (Sweden)

    Jolita Jezukevičiūtė

    2014-04-01

    Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

  4. Introducing Synchronisation in Deterministic Network Models

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.

    2006-01-01

    The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...

  5. High resolution in situ zymography reveals matrix metalloproteinase activity at glutamatergic synapses.

    Science.gov (United States)

    Gawlak, M; Górkiewicz, T; Gorlewicz, A; Konopacki, F A; Kaczmarek, L; Wilczynski, G M

    2009-01-12

    Synaptic plasticity involves remodeling of extracellular matrix. This is mediated, in part, by enzymes of the matrix metalloproteinase (MMP) family, in particular by gelatinase MMP-9. Accordingly, there is a need of developing methods to visualize gelatinolytic activity at the level of individual synapses, especially in the context of neurotransmitters receptors. Here we present a high-resolution fluorescent in situ zymography (ISZ), performed in thin sections of the alcohol-fixed and polyester wax-embedded brain tissue of the rat (Rattus norvegicus), which is superior to the current ISZ protocols. The method allows visualization of structural details up to the resolution-limit of light microscopy, in conjunction with immunofluorescent labeling. We used this technique to visualize and quantify gelatinolytic activity at the synapses in control and seizure-affected rat brain. In particular, we demonstrated, for the first time, frequent colocalization of gelatinase(s) with synaptic N-methyl-D-aspartic acid (NMDA)- and AMPA-type glutamate receptors. We believe that our method represents a valuable tool to study extracellular proteolytic processes at the synapses, it could be used, as well, to investigate proteinase involvement in a range of physiological and pathological phenomena in the nervous system.

  6. Remodelling at the calyx of Held-MNTB synapse in mice developing with unilateral conductive hearing loss.

    Science.gov (United States)

    Grande, Giovanbattista; Negandhi, Jaina; Harrison, Robert V; Wang, Lu-Yang

    2014-04-01

    Structure and function of central synapses are profoundly influenced by experience during developmental sensitive periods. Sensory synapses, which are the indispensable interface for the developing brain to interact with its environment, are particularly plastic. In the auditory system, moderate forms of unilateral hearing loss during development are prevalent but the pre- and postsynaptic modifications that occur when hearing symmetry is perturbed are not well understood. We investigated this issue by performing experiments at the large calyx of Held synapse. Principal neurons of the medial nucleus of the trapezoid body (MNTB) are innervated by calyx of Held terminals that originate from the axons of globular bushy cells located in the contralateral ventral cochlear nucleus. We compared populations of synapses in the same animal that were either sound deprived (SD) or sound experienced (SE) after unilateral conductive hearing loss (CHL). Middle ear ossicles were removed 1 week prior to hearing onset (approx. postnatal day (P) 12) and morphological and electrophysiological approaches were applied to auditory brainstem slices taken from these mice at P17-19. Calyces in the SD and SE MNTB acquired their mature digitated morphology but these were structurally more complex than those in normal hearing mice. This was accompanied by bilateral decreases in initial EPSC amplitude and synaptic conductance despite the CHL being unilateral. During high-frequency stimulation, some SD synapses displayed short-term depression whereas others displayed short-term facilitation followed by slow depression similar to the heterogeneities observed in normal hearing mice. However SE synapses predominantly displayed short-term facilitation followed by slow depression which could be explained in part by the decrease in release probability. Furthermore, the excitability of principal cells in the SD MNTB had increased significantly. Despite these unilateral changes in short-term plasticity

  7. Persistent long-term facilitation at an identified synapse becomes labile with activation of short-term heterosynaptic plasticity.

    Science.gov (United States)

    Hu, Jiang-Yuan; Schacher, Samuel

    2014-04-02

    Short-term and long-term synaptic plasticity are cellular correlates of learning and memory of different durations. Little is known, however, how these two forms of plasticity interact at the same synaptic connection. We examined the reciprocal impact of short-term heterosynaptic or homosynaptic plasticity at sensorimotor synapses of Aplysia in cell culture when expressing persistent long-term facilitation (P-LTF) evoked by serotonin [5-hydroxytryptamine (5-HT)]. Short-term heterosynaptic plasticity induced by 5-HT (facilitation) or the neuropeptide FMRFa (depression) and short-term homosynaptic plasticity induced by tetanus [post-tetanic potentiation (PTP)] or low-frequency stimulation [homosynaptic depression (HSD)] of the sensory neuron were expressed in both control synapses and synapses expressing P-LTF in the absence or presence of protein synthesis inhibitors. All forms of short-term plasticity failed to significantly affect ongoing P-LTF in the absence of protein synthesis inhibitors. However, P-LTF reversed to control levels when either 5-HT or FMRFa was applied in the presence of rapamycin. In contrast, P-LTF was unaffected when either PTP or HSD was evoked in the presence of either rapamycin or anisomycin. These results indicate that synapses expressing persistent plasticity acquire a "new" baseline and functionally express short-term changes as naive synapses, but the new baseline becomes labile following selective activations-heterosynaptic stimuli that evoke opposite forms of plasticity-such that when presented in the presence of protein synthesis inhibitors produce a rapid reversal of the persistent plasticity. Activity-selective induction of a labile state at synapses expressing persistent plasticity may facilitate the development of therapies for reversing inappropriate memories.

  8. Modeling Network Interdiction Tasks

    Science.gov (United States)

    2015-09-17

    118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

  9. Long-term memory stabilized by noise-induced rehearsal.

    Science.gov (United States)

    Wei, Yi; Koulakov, Alexei A

    2014-11-19

    Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. Copyright © 2014 the authors 0270-6474/14/3415804-12$15.00/0.

  10. Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

    CERN Document Server

    Santi, Paolo

    2012-01-01

    Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

  11. Complex Networks in Psychological Models

    Science.gov (United States)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  12. Retrograde Signaling from Progranulin to Sort1 Counteracts Synapse Elimination in the Developing Cerebellum.

    Science.gov (United States)

    Uesaka, Naofumi; Abe, Manabu; Konno, Kohtarou; Yamazaki, Maya; Sakoori, Kazuto; Watanabe, Takaki; Kao, Tzu-Huei; Mikuni, Takayasu; Watanabe, Masahiko; Sakimura, Kenji; Kano, Masanobu

    2018-02-21

    Elimination of redundant synapses formed early in development and strengthening of necessary connections are crucial for shaping functional neural circuits. Purkinje cells (PCs) in the neonatal cerebellum are innervated by multiple climbing fibers (CFs) with similar strengths. A single CF is strengthened whereas the other CFs are eliminated in each PC during postnatal development. The underlying mechanisms, particularly for the strengthening of single CFs, are poorly understood. Here we report that progranulin, a multi-functional growth factor implicated in the pathogenesis of frontotemporal dementia, strengthens developing CF synaptic inputs and counteracts their elimination from postnatal day 11 to 16. Progranulin derived from PCs acts retrogradely onto its putative receptor Sort1 on CFs. This effect is independent of semaphorin 3A, another retrograde signaling molecule that counteracts CF synapse elimination. We propose that progranulin-Sort1 signaling strengthens and maintains developing CF inputs, and may contribute to selection of single "winner" CFs that survive synapse elimination. Copyright © 2018 Elsevier Inc. All rights reserved.

  13. GABAergic Synapses at the Axon Initial Segment of Basolateral Amygdala Projection Neurons Modulate Fear Extinction.

    Science.gov (United States)

    Saha, Rinki; Knapp, Stephanie; Chakraborty, Darpan; Horovitz, Omer; Albrecht, Anne; Kriebel, Martin; Kaphzan, Hanoch; Ehrlich, Ingrid; Volkmer, Hansjürgen; Richter-Levin, Gal

    2017-01-01

    Inhibitory synaptic transmission in the amygdala has a pivotal role in fear learning and its extinction. However, the local circuits formed by GABAergic inhibitory interneurons within the amygdala and their detailed function in shaping these behaviors are not well understood. Here we used lentiviral-mediated knockdown of the cell adhesion molecule neurofascin in the basolateral amygdala (BLA) to specifically remove inhibitory synapses at the axon initial segment (AIS) of BLA projection neurons. Quantitative analysis of GABAergic synapse markers and measurement of miniature inhibitory postsynaptic currents in BLA projection neurons after neurofascin knockdown ex vivo confirmed the loss of GABAergic input. We then studied the impact of this manipulation on anxiety-like behavior and auditory cued fear conditioning and its extinction as BLA related behavioral paradigms, as well as on long-term potentiation (LTP) in the ventral subiculum-BLA pathway in vivo. BLA knockdown of neurofascin impaired ventral subiculum-BLA-LTP. While this manipulation did not affect anxiety-like behavior and fear memory acquisition and consolidation, it specifically impaired extinction. Our findings indicate that modification of inhibitory synapses at the AIS of BLA projection neurons is sufficient to selectively impair extinction behavior. A better understanding of the role of distinct GABAergic synapses may provide novel and more specific targets for therapeutic interventions in extinction-based therapies.

  14. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  15. Chimera states in a multilayer network of coupled and uncoupled neurons

    Science.gov (United States)

    Majhi, Soumen; Perc, Matjaž; Ghosh, Dibakar

    2017-07-01

    We study the emergence of chimera states in a multilayer neuronal network, where one layer is composed of coupled and the other layer of uncoupled neurons. Through the multilayer structure, the layer with coupled neurons acts as the medium by means of which neurons in the uncoupled layer share information in spite of the absence of physical connections among them. Neurons in the coupled layer are connected with electrical synapses, while across the two layers, neurons are connected through chemical synapses. In both layers, the dynamics of each neuron is described by the Hindmarsh-Rose square wave bursting dynamics. We show that the presence of two different types of connecting synapses within and between the two layers, together with the multilayer network structure, plays a key role in the emergence of between-layer synchronous chimera states and patterns of synchronous clusters. In particular, we find that these chimera states can emerge in the coupled layer regardless of the range of electrical synapses. Even in all-to-all and nearest-neighbor coupling within the coupled layer, we observe qualitatively identical between-layer chimera states. Moreover, we show that the role of information transmission delay between the two layers must not be neglected, and we obtain precise parameter bounds at which chimera states can be observed. The expansion of the chimera region and annihilation of cluster and fully coherent states in the parameter plane for increasing values of inter-layer chemical synaptic time delay are illustrated using effective range measurements. These results are discussed in the light of neuronal evolution, where the coexistence of coherent and incoherent dynamics during the developmental stage is particularly likely.

  16. Gossip spread in social network Models

    Science.gov (United States)

    Johansson, Tobias

    2017-04-01

    Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

  17. Connexin-Dependent Neuroglial Networking as a New Therapeutic Target.

    Science.gov (United States)

    Charvériat, Mathieu; Naus, Christian C; Leybaert, Luc; Sáez, Juan C; Giaume, Christian

    2017-01-01

    Astrocytes and neurons dynamically interact during physiological processes, and it is now widely accepted that they are both organized in plastic and tightly regulated networks. Astrocytes are connected through connexin-based gap junction channels, with brain region specificities, and those networks modulate neuronal activities, such as those involved in sleep-wake cycle, cognitive, or sensory functions. Additionally, astrocyte domains have been involved in neurogenesis and neuronal differentiation during development; they participate in the "tripartite synapse" with both pre-synaptic and post-synaptic neurons by tuning down or up neuronal activities through the control of neuronal synaptic strength. Connexin-based hemichannels are also involved in those regulations of neuronal activities, however, this feature will not be considered in the present review. Furthermore, neuronal processes, transmitting electrical signals to chemical synapses, stringently control astroglial connexin expression, and channel functions. Long-range energy trafficking toward neurons through connexin-coupled astrocytes and plasticity of those networks are hence largely dependent on neuronal activity. Such reciprocal interactions between neurons and astrocyte networks involve neurotransmitters, cytokines, endogenous lipids, and peptides released by neurons but also other brain cell types, including microglial and endothelial cells. Over the past 10 years, knowledge about neuroglial interactions has widened and now includes effects of CNS-targeting drugs such as antidepressants, antipsychotics, psychostimulants, or sedatives drugs as potential modulators of connexin function and thus astrocyte networking activity. In physiological situations, neuroglial networking is consequently resulting from a two-way interaction between astrocyte gap junction-mediated networks and those made by neurons. As both cell types are modulated by CNS drugs we postulate that neuroglial networking may emerge as

  18. Short-term modern life-like stress exacerbates Aβ-pathology and synapse loss in 3xTg-AD mice.

    Science.gov (United States)

    Baglietto-Vargas, David; Chen, Yuncai; Suh, Dongjin; Ager, Rahasson R; Rodriguez-Ortiz, Carlos J; Medeiros, Rodrigo; Myczek, Kristoffer; Green, Kim N; Baram, Tallie Z; LaFerla, Frank M

    2015-09-01

    Alzheimer's disease (AD) is a progressive neurological disorder that impairs memory and other cognitive functions in the elderly. The social and financial impacts of AD are overwhelming and are escalating exponentially as a result of population aging. Therefore, identifying AD-related risk factors and the development of more efficacious therapeutic approaches are critical to cure this neurological disorder. Current epidemiological evidence indicates that life experiences, including chronic stress, are a risk for AD. However, it is unknown if short-term stress, lasting for hours, influences the onset or progression of AD. Here, we determined the effect of short-term, multi-modal 'modern life-like' stress on AD pathogenesis and synaptic plasticity in mice bearing three AD mutations (the 3xTg-AD mouse model). We found that combined emotional and physical stress lasting 5 h severely impaired memory in wild-type mice and tended to impact it in already low-performing 3xTg-AD mice. This stress reduced the number of synapse-bearing dendritic spines in 3xTg-AD mice and increased Aβ levels by augmenting AβPP processing. Thus, short-term stress simulating modern-life conditions may exacerbate cognitive deficits in preclinical AD by accelerating amyloid pathology and reducing synapse numbers. Epidemiological evidence indicates that life experiences, including chronic stress, are a risk for Alzheimer disease (AD). However, it is unknown if short stress in the range of hours influences the onset or progression of AD. Here, we determined the effect of short, multi-modal 'modern-lifelike'stress on AD pathogenesis and synaptic plasticity in mice bearing three AD mutations (the 3xTg-AD mouse model). We found that combined emotional and physical stress lasting 5 h severely impaired memory in wild-type mice and tended to impact it in already low-performing 3xTg-AD mice. This stress reduced the number of synapse-bearing dendritic spines in 3xTg-AD mice and increased Aβ levels by

  19. Chronic Fluoxetine Induces the Enlargement of Perforant Path-Granule Cell Synapses in the Mouse Dentate Gyrus

    Science.gov (United States)

    Kitahara, Yosuke; Ohta, Keisuke; Hasuo, Hiroshi; Shuto, Takahide; Kuroiwa, Mahomi; Sotogaku, Naoki; Togo, Akinobu; Nakamura, Kei-ichiro; Nishi, Akinori

    2016-01-01

    A selective serotonin reuptake inhibitor is the most commonly prescribed antidepressant for the treatment of major depression. However, the mechanisms underlying the actions of selective serotonin reuptake inhibitors are not fully understood. In the dentate gyrus, chronic fluoxetine treatment induces increased excitability of mature granule cells (GCs) as well as neurogenesis. The major input to the dentate gyrus is the perforant path axons (boutons) from the entorhinal cortex (layer II). Through voltage-sensitive dye imaging, we found that the excitatory neurotransmission of the perforant path synapse onto the GCs in the middle molecular layer of the mouse dentate gyrus (perforant path-GC synapse) is enhanced after chronic fluoxetine treatment (15 mg/kg/day, 14 days). Therefore, we further examined whether chronic fluoxetine treatment affects the morphology of the perforant path-GC synapse, using FIB/SEM (focused ion beam/scanning electron microscopy). A three-dimensional reconstruction of dendritic spines revealed the appearance of extremely large-sized spines after chronic fluoxetine treatment. The large-sized spines had a postsynaptic density with a large volume. However, chronic fluoxetine treatment did not affect spine density. The presynaptic boutons that were in contact with the large-sized spines were large in volume, and the volumes of the mitochondria and synaptic vesicles inside the boutons were correlated with the size of the boutons. Thus, the large-sized perforant path-GC synapse induced by chronic fluoxetine treatment contains synaptic components that correlate with the synapse size and that may be involved in enhanced glutamatergic neurotransmission. PMID:26788851

  20. Brand Marketing Model on Social Networks

    OpenAIRE

    Jolita Jezukevičiūtė; Vida Davidavičienė

    2014-01-01

    The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...

  1. Astrocytic glutamate transport regulates a Drosophila CNS synapse that lacks astrocyte ensheathment.

    Science.gov (United States)

    MacNamee, Sarah E; Liu, Kendra E; Gerhard, Stephan; Tran, Cathy T; Fetter, Richard D; Cardona, Albert; Tolbert, Leslie P; Oland, Lynne A

    2016-07-01

    Anatomical, molecular, and physiological interactions between astrocytes and neuronal synapses regulate information processing in the brain. The fruit fly Drosophila melanogaster has become a valuable experimental system for genetic manipulation of the nervous system and has enormous potential for elucidating mechanisms that mediate neuron-glia interactions. Here, we show the first electrophysiological recordings from Drosophila astrocytes and characterize their spatial and physiological relationship with particular synapses. Astrocyte intrinsic properties were found to be strongly analogous to those of vertebrate astrocytes, including a passive current-voltage relationship, low membrane resistance, high capacitance, and dye-coupling to local astrocytes. Responses to optogenetic stimulation of glutamatergic premotor neurons were correlated directly with anatomy using serial electron microscopy reconstructions of homologous identified neurons and surrounding astrocytic processes. Robust bidirectional communication was present: neuronal activation triggered astrocytic glutamate transport via excitatory amino acid transporter 1 (Eaat1), and blocking Eaat1 extended glutamatergic interneuron-evoked inhibitory postsynaptic currents in motor neurons. The neuronal synapses were always located within 1 μm of an astrocytic process, but none were ensheathed by those processes. Thus, fly astrocytes can modulate fast synaptic transmission via neurotransmitter transport within these anatomical parameters. J. Comp. Neurol. 524:1979-1998, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. A model of coauthorship networks

    Science.gov (United States)

    Zhou, Guochang; Li, Jianping; Xie, Zonglin

    2017-10-01

    A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

  3. Limits to the development of feed-forward structures in large recurrent neuronal networks

    Directory of Open Access Journals (Sweden)

    Susanne Kunkel

    2011-02-01

    Full Text Available Spike-timing dependent plasticity (STDP has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming. Such demonstrations as there are typically rely on constraining the problem artificially. Techniques include employing additional pruning mechanisms or STDP rules that enhance symmetry breaking, simulating networks with low connectivity that magnify competition between synapses, or combinations of the above. In this paper we first review modeling choices that carry particularly high risks of producing non-generalizable results in the context of STDP in recurrent networks. We then develop a theory for the development of feed-forward structure in random networks and conclude that an unstable fixed point in the dynamics prevents the stable propagation of structure in recurrent networks with weight-dependent STDP. We demonstrate that the key predictions of the theory hold in large-scale simulations. The theory provides insight into the reasons why such development does not take place in unconstrained systems and enables us to identify candidate biologically motivated adaptations to the balanced random network model that might enable it.

  4. Brand marketing model on social networks

    OpenAIRE

    Jezukevičiūtė, Jolita; Davidavičienė, Vida

    2014-01-01

    Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...

  5. Target-Centric Network Modeling

    DEFF Research Database (Denmark)

    Mitchell, Dr. William L.; Clark, Dr. Robert M.

    In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

  6. A Complex Network Approach to Distributional Semantic Models.

    Directory of Open Access Journals (Sweden)

    Akira Utsumi

    Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

  7. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    Science.gov (United States)

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  8. Modeling Renewable Penertration Using a Network Economic Model

    Science.gov (United States)

    Lamont, A.

    2001-03-01

    This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

  9. Modelling intelligent behavior

    Science.gov (United States)

    Green, H. S.; Triffet, T.

    1993-01-01

    An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

  10. Zinc-mediated transactivation of TrkB potentiates the hippocampal mossy fiber-CA3 pyramid synapse.

    Science.gov (United States)

    Huang, Yang Z; Pan, Enhui; Xiong, Zhi-Qi; McNamara, James O

    2008-02-28

    The receptor tyrosine kinase, TrkB, is critical to diverse functions of the mammalian nervous system in health and disease. Evidence of TrkB activation during epileptogenesis in vivo despite genetic deletion of its prototypic neurotrophin ligands led us to hypothesize that a non-neurotrophin, the divalent cation zinc, can transactivate TrkB. We found that zinc activates TrkB through increasing Src family kinase activity by an activity-regulated mechanism independent of neurotrophins. One subcellular locale at which zinc activates TrkB is the postsynaptic density of excitatory synapses. Exogenous zinc potentiates the efficacy of the hippocampal mossy fiber (mf)-CA3 pyramid synapse by a TrkB-requiring mechanism. Long-term potentiation of this synapse is impaired by deletion of TrkB, inhibition of TrkB kinase activity, and by CaEDTA, a selective chelator of zinc. The activity-dependent activation of synaptic TrkB in a neurotrophin-independent manner provides a mechanism by which this receptor can regulate synaptic plasticity.

  11. An evolving network model with community structure

    International Nuclear Information System (INIS)

    Li Chunguang; Maini, Philip K

    2005-01-01

    Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

  12. Multilevel method for modeling large-scale networks.

    Energy Technology Data Exchange (ETDEWEB)

    Safro, I. M. (Mathematics and Computer Science)

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  13. Research on the model of home networking

    Science.gov (United States)

    Yun, Xiang; Feng, Xiancheng

    2007-11-01

    It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

  14. Network models in economics and finance

    CERN Document Server

    Pardalos, Panos; Rassias, Themistocles

    2014-01-01

    Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

  15. On optical detection of densely labeled synapses in neuropil and mapping connectivity with combinatorially multiplexed fluorescent synaptic markers.

    Directory of Open Access Journals (Sweden)

    Yuriy Mishchenko

    Full Text Available We propose a new method for mapping neural connectivity optically, by utilizing Cre/Lox system Brainbow to tag synapses of different neurons with random mixtures of different fluorophores, such as GFP, YFP, etc., and then detecting patterns of fluorophores at different synapses using light microscopy (LM. Such patterns will immediately report the pre- and post-synaptic cells at each synaptic connection, without tracing neural projections from individual synapses to corresponding cell bodies. We simulate fluorescence from a population of densely labeled synapses in a block of hippocampal neuropil, completely reconstructed from electron microscopy data, and show that high-end LM is able to detect such patterns with over 95% accuracy. We conclude, therefore, that with the described approach neural connectivity in macroscopically large neural circuits can be mapped with great accuracy, in scalable manner, using fast optical tools, and straightforward image processing. Relying on an electron microscopy dataset, we also derive and explicitly enumerate the conditions that should be met to allow synaptic connectivity studies with high-resolution optical tools.

  16. Intermittent synchronization in a network of bursting neurons

    Science.gov (United States)

    Park, Choongseok; Rubchinsky, Leonid L.

    2011-09-01

    Synchronized oscillations in networks of inhibitory and excitatory coupled bursting neurons are common in a variety of neural systems from central pattern generators to human brain circuits. One example of the latter is the subcortical network of the basal ganglia, formed by excitatory and inhibitory bursters of the subthalamic nucleus and globus pallidus, involved in motor control and affected in Parkinson's disease. Recent experiments have demonstrated the intermittent nature of the phase-locking of neural activity in this network. Here, we explore one potential mechanism to explain the intermittent phase-locking in a network. We simplify the network to obtain a model of two inhibitory coupled elements and explore its dynamics. We used geometric analysis and singular perturbation methods for dynamical systems to reduce the full model to a simpler set of equations. Mathematical analysis was completed using three slow variables with two different time scales. Intermittently, synchronous oscillations are generated by overlapped spiking which crucially depends on the geometry of the slow phase plane and the interplay between slow variables as well as the strength of synapses. Two slow variables are responsible for the generation of activity patterns with overlapped spiking, and the other slower variable enhances the robustness of an irregular and intermittent activity pattern. While the analyzed network and the explored mechanism of intermittent synchrony appear to be quite generic, the results of this analysis can be used to trace particular values of biophysical parameters (synaptic strength and parameters of calcium dynamics), which are known to be impacted in Parkinson's disease.

  17. Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala.

    Science.gov (United States)

    Vlachos, Ioannis; Herry, Cyril; Lüthi, Andreas; Aertsen, Ad; Kumar, Arvind

    2011-03-01

    The basal nucleus of the amygdala (BA) is involved in the formation of context-dependent conditioned fear and extinction memories. To understand the underlying neural mechanisms we developed a large-scale neuron network model of the BA, composed of excitatory and inhibitory leaky-integrate-and-fire neurons. Excitatory BA neurons received conditioned stimulus (CS)-related input from the adjacent lateral nucleus (LA) and contextual input from the hippocampus or medial prefrontal cortex (mPFC). We implemented a plasticity mechanism according to which CS and contextual synapses were potentiated if CS and contextual inputs temporally coincided on the afferents of the excitatory neurons. Our simulations revealed a differential recruitment of two distinct subpopulations of BA neurons during conditioning and extinction, mimicking the activation of experimentally observed cell populations. We propose that these two subgroups encode contextual specificity of fear and extinction memories, respectively. Mutual competition between them, mediated by feedback inhibition and driven by contextual inputs, regulates the activity in the central amygdala (CEA) thereby controlling amygdala output and fear behavior. The model makes multiple testable predictions that may advance our understanding of fear and extinction memories.

  18. Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala.

    Directory of Open Access Journals (Sweden)

    Ioannis Vlachos

    2011-03-01

    Full Text Available The basal nucleus of the amygdala (BA is involved in the formation of context-dependent conditioned fear and extinction memories. To understand the underlying neural mechanisms we developed a large-scale neuron network model of the BA, composed of excitatory and inhibitory leaky-integrate-and-fire neurons. Excitatory BA neurons received conditioned stimulus (CS-related input from the adjacent lateral nucleus (LA and contextual input from the hippocampus or medial prefrontal cortex (mPFC. We implemented a plasticity mechanism according to which CS and contextual synapses were potentiated if CS and contextual inputs temporally coincided on the afferents of the excitatory neurons. Our simulations revealed a differential recruitment of two distinct subpopulations of BA neurons during conditioning and extinction, mimicking the activation of experimentally observed cell populations. We propose that these two subgroups encode contextual specificity of fear and extinction memories, respectively. Mutual competition between them, mediated by feedback inhibition and driven by contextual inputs, regulates the activity in the central amygdala (CEA thereby controlling amygdala output and fear behavior. The model makes multiple testable predictions that may advance our understanding of fear and extinction memories.

  19. Complex networks under dynamic repair model

    Science.gov (United States)

    Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

    2018-01-01

    Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

  20. Adaptive-network models of collective dynamics

    Science.gov (United States)

    Zschaler, G.

    2012-09-01

    Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

  1. Linear approximation model network and its formation via ...

    Indian Academy of Sciences (India)

    To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

  2. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  3. Spatial Epidemic Modelling in Social Networks

    Science.gov (United States)

    Simoes, Joana Margarida

    2005-06-01

    The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

  4. Modeling of fluctuating reaction networks

    International Nuclear Information System (INIS)

    Lipshtat, A.; Biham, O.

    2004-01-01

    Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press

  5. Deterministic ripple-spreading model for complex networks.

    Science.gov (United States)

    Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

    2011-04-01

    This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

  6. Network model of security system

    Directory of Open Access Journals (Sweden)

    Adamczyk Piotr

    2016-01-01

    Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

  7. Generation of functional inhibitory synapses incorporating defined combinations of GABA(A or glycine receptor subunits

    Directory of Open Access Journals (Sweden)

    Christine Laura Dixon

    2015-12-01

    Full Text Available Fast inhibitory neurotransmission in the brain is mediated by wide range of GABAA receptor (GABAAR and glycine receptor (GlyR isoforms, each with different physiological and pharmacological properties. Because multiple isoforms are expressed simultaneously in most neurons, it is difficult to define the properties of inhibitory postsynaptic currents mediated by individual isoforms in vivo. Although recombinant expression systems permit the expression of individual isoforms in isolation, they require exogenous agonist application which cannot mimic the dynamic neurotransmitter profile characteristic of native synapses. We describe a neuron-HEK293 cell co-culture technique for generating inhibitory synapses incorporating defined combinations of GABAAR or GlyR subunits. Primary neuronal cultures, prepared from embryonic rat cerebral cortex or spinal cord, are used to provide presynaptic GABAergic and glycinergic terminals, respectively. When the cultures are mature, HEK293 cells expressing the subunits of interest plus neuroligin 2A are plated onto the neurons, which rapidly form synapses onto HEK293 cells. Patch clamp electrophysiology is then used to analyze the physiological and pharmacological properties of the inhibitory postsynaptic currents mediated by the recombinant receptors. The method is suitable for investigating the kinetic properties or the effects of drugs on inhibitory postsynaptic currents mediated by defined GABAAR or GlyR isoforms of interest, the effects of hereditary disease mutations on the formation and function of both types of synapses, and synaptogenesis and synaptic clustering mechanisms. The entire cell preparation procedure takes 2 – 5 weeks.

  8. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  9. Making of a Synapse: Recurrent Roles of Drebrin A at Excitatory Synapses Throughout Life.

    Science.gov (United States)

    Aoki, Chiye; Sherpa, Ang D

    2017-01-01

    Mature excitatory synapses are composed of more than 1500 proteins postsynaptically and hundreds more that operate presynaptically. Among them, drebrin is an F-actin-binding protein that increases noticeably during juvenile synaptogenesis. Electron microscopic analysis reveals that drebrin is highly enriched specifically on the postsynaptic side of excitatory synapses. Since dendritic spines are structures specialized for excitatory synaptic transmission, the function of drebrin was probed by analyzing the ultrastructural characteristics of dendritic spines of animals with genetic deletion of drebrin A (DAKO), the adult isoform of drebrin. Electron microscopic analyses revealed that these brains are surprisingly intact, in that axo-spinous synaptic junctions are well-formed and not significantly altered in number. This normal ultrastructure may be because drebrin E, the alternate embryonic isoform, compensates for the genetic deletion of drebrin A. However, DAKO results in the loss of homeostatic plasticity of N-methyl-D-aspartate receptors (NMDARs). The NMDAR activation-dependent trafficking of the NR2A subunit-containing NMDARs from dendritic shafts into spine head cytoplasm is greatly diminished within brains of DAKO. Conversely, within brains of wild-type rodents, spines respond to NMDAR blockade with influx of F-actin, drebrin A, and NR2A subunits of NMDARs. These observations indicate that drebrin A facilitates the trafficking of NMDAR cargos in an F-actin-dependent manner to mediate homeostatic plasticity. Analysis of the brains of transgenic mice used as models of Alzheimer's disease (AD) reveals that the loss of drebrin from dendritic spines predates the emergence of synaptic dysfunction and cognitive impairment, suggesting that this form of homeostatic plasticity contributes toward cognition. Two studies suggest that the nature of drebrin's interaction with NMDARs is dependent on the receptor's subunit composition. Drebrin A can be found co

  10. An acoustical model based monitoring network

    NARCIS (Netherlands)

    Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

    2010-01-01

    In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

  11. How to model wireless mesh networks topology

    International Nuclear Information System (INIS)

    Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M

    2013-01-01

    The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches

  12. Selectivity and sparseness in randomly connected balanced networks.

    Directory of Open Access Journals (Sweden)

    Cengiz Pehlevan

    Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

  13. On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

    Science.gov (United States)

    Schmitt, Michael

    2004-09-01

    We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

  14. Growth dynamics explain the development of spatiotemporal burst activity of young cultured neuronal networks in detail.

    Directory of Open Access Journals (Sweden)

    Taras A Gritsun

    Full Text Available A typical property of isolated cultured neuronal networks of dissociated rat cortical cells is synchronized spiking, called bursting, starting about one week after plating, when the dissociated cells have sufficiently sent out their neurites and formed enough synaptic connections. This paper is the third in a series of three on simulation models of cultured networks. Our two previous studies [26], [27] have shown that random recurrent network activity models generate intra- and inter-bursting patterns similar to experimental data. The networks were noise or pacemaker-driven and had Izhikevich-neuronal elements with only short-term plastic (STP synapses (so, no long-term potentiation, LTP, or depression, LTD, was included. However, elevated pre-phases (burst leaders and after-phases of burst main shapes, that usually arise during the development of the network, were not yet simulated in sufficient detail. This lack of detail may be due to the fact that the random models completely missed network topology .and a growth model. Therefore, the present paper adds, for the first time, a growth model to the activity model, to give the network a time dependent topology and to explain burst shapes in more detail. Again, without LTP or LTD mechanisms. The integrated growth-activity model yielded realistic bursting patterns. The automatic adjustment of various mutually interdependent network parameters is one of the major advantages of our current approach. Spatio-temporal bursting activity was validated against experiment. Depending on network size, wave reverberation mechanisms were seen along the network boundaries, which may explain the generation of phases of elevated firing before and after the main phase of the burst shape.In summary, the results show that adding topology and growth explain burst shapes in great detail and suggest that young networks still lack/do not need LTP or LTD mechanisms.

  15. Ordering chaos and synchronization transitions by chemical delay and coupling on scale-free neuronal networks

    International Nuclear Information System (INIS)

    Gong Yubing; Xie Yanhang; Lin Xiu; Hao Yinghang; Ma Xiaoguang

    2010-01-01

    Research highlights: → Chemical delay and chemical coupling can tame chaotic bursting. → Chemical delay-induced transitions from bursting synchronization to intermittent multiple spiking synchronizations. → Chemical coupling-induced different types of delay-dependent firing transitions. - Abstract: Chemical synaptic connections are more common than electric ones in neurons, and information transmission delay is especially significant for the synapses of chemical type. In this paper, we report a phenomenon of ordering spatiotemporal chaos and synchronization transitions by the delays and coupling through chemical synapses of modified Hodgkin-Huxley (MHH) neurons on scale-free networks. As the delay τ is increased, the neurons exhibit transitions from bursting synchronization (BS) to intermittent multiple spiking synchronizations (SS). As the coupling g syn is increased, the neurons exhibit different types of firing transitions, depending on the values of τ. For a smaller τ, there are transitions from spatiotemporal chaotic bursting (SCB) to BS or SS; while for a larger τ, there are transitions from SCB to intermittent multiple SS. These findings show that the delays and coupling through chemical synapses can tame the chaotic firings and repeatedly enhance the firing synchronization of neurons, and hence could play important roles in the firing activity of the neurons on scale-free networks.

  16. The number and distribution of AMPA receptor channels containing fast kinetic GluA3 and GluA4 subunits at auditory nerve synapses depend on the target cells.

    Science.gov (United States)

    Rubio, María E; Matsui, Ko; Fukazawa, Yugo; Kamasawa, Naomi; Harada, Harumi; Itakura, Makoto; Molnár, Elek; Abe, Manabu; Sakimura, Kenji; Shigemoto, Ryuichi

    2017-11-01

    The neurotransmitter receptor subtype, number, density, and distribution relative to the location of transmitter release sites are key determinants of signal transmission. AMPA-type ionotropic glutamate receptors (AMPARs) containing GluA3 and GluA4 subunits are prominently expressed in subsets of neurons capable of firing action potentials at high frequencies, such as auditory relay neurons. The auditory nerve (AN) forms glutamatergic synapses on two types of relay neurons, bushy cells (BCs) and fusiform cells (FCs) of the cochlear nucleus. AN-BC and AN-FC synapses have distinct kinetics; thus, we investigated whether the number, density, and localization of GluA3 and GluA4 subunits in these synapses are differentially organized using quantitative freeze-fracture replica immunogold labeling. We identify a positive correlation between the number of AMPARs and the size of AN-BC and AN-FC synapses. Both types of AN synapses have similar numbers of AMPARs; however, the AN-BC have a higher density of AMPARs than AN-FC synapses, because the AN-BC synapses are smaller. A higher number and density of GluA3 subunits are observed at AN-BC synapses, whereas a higher number and density of GluA4 subunits are observed at AN-FC synapses. The intrasynaptic distribution of immunogold labeling revealed that AMPAR subunits, particularly GluA3, are concentrated at the center of the AN-BC synapses. The central distribution of AMPARs is absent in GluA3-knockout mice, and gold particles are evenly distributed along the postsynaptic density. GluA4 gold labeling was homogenously distributed along both synapse types. Thus, GluA3 and GluA4 subunits are distributed at AN synapses in a target-cell-dependent manner.

  17. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  18. Entropy Characterization of Random Network Models

    Directory of Open Access Journals (Sweden)

    Pedro J. Zufiria

    2017-06-01

    Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

  19. The model of social crypto-network

    Directory of Open Access Journals (Sweden)

    Марк Миколайович Орел

    2015-06-01

    Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks

  20. Network-driven design principles for neuromorphic systems

    Directory of Open Access Journals (Sweden)

    Johannes ePartzsch

    2015-10-01

    Full Text Available Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems.

  1. Network-driven design principles for neuromorphic systems.

    Science.gov (United States)

    Partzsch, Johannes; Schüffny, Rene

    2015-01-01

    Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems.

  2. Towards reproducible descriptions of neuronal network models.

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2009-08-01

    Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  3. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  4. An Improved Car-Following Model in Vehicle Networking Based on Network Control

    Directory of Open Access Journals (Sweden)

    D. Y. Kong

    2014-01-01

    Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.

  5. The Role of Adult-Born Neurons in the Constantly Changing Olfactory Bulb Network

    Directory of Open Access Journals (Sweden)

    Sarah Malvaut

    2016-01-01

    Full Text Available The adult mammalian brain is remarkably plastic and constantly undergoes structurofunctional modifications in response to environmental stimuli. In many regions plasticity is manifested by modifications in the efficacy of existing synaptic connections or synapse formation and elimination. In a few regions, however, plasticity is brought by the addition of new neurons that integrate into established neuronal networks. This type of neuronal plasticity is particularly prominent in the olfactory bulb (OB where thousands of neuronal progenitors are produced on a daily basis in the subventricular zone (SVZ and migrate along the rostral migratory stream (RMS towards the OB. In the OB, these neuronal precursors differentiate into local interneurons, mature, and functionally integrate into the bulbar network by establishing output synapses with principal neurons. Despite continuous progress, it is still not well understood how normal functioning of the OB is preserved in the constantly remodelling bulbar network and what role adult-born neurons play in odor behaviour. In this review we will discuss different levels of morphofunctional plasticity effected by adult-born neurons and their functional role in the adult OB and also highlight the possibility that different subpopulations of adult-born cells may fulfill distinct functions in the OB neuronal network and odor behaviour.

  6. The Role of Adult-Born Neurons in the Constantly Changing Olfactory Bulb Network

    Science.gov (United States)

    Malvaut, Sarah; Saghatelyan, Armen

    2016-01-01

    The adult mammalian brain is remarkably plastic and constantly undergoes structurofunctional modifications in response to environmental stimuli. In many regions plasticity is manifested by modifications in the efficacy of existing synaptic connections or synapse formation and elimination. In a few regions, however, plasticity is brought by the addition of new neurons that integrate into established neuronal networks. This type of neuronal plasticity is particularly prominent in the olfactory bulb (OB) where thousands of neuronal progenitors are produced on a daily basis in the subventricular zone (SVZ) and migrate along the rostral migratory stream (RMS) towards the OB. In the OB, these neuronal precursors differentiate into local interneurons, mature, and functionally integrate into the bulbar network by establishing output synapses with principal neurons. Despite continuous progress, it is still not well understood how normal functioning of the OB is preserved in the constantly remodelling bulbar network and what role adult-born neurons play in odor behaviour. In this review we will discuss different levels of morphofunctional plasticity effected by adult-born neurons and their functional role in the adult OB and also highlight the possibility that different subpopulations of adult-born cells may fulfill distinct functions in the OB neuronal network and odor behaviour. PMID:26839709

  7. Multiplicative Attribute Graph Model of Real-World Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)

    2010-10-20

    Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.

  8. Developing Personal Network Business Models

    DEFF Research Database (Denmark)

    Saugstrup, Dan; Henten, Anders

    2006-01-01

    The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...

  9. A novel Direct Small World network model

    Directory of Open Access Journals (Sweden)

    LIN Tao

    2016-10-01

    Full Text Available There is a certain degree of redundancy and low efficiency of existing computer networks.This paper presents a novel Direct Small World network model in order to optimize networks.In this model,several nodes construct a regular network.Then,randomly choose and replot some nodes to generate Direct Small World network iteratively.There is no change in average distance and clustering coefficient.However,the network performance,such as hops,is improved.The experiments prove that compared to traditional small world network,the degree,average of degree centrality and average of closeness centrality are lower in Direct Small World network.This illustrates that the nodes in Direct Small World networks are closer than Watts-Strogatz small world network model.The Direct Small World can be used not only in the communication of the community information,but also in the research of epidemics.

  10. Non-consensus Opinion Models on Complex Networks

    Science.gov (United States)

    Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo

    2013-04-01

    Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not

  11. Low-doses of cisplatin injure hippocampal synapses: a mechanism for 'chemo' brain?

    Science.gov (United States)

    Andres, Adrienne L; Gong, Xing; Di, Kaijun; Bota, Daniela A

    2014-05-01

    Chemotherapy-related cognitive deficits are a major neurological problem, but the underlying mechanisms are unclear. The death of neural stem/precursor cell (NSC) by cisplatin has been reported as a potential cause, but this requires high doses of chemotherapeutic agents. Cisplatin is frequently used in modern oncology, and it achieves high concentrations in the patient's brain. Here we report that exposure to low concentrations of cisplatin (0.1μM) causes the loss of dendritic spines and synapses within 30min. Longer exposures injured dendritic branches and reduced dendritic complexity. At this low concentration, cisplatin did not affect NSC viability nor provoke apoptosis. However, higher cisplatin levels (1μM) led to the rapid loss of synapses and dendritic disintegration, and neuronal-but not NSC-apoptosis. In-vivo treatment with cisplatin at clinically relevant doses also caused a reduction of dendritic branches and decreased spine density in CA1 and CA3 hippocampal neurons. An acute increase in cell death was measured in the CA1 and CA3 neurons, as well as in the NSC population located in the subgranular zone of the dentate gyrus in the cisplatin treated animals. The density of dendritic spines is related to the degree of neuronal connectivity and function, and pathological changes in spine number or structure have significant consequences for brain function. Therefore, this synapse and dendritic damage might contribute to the cognitive impairment observed after cisplatin treatment. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Cux1 and Cux2 regulate dendritic branching, spine morphology and synapses of the upper layer neurons of the cortex

    Science.gov (United States)

    Cubelos, Beatriz; Sebastián-Serrano, Alvaro; Beccari, Leonardo; Calcagnotto, Maria Elisa; Cisneros, Elsa; Kim, Seonhee; Dopazo, Ana; Alvarez-Dolado, Manuel; Redondo, Juan Miguel; Bovolenta, Paola; Walsh, Christopher A.; Nieto, Marta

    2010-01-01

    Summary Dendrite branching and spine formation determines the function of morphologically distinct and specialized neuronal subclasses. However, little is known about the programs instructing specific branching patterns in vertebrate neurons and whether such programs influence dendritic spines and synapses. Using knockout and knockdown studies combined with morphological, molecular and electrophysiological analysis we show that the homeobox Cux1 and Cux2 are intrinsic and complementary regulators of dendrite branching, spine development and synapse formation in layer II–III neurons of the cerebral cortex. Cux genes control the number and maturation of dendritic spines partly through direct regulation of the expression of Xlr3b and Xlr4b, chromatin remodeling genes previously implicated in cognitive defects. Accordingly, abnormal dendrites and synapses in Cux2−/− mice correlate with reduced synaptic function and defects in working memory. These demonstrate critical roles of Cux in dendritogenesis and highlight novel subclass-specific mechanisms of synapse regulation that contribute to the establishment of cognitive circuits. PMID:20510857

  13. A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

    Science.gov (United States)

    Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

    2018-02-01

    Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

  14. Network structure exploration via Bayesian nonparametric models

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

    2015-01-01

    Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

  15. Homophyly/Kinship Model: Naturally Evolving Networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

    2015-10-01

    It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

  16. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  17. Neural networks within multi-core optic fibers.

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-07

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  18. Presynaptic membrane receptors in acetylcholine release modulation in the neuromuscular synapse.

    Science.gov (United States)

    Tomàs, Josep; Santafé, Manel M; Garcia, Neus; Lanuza, Maria A; Tomàs, Marta; Besalduch, Núria; Obis, Teresa; Priego, Mercedes; Hurtado, Erica

    2014-05-01

    Over the past few years, we have studied, in the mammalian neuromuscular junction (NMJ), the local involvement in transmitter release of the presynaptic muscarinic ACh autoreceptors (mAChRs), purinergic adenosine autoreceptors (P1Rs), and trophic factor receptors (TFRs; for neurotrophins and trophic cytokines) during development and in the adult. At any given moment, the way in which a synapse works is largely the logical outcome of the confluence of these (and other) metabotropic signalling pathways on intracellular kinases, which phosphorylate protein targets and materialize adaptive changes. We propose an integrated interpretation of the complementary function of these receptors in the adult NMJ. The activity of a given receptor group can modulate a given combination of spontaneous, evoked, and activity-dependent release characteristics. For instance, P1Rs can conserve resources by limiting spontaneous quantal leak of ACh (an A1 R action) and protect synapse function, because stimulation with adenosine reduces the magnitude of depression during repetitive activity. The overall outcome of the mAChRs seems to contribute to upkeep of spontaneous quantal output of ACh, save synapse function by decreasing the extent of evoked release (mainly an M2 action), and reduce depression. We have also identified several links among P1Rs, mAChRs, and TFRs. We found a close dependence between mAChR and some TFRs and observed that the muscarinic group has to operate correctly if the tropomyosin-related kinase B receptor (trkB) is also to operate correctly, and vice versa. Likewise, the functional integrity of mAChRs depends on P1Rs operating normally. Copyright © 2014 Wiley Periodicals, Inc.

  19. Cyber threat model for tactical radio networks

    Science.gov (United States)

    Kurdziel, Michael T.

    2014-05-01

    The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

  20. Tool wear modeling using abductive networks

    Science.gov (United States)

    Masory, Oren

    1992-09-01

    A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

  1. Developmental time windows for axon growth influence neuronal network topology.

    Science.gov (United States)

    Lim, Sol; Kaiser, Marcus

    2015-04-01

    Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

  2. Region stability analysis and tracking control of memristive recurrent neural network.

    Science.gov (United States)

    Bao, Gang; Zeng, Zhigang; Shen, Yanjun

    2018-02-01

    Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Effects of dynamic synapses on noise-delayed response latency of a single neuron

    Science.gov (United States)

    Uzuntarla, M.; Ozer, M.; Ileri, U.; Calim, A.; Torres, J. J.

    2015-12-01

    The noise-delayed decay (NDD) phenomenon emerges when the first-spike latency of a periodically forced stochastic neuron exhibits a maximum for a particular range of noise intensity. Here, we investigate the latency response dynamics of a single Hodgkin-Huxley neuron that is subject to both a suprathreshold periodic stimulus and a background activity arriving through dynamic synapses. We study the first-spike latency response as a function of the presynaptic firing rate f . This constitutes a more realistic scenario than previous works, since f provides a suitable biophysically realistic parameter to control the level of activity in actual neural systems. We first report on the emergence of classical NDD behavior as a function of f for the limit of static synapses. Second, we show that when short-term depression and facilitation mechanisms are included at the synapses, different NDD features can be found due to their modulatory effect on synaptic current fluctuations. For example, an intriguing double NDD (DNDD) behavior occurs for different sets of relevant synaptic parameters. Moreover, depending on the balance between synaptic depression and synaptic facilitation, single NDD or DNDD can prevail, in such a way that synaptic facilitation favors the emergence of DNDD whereas synaptic depression favors the existence of single NDD. Here we report the existence of the DNDD effect in the response latency dynamics of a neuron.

  4. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

    2015-01-01

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

  5. Synergistic effects in threshold models on networks

    Science.gov (United States)

    Juul, Jonas S.; Porter, Mason A.

    2018-01-01

    Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

  6. Modeling geomagnetic induced currents in Australian power networks

    Science.gov (United States)

    Marshall, R. A.; Kelly, A.; Van Der Walt, T.; Honecker, A.; Ong, C.; Mikkelsen, D.; Spierings, A.; Ivanovich, G.; Yoshikawa, A.

    2017-07-01

    Geomagnetic induced currents (GICs) have been considered an issue for high-latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a "geoelectric field" determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29-31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29-31 October 2003 exceed 40 A with the larger GICs occurring in the south-east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east-west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

  7. Seizure-induced alterations in fast-spiking basket cell GABA currents modulate frequency and coherence of gamma oscillation in network simulations

    International Nuclear Information System (INIS)

    Proddutur, Archana; Yu, Jiandong; Elgammal, Fatima S.; Santhakumar, Vijayalakshmi

    2013-01-01

    Gamma frequency oscillations have been proposed to contribute to memory formation and retrieval. Fast-spiking basket cells (FS-BCs) are known to underlie development of gamma oscillations. Fast, high amplitude GABA synapses and gap junctions have been suggested to contribute to gamma oscillations in FS-BC networks. Recently, we identified that, apart from GABAergic synapses, FS-BCs in the hippocampal dentate gyrus have GABAergic currents mediated by extrasynaptic receptors. Our experimental studies demonstrated two specific changes in FS-BC GABA currents following experimental seizures [Yu et al., J. Neurophysiol. 109, 1746 (2013)]: increase in the magnitude of extrasynaptic (tonic) GABA currents and a depolarizing shift in GABA reversal potential (E GABA ). Here, we use homogeneous networks of a biophysically based model of FS-BCs to examine how the presence of extrasynaptic GABA conductance (g GABA-extra ) and experimentally identified, seizure-induced changes in g GABA-extra and E GABA influence network activity. Networks of FS-BCs interconnected by fast GABAergic synapses developed synchronous firing in the dentate gamma frequency range (40–100 Hz). Systematic investigation revealed that the biologically realistic range of 30 to 40 connections between FS-BCs resulted in greater coherence in the gamma frequency range when networks were activated by Poisson-distributed dendritic synaptic inputs rather than by homogeneous somatic current injections, which were balanced for FS-BC firing frequency in unconnected networks. Distance-dependent conduction delay enhanced coherence in networks with 30–40 FS-BC interconnections while inclusion of gap junctional conductance had a modest effect on coherence. In networks activated by somatic current injections resulting in heterogeneous FS-BC firing, increasing g GABA-extra reduced the frequency and coherence of FS-BC firing when E GABA was shunting (−74 mV), but failed to alter average FS-BC frequency when E GABA

  8. Reduced cortical distribution volume of iodine-123 iomazenil in Alzheimer's disease as a measure of loss of synapses

    DEFF Research Database (Denmark)

    Soricelli, A; Postiglione, A; Grivet-Fojaja, M R

    1996-01-01

    Iodine-123 labelled iomazenil (IMZ) is a specific tracer for the GABAA receptor, the dominant inhibitory synapse of the brain. The cerebral distribution volume (Vd) of IMZ may be taken as a quantitative measure of these synapses in Alzheimer's disease (AD), where synaptic loss tends indiscriminat...... simultaneously. Reduced values were found in all regions except in the occipital (visual) cortex. In particular, temporal and parietal cortex Vd was significantly (P...

  9. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  10. Feature network models for proximity data : statistical inference, model selection, network representations and links with related models

    NARCIS (Netherlands)

    Frank, Laurence Emmanuelle

    2006-01-01

    Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor

  11. Human synapses show a wide temporal window for spike-timing-dependent plasticity

    NARCIS (Netherlands)

    Testa-Silva, G.; Verhoog, M.B.; Goriounova, N.A.; Loebel, A.; Hjorth, J.; Baayen, J.C.; de Kock, C.P.J.; Mansvelder, H.D.

    2010-01-01

    Throughout our lifetime, activity-dependent changes in neuronal connection strength enable the brain to refine neural circuits and learn based on experience. Synapses can bi-directionally alter strength and the magnitude and sign depend on the millisecond timing of presynaptic and postsynaptic

  12. Live cell linear dichroism imaging reveals extensive membrane ruffling within the docking structure of natural killer cell immune synapses

    DEFF Research Database (Denmark)

    Benninger, Richard K P; Vanherberghen, Bruno; Young, Stephen

    2009-01-01

    We have applied fluorescence imaging of two-photon linear dichroism to measure the subresolution organization of the cell membrane during formation of the activating (cytolytic) natural killer (NK) cell immune synapse (IS). This approach revealed that the NK cell plasma membrane is convoluted...... into ruffles at the periphery, but not in the center of a mature cytolytic NK cell IS. Time-lapse imaging showed that the membrane ruffles formed at the initial point of contact between NK cells and target cells and then spread radialy across the intercellular contact as the size of the IS increased, becoming...... absent from the center of the mature synapse. Understanding the role of such extensive membrane ruffling in the assembly of cytolytic synapses is an intriguing new goal....

  13. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...

  14. Chimeric antigen receptor T cells form nonclassical and potent immune synapses driving rapid cytotoxicity.

    Science.gov (United States)

    Davenport, A J; Cross, R S; Watson, K A; Liao, Y; Shi, W; Prince, H M; Beavis, P A; Trapani, J A; Kershaw, M H; Ritchie, D S; Darcy, P K; Neeson, P J; Jenkins, M R

    2018-02-27

    Chimeric antigen receptor T (CAR-T) cells are effective serial killers with a faster off-rate from dying tumor cells than CAR-T cells binding target cells through their T cell receptor (TCR). Here we explored the functional consequences of CAR-mediated signaling using a dual-specific CAR-T cell, where the same cell was triggered via TCR (tcrCTL) or CAR (carCTL). The carCTL immune synapse lacked distinct LFA-1 adhesion rings and was less reliant on LFA to form stable conjugates with target cells. carCTL receptors associated with the synapse were found to be disrupted and formed a convoluted multifocal pattern of Lck microclusters. Both proximal and distal receptor signaling pathways were induced more rapidly and subsequently decreased more rapidly in carCTL than in tcrCTL. The functional consequence of this rapid signaling in carCTL cells included faster lytic granule recruitment to the immune synapse, correlating with faster detachment of the CTL from the target cell. This study provides a mechanism for how CAR-T cells can debulk large tumor burden quickly and may contribute to further refinement of CAR design for enhancing the quality of signaling and programming of the T cell. Copyright © 2018 the Author(s). Published by PNAS.

  15. Modelling and designing electric energy networks

    International Nuclear Information System (INIS)

    Retiere, N.

    2003-11-01

    The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets

  16. From synapse to nucleus and back again--communication over distance within neurons.

    Science.gov (United States)

    Fainzilber, Mike; Budnik, Vivian; Segal, Rosalind A; Kreutz, Michael R

    2011-11-09

    How do neurons integrate intracellular communication from synapse to nucleus and back? Here we briefly summarize aspects of this topic covered by a symposium at Neuroscience 2011. A rich repertoire of signaling mechanisms link both dendritic terminals and axon tips with neuronal soma and nucleus, using motor-dependent transport machineries to traverse the long intracellular distances along neuronal processes. Activation mechanisms at terminals include localized translation of dendritic or axonal RNA, proteolytic cleavage of receptors or second messengers, and differential phosphorylation of signaling moieties. Signaling complexes may be transported in endosomes, or as non-endosomal complexes associated with importins and dynein. Anterograde transport of RNA granules from the soma to neuronal processes, coupled with retrograde transport of proteins translated locally at terminals or within processes, may fuel ongoing bidirectional communication between soma and synapse to modulate synaptic plasticity as well as neuronal growth and survival decisions.

  17. Modelling traffic congestion using queuing networks

    Indian Academy of Sciences (India)

    Flow-density curves; uninterrupted traffic; Jackson networks. ... ness - also suffer from a big handicap vis-a-vis the Indian scenario: most of these models do .... more well-known queuing network models and onsite data, a more exact Road Cell ...

  18. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    Science.gov (United States)

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    . The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...

  20. Npas4 Is a Critical Regulator of Learning-Induced Plasticity at Mossy Fiber-CA3 Synapses during Contextual Memory Formation.

    Science.gov (United States)

    Weng, Feng-Ju; Garcia, Rodrigo I; Lutzu, Stefano; Alviña, Karina; Zhang, Yuxiang; Dushko, Margaret; Ku, Taeyun; Zemoura, Khaled; Rich, David; Garcia-Dominguez, Dario; Hung, Matthew; Yelhekar, Tushar D; Sørensen, Andreas Toft; Xu, Weifeng; Chung, Kwanghun; Castillo, Pablo E; Lin, Yingxi

    2018-03-07

    Synaptic connections between hippocampal mossy fibers (MFs) and CA3 pyramidal neurons are essential for contextual memory encoding, but the molecular mechanisms regulating MF-CA3 synapses during memory formation and the exact nature of this regulation are poorly understood. Here we report that the activity-dependent transcription factor Npas4 selectively regulates the structure and strength of MF-CA3 synapses by restricting the number of their functional synaptic contacts without affecting the other synaptic inputs onto CA3 pyramidal neurons. Using an activity-dependent reporter, we identified CA3 pyramidal cells that were activated by contextual learning and found that MF inputs on these cells were selectively strengthened. Deletion of Npas4 prevented both contextual memory formation and this learning-induced synaptic modification. We further show that Npas4 regulates MF-CA3 synapses by controlling the expression of the polo-like kinase Plk2. Thus, Npas4 is a critical regulator of experience-dependent, structural, and functional plasticity at MF-CA3 synapses during contextual memory formation. Copyright © 2018 Elsevier Inc. All rights reserved.