Faugeras, Olivier; Touboul, Jonathan; Cessac, Bruno
2009-01-01
We deal with the problem of bridging the gap between two scales in neuronal modeling. At the first (microscopic) scale, neurons are considered individually and their behavior described by stochastic differential equations that govern the time variations of their membrane potentials. They are coupled by synaptic connections acting on their resulting activity, a nonlinear function of their membrane potential. At the second (mesoscopic) scale, interacting populations of neurons are described individually by similar equations. The equations describing the dynamical and the stationary mean-field behaviors are considered as functional equations on a set of stochastic processes. Using this new point of view allows us to prove that these equations are well-posed on any finite time interval and to provide a constructive method for effectively computing their unique solution. This method is proved to converge to the unique solution and we characterize its complexity and convergence rate. We also provide partial results for the stationary problem on infinite time intervals. These results shed some new light on such neural mass models as the one of Jansen and Rit (1995): their dynamics appears as a coarse approximation of the much richer dynamics that emerges from our analysis. Our numerical experiments confirm that the framework we propose and the numerical methods we derive from it provide a new and powerful tool for the exploration of neural behaviors at different scales.
Stochastic Learning in Oxide Binary Synaptic Device for Neuromorphic Computing
Directory of Open Access Journals (Sweden)
Shimeng eYu
2013-10-01
Full Text Available Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on transition of metal oxide resistance switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.
Stochastic learning in oxide binary synaptic device for neuromorphic computing.
Yu, Shimeng; Gao, Bin; Fang, Zheng; Yu, Hongyu; Kang, Jinfeng; Wong, H-S Philip
2013-01-01
Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.
Bilinearity in spatiotemporal integration of synaptic inputs.
Directory of Open Access Journals (Sweden)
Songting Li
2014-12-01
Full Text Available Neurons process information via integration of synaptic inputs from dendrites. Many experimental results demonstrate dendritic integration could be highly nonlinear, yet few theoretical analyses have been performed to obtain a precise quantitative characterization analytically. Based on asymptotic analysis of a two-compartment passive cable model, given a pair of time-dependent synaptic conductance inputs, we derive a bilinear spatiotemporal dendritic integration rule. The summed somatic potential can be well approximated by the linear summation of the two postsynaptic potentials elicited separately, plus a third additional bilinear term proportional to their product with a proportionality coefficient [Formula: see text]. The rule is valid for a pair of synaptic inputs of all types, including excitation-inhibition, excitation-excitation, and inhibition-inhibition. In addition, the rule is valid during the whole dendritic integration process for a pair of synaptic inputs with arbitrary input time differences and input locations. The coefficient [Formula: see text] is demonstrated to be nearly independent of the input strengths but is dependent on input times and input locations. This rule is then verified through simulation of a realistic pyramidal neuron model and in electrophysiological experiments of rat hippocampal CA1 neurons. The rule is further generalized to describe the spatiotemporal dendritic integration of multiple excitatory and inhibitory synaptic inputs. The integration of multiple inputs can be decomposed into the sum of all possible pairwise integration, where each paired integration obeys the bilinear rule. This decomposition leads to a graph representation of dendritic integration, which can be viewed as functionally sparse.
Stochastic single-molecule dynamics of synaptic membrane protein domains
Kahraman, Osman; Li, Yiwei; Haselwandter, Christoph A.
2016-09-01
Motivated by single-molecule experiments on synaptic membrane protein domains, we use a stochastic lattice model to study protein reaction and diffusion processes in crowded membranes. We find that the stochastic reaction-diffusion dynamics of synaptic proteins provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the single-molecule trajectories observed for synaptic proteins, and spatially inhomogeneous protein lifetimes at the cell membrane. Our results suggest that central aspects of the single-molecule and collective dynamics observed for membrane protein domains can be understood in terms of stochastic reaction-diffusion processes at the cell membrane.
Stochastic lattice model of synaptic membrane protein domains
Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A.
2017-05-01
Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.
Stochastic lattice model of synaptic membrane protein domains.
Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A
2017-05-01
Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.
Input significance analysis: feature selection through synaptic ...
African Journals Online (AJOL)
This work is interested in ISA methods that can manipulate synaptic weights namely. Connection Weights (CW) and Garson's Algorithm (GA) and the classifier selected is. Evolving Fuzzy Neural Networks (EFuNNs). Firstly, it test FS method on a dataset selected from the UCI Machine Learning Repository and executed in an ...
Stochastic synaptic plasticity with memristor crossbar arrays
Naous, Rawan
2016-11-01
Memristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.
Memristor-based neural networks: Synaptic versus neuronal stochasticity
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.
Characterization of auditory synaptic inputs to gerbil perirhinal cortex
Directory of Open Access Journals (Sweden)
Vibhakar C Kotak
2015-08-01
Full Text Available The representation of acoustic cues involves regions downstream from the auditory cortex (ACx. One such area, the perirhinal cortex (PRh, processes sensory signals containing mnemonic information. Therefore, our goal was to assess whether PRh receives auditory inputs from the auditory thalamus (MG and ACx in an auditory thalamocortical brain slice preparation and characterize these afferent-driven synaptic properties. When the MG or ACx was electrically stimulated, synaptic responses were recorded from the PRh neurons. Blockade of GABA-A receptors dramatically increased the amplitude of evoked excitatory potentials. Stimulation of the MG or ACx also evoked calcium transients in most PRh neurons. Separately, when fluoro ruby was injected in ACx in vivo, anterogradely labeled axons and terminals were observed in the PRh. Collectively, these data show that the PRh integrates auditory information from the MG and ACx and that auditory driven inhibition dominates the postsynaptic responses in a non-sensory cortical region downstream from the auditory cortex.
Cooperative stochastic binding and unbinding explain synaptic size dynamics and statistics.
Shomar, Aseel; Geyrhofer, Lukas; Ziv, Noam E; Brenner, Naama
2017-07-01
Synapses are dynamic molecular assemblies whose sizes fluctuate significantly over time-scales of hours and days. In the current study, we examined the possibility that the spontaneous microscopic dynamics exhibited by synaptic molecules can explain the macroscopic size fluctuations of individual synapses and the statistical properties of synaptic populations. We present a mesoscopic model, which ties the two levels. Its basic premise is that synaptic size fluctuations reflect cooperative assimilation and removal of molecules at a patch of postsynaptic membrane. The introduction of cooperativity to both assimilation and removal in a stochastic biophysical model of these processes, gives rise to features qualitatively similar to those measured experimentally: nanoclusters of synaptic scaffolds, fluctuations in synaptic sizes, skewed, stable size distributions and their scaling in response to perturbations. Our model thus points to the potentially fundamental role of cooperativity in dictating synaptic remodeling dynamics and offers a conceptual understanding of these dynamics in terms of central microscopic features and processes.
Cooperative stochastic binding and unbinding explain synaptic size dynamics and statistics.
Directory of Open Access Journals (Sweden)
Aseel Shomar
2017-07-01
Full Text Available Synapses are dynamic molecular assemblies whose sizes fluctuate significantly over time-scales of hours and days. In the current study, we examined the possibility that the spontaneous microscopic dynamics exhibited by synaptic molecules can explain the macroscopic size fluctuations of individual synapses and the statistical properties of synaptic populations. We present a mesoscopic model, which ties the two levels. Its basic premise is that synaptic size fluctuations reflect cooperative assimilation and removal of molecules at a patch of postsynaptic membrane. The introduction of cooperativity to both assimilation and removal in a stochastic biophysical model of these processes, gives rise to features qualitatively similar to those measured experimentally: nanoclusters of synaptic scaffolds, fluctuations in synaptic sizes, skewed, stable size distributions and their scaling in response to perturbations. Our model thus points to the potentially fundamental role of cooperativity in dictating synaptic remodeling dynamics and offers a conceptual understanding of these dynamics in terms of central microscopic features and processes.
DEFF Research Database (Denmark)
Kolind, Jens; Hounsgaard, Jørn Dybkjær; Berg, Rune W
2012-01-01
Neurons often receive massive concurrent bombardment of synaptic inhibition and excitation during functional network activity. This increases membrane conductance and causes fluctuations in membrane potential (V(m)) and spike timing. The conductance increase is commonly attributed to synaptic...... conductance, but also includes the intrinsic conductances recruited during network activity. These two sources of conductance have contrasting dynamic properties at sub-threshold membrane potentials. Synaptic transmitter gated conductance changes abruptly and briefly with each presynaptic action potential...... input we find that the magnitude of the membrane fluctuations uniquely determines the relative contribution of synaptic and intrinsic conductance. We also quantify how V(m)-fluctuations vary with synaptic correlations for fixed ratios of synaptic and intrinsic conductance. Interestingly, the levels of V...
Directory of Open Access Journals (Sweden)
Maxim Volgushev
2015-03-01
Full Text Available Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1 How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2 What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results
A probabilistic graphical model based stochastic input model construction
International Nuclear Information System (INIS)
Wan, Jiang; Zabaras, Nicholas
2014-01-01
Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media
Learning structure of sensory inputs with synaptic plasticity leads to interference.
Chrol-Cannon, Joseph; Jin, Yaochu
2015-01-01
Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience.
A stochastic model of input effectiveness during irregular gamma rhythms.
Dumont, Grégory; Northoff, Georg; Longtin, André
2016-02-01
Gamma-band synchronization has been linked to attention and communication between brain regions, yet the underlying dynamical mechanisms are still unclear. How does the timing and amplitude of inputs to cells that generate an endogenously noisy gamma rhythm affect the network activity and rhythm? How does such "communication through coherence" (CTC) survive in the face of rhythm and input variability? We present a stochastic modelling approach to this question that yields a very fast computation of the effectiveness of inputs to cells involved in gamma rhythms. Our work is partly motivated by recent optogenetic experiments (Cardin et al. Nature, 459(7247), 663-667 2009) that tested the gamma phase-dependence of network responses by first stabilizing the rhythm with periodic light pulses to the interneurons (I). Our computationally efficient model E-I network of stochastic two-state neurons exhibits finite-size fluctuations. Using the Hilbert transform and Kuramoto index, we study how the stochastic phase of its gamma rhythm is entrained by external pulses. We then compute how this rhythmic inhibition controls the effectiveness of external input onto pyramidal (E) cells, and how variability shapes the window of firing opportunity. For transferring the time variations of an external input to the E cells, we find a tradeoff between the phase selectivity and depth of rate modulation. We also show that the CTC is sensitive to the jitter in the arrival times of spikes to the E cells, and to the degree of I-cell entrainment. We further find that CTC can occur even if the underlying deterministic system does not oscillate; quasicycle-type rhythms induced by the finite-size noise retain the basic CTC properties. Finally a resonance analysis confirms the relative importance of the I cell pacing for rhythm generation. Analysis of whole network behaviour, including computations of synchrony, phase and shifts in excitatory-inhibitory balance, can be further sped up by orders of
Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference
Directory of Open Access Journals (Sweden)
Joseph eChrol-Cannon
2015-08-01
Full Text Available Synaptic plasticity is often explored as a form of unsupervised adaptationin cortical microcircuits to learn the structure of complex sensoryinputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data.In this work, input-specific structural changes are analyzed forthree empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP and the Bienenstock-Cooper-Munro (BCM plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks.It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by thepresentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network.To solve the problem of interference, we suggest that models of plasticitybe extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the casein experimental neuroscience.
Choice of input fields in stochastic finite elements
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1999-01-01
, the flexibility field, as the input to the stochastic finite element model. To answer this question the focus should be on the error of the output of the mechanical model rather than on the input field itself when discretizing the held through replacing it by a field defined in terms of a finite number of random...... variables. Several reported discretization methods define these random variables as integrals of the product of the held and some suitable weight functions. In particular, the weight functions can be Dirac delta functions whereby the random variables become the field values at a finite set of given points...... the differential equation of the column displacement and the relevant boundary conditions, it can be expected that the discretization of the flexibility field is preferable over the discretization of the stiffness field. Direct mechanical considerations support this expectation. (C) 1998 Published by Elsevier...
Choice of input fields in stochastic finite elements
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1996-01-01
, the flexibility field, as the input to the stochastic finite element model. To answer this question the focus should be on the error of the output of the mechanical model rather than on the input field itself when discretizing the field through replacing it by a field defined in terms of a finite number of random...... variables. Several reported discretization methods define these random variables as integrals of the product of the field and some suitable weight functions. In particular, the weight functions can be Dirac delta functions whereby the random variables become the field values at a finite set of given points...... the differential equation of the column displacement and the relevant boundarv conditions, it can be expected that the discretization of the flexibility field is preferable over the discretization of the stiffness field. Direct mechanical considerations support this expectation.Keywords: Random stiffness...
Choice of input fields in stochastic finite elements
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1996-01-01
variables. Several reported discretization methods define these random variables as integrals of the product of the field and some suitable weight functions. In particular, the weight functions can be Dirac delta functions whereby the random variables become the field values at a finite set of given points......The problem of the arbitrary choice of variables for random field modelling in structural mechanics or in soil mechanics is treated. For example, it is relevant to ask the question of whether it is best to choose a stiffness field along a beam element or to choose its reciprocal field......, the flexibility field, as the input to the stochastic finite element model. To answer this question the focus should be on the error of the output of the mechanical model rather than on the input field itself when discretizing the field through replacing it by a field defined in terms of a finite number of random...
Short- and long-term modulation of synaptic inputs to brain reward areas by nicotine
Fagen, Z.M.; Mansvelder, H.D.; Keath, R.; McGehee, D.S.
2003-01-01
Dopamine signaling in brain reward areas is a key element in the development of drug abuse and dependence. Recent anatomical and electrophysiological research has begun to elucidate both complexity and specificity In synaptic connections between ventral tegmental neurons and their inputs.
Mathews, Miranda A.; Murray, Andrew; Wijesinghe, Rajiv; Cullen, Karen; Tung, Victoria W. K.; Camp, Aaron J.
2015-01-01
Despite the importance of our sense of balance we still know remarkably little about the central control of the peripheral balance system. While previous work has shown that activation of the efferent vestibular system results in modulation of afferent vestibular neuron discharge, the intrinsic and synaptic properties of efferent neurons themselves are largely unknown. Here we substantiate the location of the efferent vestibular nucleus (EVN) in the mouse, before characterizing the input and output properties of EVN neurons in vitro. We made transverse serial sections through the brainstem of 4-week-old mice, and performed immunohistochemistry for calcitonin gene-related peptide (CGRP) and choline acetyltransferase (ChAT), both expressed in the EVN of other species. We also injected fluorogold into the posterior canal and retrogradely labelled neurons in the EVN of ChAT:: tdTomato mice expressing tdTomato in all cholinergic neurons. As expected the EVN lies dorsolateral to the genu of the facial nerve (CNVII). We then made whole-cell current-, and voltage-clamp recordings from visually identified EVN neurons. In current-clamp, EVN neurons display a homogeneous discharge pattern. This is characterized by a high frequency burst of action potentials at the onset of a depolarizing stimulus and the offset of a hyperpolarizing stimulus that is mediated by T-type calcium channels. In voltage-clamp, EVN neurons receive either exclusively excitatory or inhibitory inputs, or a combination of both. Despite this heterogeneous mixture of inputs, we show that synaptic inputs onto EVN neurons are predominantly excitatory. Together these findings suggest that the inputs onto EVN neurons, and more specifically the origin of these inputs may underlie EVN neuron function. PMID:26422206
Directory of Open Access Journals (Sweden)
Miranda A Mathews
Full Text Available Despite the importance of our sense of balance we still know remarkably little about the central control of the peripheral balance system. While previous work has shown that activation of the efferent vestibular system results in modulation of afferent vestibular neuron discharge, the intrinsic and synaptic properties of efferent neurons themselves are largely unknown. Here we substantiate the location of the efferent vestibular nucleus (EVN in the mouse, before characterizing the input and output properties of EVN neurons in vitro. We made transverse serial sections through the brainstem of 4-week-old mice, and performed immunohistochemistry for calcitonin gene-related peptide (CGRP and choline acetyltransferase (ChAT, both expressed in the EVN of other species. We also injected fluorogold into the posterior canal and retrogradely labelled neurons in the EVN of ChAT:: tdTomato mice expressing tdTomato in all cholinergic neurons. As expected the EVN lies dorsolateral to the genu of the facial nerve (CNVII. We then made whole-cell current-, and voltage-clamp recordings from visually identified EVN neurons. In current-clamp, EVN neurons display a homogeneous discharge pattern. This is characterized by a high frequency burst of action potentials at the onset of a depolarizing stimulus and the offset of a hyperpolarizing stimulus that is mediated by T-type calcium channels. In voltage-clamp, EVN neurons receive either exclusively excitatory or inhibitory inputs, or a combination of both. Despite this heterogeneous mixture of inputs, we show that synaptic inputs onto EVN neurons are predominantly excitatory. Together these findings suggest that the inputs onto EVN neurons, and more specifically the origin of these inputs may underlie EVN neuron function.
Directory of Open Access Journals (Sweden)
Marco Tripodi
2008-10-01
Full Text Available As the nervous system develops, there is an inherent variability in the connections formed between differentiating neurons. Despite this variability, neural circuits form that are functional and remarkably robust. One way in which neurons deal with variability in their inputs is through compensatory, homeostatic changes in their electrical properties. Here, we show that neurons also make compensatory adjustments to their structure. We analysed the development of dendrites on an identified central neuron (aCC in the late Drosophila embryo at the stage when it receives its first connections and first becomes electrically active. At the same time, we charted the distribution of presynaptic sites on the developing postsynaptic arbor. Genetic manipulations of the presynaptic partners demonstrate that the postsynaptic dendritic arbor adjusts its growth to compensate for changes in the activity and density of synaptic sites. Blocking the synthesis or evoked release of presynaptic neurotransmitter results in greater dendritic extension. Conversely, an increase in the density of presynaptic release sites induces a reduction in the extent of the dendritic arbor. These growth adjustments occur locally in the arbor and are the result of the promotion or inhibition of growth of neurites in the proximity of presynaptic sites. We provide evidence that suggest a role for the postsynaptic activity state of protein kinase A in mediating this structural adjustment, which modifies dendritic growth in response to synaptic activity. These findings suggest that the dendritic arbor, at least during early stages of connectivity, behaves as a homeostatic device that adjusts its size and geometry to the level and the distribution of input received. The growing arbor thus counterbalances naturally occurring variations in synaptic density and activity so as to ensure that an appropriate level of input is achieved.
Quilty, J.; Adamowski, J. F.
2015-12-01
Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.
Relevance of neuronal and glial NPC1 for synaptic input to cerebellar Purkinje cells.
Buard, Isabelle; Pfrieger, Frank W
2014-07-01
Niemann-Pick type C disease is a rare and ultimately fatal lysosomal storage disorder with variable neurologic symptoms. The disease-causing mutations concern NPC1 or NPC2, whose dysfunction entails accumulation of cholesterol in the endosomal-lysosomal system and the selective death of specific neurons, namely cerebellar Purkinje cells. Here, we investigated whether neurodegeneration is preceded by an imbalance of synaptic input to Purkinje cells and whether neuronal or glial absence of NPC1 has different impacts on synapses. To this end, we prepared primary cerebellar cultures from wildtype or NPC1-deficient mice that are glia-free and highly enriched with Purkinje cells. We report that lack of NPC1 in either neurons or glial cells did not affect the excitability of Purkinje cells, the formation of dendrites or their excitatory synaptic activity. However, simultaneous absence of NPC1 from neuronal and glial cells impaired the presynaptic input to Purkinje cells suggesting a cooperative effect of neuronal and glial NPC1 on synapses. Copyright © 2014. Published by Elsevier Inc.
Directory of Open Access Journals (Sweden)
Milad eLankarany
2013-09-01
Full Text Available Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The Kalman filtering is followed by an expectation maximization algorithm to infer the statistical parameters (time-varying mean and variance of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model. The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012 with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.
Kheirollahi, Hooshang; Matin, Behzad Karami; Mahboubi, Mohammad; Alavijeh, Mehdi Mirzaei
2015-01-01
This article developed an approached model of congestion, based on relaxed combination of inputs, in stochastic data envelopment analysis (SDEA) with chance constrained programming approaches. Classic data envelopment analysis models with deterministic data have been used by many authors to identify congestion and estimate its levels; however, data envelopment analysis with stochastic data were rarely used to identify congestion. This article used chance constrained programming approaches to replace stochastic models with "deterministic equivalents". This substitution leads us to non-linear problems that should be solved. Finally, the proposed method based on relaxed combination of inputs was used to identify congestion input in six Iranian hospital with one input and two outputs in the period of 2009 to 2012.
Directory of Open Access Journals (Sweden)
Ayla eAksoy Aksel
2013-08-01
Full Text Available In terms of its sub-regional differentiation, the hippocampal CA1 region receives cortical information directly via the perforant (temporoammonic path (pp-CA1 synapse and indirectly via the tri-synaptic pathway where the last relay station is the Schaffer collateral-CA1 synapse (Sc-CA1 synapse. Research to date on pp-CA1 synapses has been conducted predominantly in vitro and never in awake animals, but these studies hint that information processing at this synapse might be distinct to processing at the Sc-CA1 synapse. Here, we characterized synaptic properties and synaptic plasticity at the pp-CA1 synapse of freely behaving adult rats. We established that field excitatory postsynaptic potentials at the pp-CA1 have longer onset latencies and a shorter time-to-peak compared to the Sc-CA1 synapse. LTP (> 24h was successfully evoked by tetanic afferent stimulation of pp-CA1 synapses. Low frequency stimulation evoked synaptic depression at Sc-CA1 synapses, but did not elicit LTD at pp-CA1 synapses unless the Schaffer collateral afferents to the CA1 region had been severed. Paired-pulse responses also showed significant differences. Our data suggest that synaptic plasticity at the pp-CA1 synapse is distinct from the Sc-CA1 synapse and that this may reflect its specific role in hippocampal information processing.
Czech Academy of Sciences Publication Activity Database
Kobayashi, R.; He, J.; Lánský, Petr
2015-01-01
Roč. 9, May 18 (2015), s. 59 ISSN 1662-5188 R&D Projects: GA ČR(CZ) GA15-08066S Institutional support: RVO:67985823 Keywords : synaptic inputs * statistical inference * state-space models * intracellular recordings * auditory cortex Subject RIV: BD - Theory of Information Impact factor: 2.653, year: 2015
On a multi-channel stochastic network with controlled input
Livinska, Hanna; Lebedev, Eugene
2017-06-01
In this paper stationary properties of queueing network of the type [M|M|∞]r are investigated provided that the input flow is controlled by a Markov chain. We consider two cases. In the one-dimensional case a generating function of the stationary distribution is obtained. The form of the generating function is a matrix version of the well-known Takasc formula. For a multivariate service process the condition of a stationary regime existence and a correlation matrix are found.
Directory of Open Access Journals (Sweden)
Mark D McDonnell
2013-05-01
Full Text Available The release of neurotransmitter vesicles after arrival of a pre-synaptic action potential at cortical synapses is known to be a stochastic process, as is the availability of vesicles for release. These processes are known to also depend on the recent history of action-potential arrivals, and this can be described in terms of time-varying probabilities of vesicle release. Mathematical models of such synaptic dynamics frequently are based only on the mean number of vesicles released by each pre-synaptic action potential, since if it is assumed there are sufficiently many vesicle sites, then variance is small. However, it has been shown recently that variance across sites can be significant for neuron and network dynamics, and this suggests the potential importance of studying short-term plasticity using simulations that do generate trial-to-trial variability. Therefore, in this paper we study several well-known conceptual models for stochastic availability and release. We state explicitly the random variables that these models describe and propose efficient algorithms for accurately implementing stochastic simulations of these random variables in software or hardware. Our results are complemented by mathematical analysis and statement of pseudo-code algorithms.
Yan, Xiaodong; Tian, He; Xie, Yujun; Kostelec, Andrew; Zhao, Huan; Cha, Judy J.; Tice, Jesse; Wang, Han
Modulatory input-dependent plasticity is a well-known type of hetero-synaptic response where the release of neuromodulators can alter the efficacy of neurotransmission in a nearby chemical synapse. Solid-state devices that can mimic such phenomenon are desirable for enhancing the functionality and reconfigurability of neuromorphic electronics. In this work, we demonstrated a tunable artificial synaptic device concept based on the properties of graphene and tin oxide that can mimic the modulatory input-dependent plasticity. By using graphene as the contact electrode, a third electrode terminal can be used to modulate the conductive filament formation in the vertical tin oxide based resistive memory device. The resulting synaptic characteristics of this device, in terms of the profile of synaptic weight change and the spike-timing-dependent-plasticity, is tunable with the bias at the modulating terminal. Furthermore, the synaptic response can be reconfigured between excitatory and inhibitory modes by this modulating bias. The operation mechanism of the device is studied with combined experimental and theoretical analysis. The device is attractive for application in neuromorphic electronics. This work is supported by ARO and NG-ION2 at USC.
α-Motor neurons are spared from aging while their synaptic inputs degenerate in monkeys and mice.
Maxwell, Nicholas; Castro, Ryan W; Sutherland, Natalia M; Vaughan, Kelli L; Szarowicz, Mark D; de Cabo, Rafael; Mattison, Julie A; Valdez, Gregorio
2018-02-04
Motor function deteriorates with advancing age, increasing the risk of adverse health outcomes. While it is well established that skeletal muscles and neuromuscular junctions (NMJs) degenerate with increasing age, the effect of aging on α-motor neurons and their innervating synaptic inputs remains largely unknown. In this study, we examined the soma of α-motor neurons and innervating synaptic inputs in the spinal cord of aged rhesus monkeys and mice, two species with vastly different lifespans. We found that, in both species, α-motor neurons retain their soma size despite an accumulation of large amounts of cellular waste or lipofuscin. Interestingly, the lipofuscin profile varied considerably, indicating that α-motor neurons age at different rates. Although the rate of aging varies, α-motor neurons do not atrophy in old age. In fact, there is no difference in the number of motor axons populating ventral roots in old mice compared to adult mice. Moreover, the transcripts and proteins associated with α-motor neurons do not decrease in the spinal cord of old mice. However, in aged rhesus monkeys and mice, there were fewer cholinergic and glutamatergic synaptic inputs directly abutting α-motor neurons, evidence that aging causes α-motor neurons to shed synaptic inputs. Thus, the loss of synaptic inputs may contribute to age-related dysfunction of α-motor neurons. These findings broaden our understanding of the degeneration of the somatic motor system that precipitates motor dysfunction with advancing age. © 2018 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Sugimura, Yae K.; Takahashi, Yukari; Watabe, Ayako M.
2016-01-01
A large majority of neurons in the superficial layer of the dorsal horn projects to the lateral parabrachial nucleus (LPB). LPB neurons then project to the capsular part of the central amygdala (CeA; CeC), a key structure underlying the nociception-emotion link. LPB-CeC synaptic transmission is enhanced in various pain models by using electrical stimulation of putative fibers of LPB origin in brain slices. However, this approach has limitations for examining direct monosynaptic connections devoid of directly stimulating fibers from other structures and local GABAergic neurons. To overcome these limitations, we infected the LPB of rats with an adeno-associated virus vector expressing channelrhodopsin-2 and prepared coronal and horizontal brain slices containing the amygdala. We found that blue light stimulation resulted in monosynaptic excitatory postsynaptic currents (EPSCs), with very small latency fluctuations, followed by a large polysynaptic inhibitory postsynaptic current in CeC neurons, regardless of the firing pattern type. Intraplantar formalin injection at 24 h before slice preparation significantly increased EPSC amplitude in late firing-type CeC neurons. These results indicate that direct monosynaptic glutamatergic inputs from the LPB not only excite CeC neurons but also regulate CeA network signaling through robust feed-forward inhibition, which is under plastic modulation in response to persistent inflammatory pain. PMID:26888105
Optimal control of linear, stochastic systems with state and input constraints
Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep
2002-01-01
In this paper we extend the work presented in our previous papers (2001) where we considered optimal control of a linear, discrete time system subject to input constraints and stochastic disturbances. Here we basically look at the same problem but we additionally consider state constraints. We
A non-linear dimension reduction methodology for generating data-driven stochastic input models
Ganapathysubramanian, Baskar; Zabaras, Nicholas
2008-06-01
Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low
A non-linear dimension reduction methodology for generating data-driven stochastic input models
International Nuclear Information System (INIS)
Ganapathysubramanian, Baskar; Zabaras, Nicholas
2008-01-01
Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R n . An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R d (d<< n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology
Tornero, Daniel; Tsupykov, Oleg; Granmo, Marcus; Rodriguez, Cristina; Grønning-Hansen, Marita; Thelin, Jonas; Smozhanik, Ekaterina; Laterza, Cecilia; Wattananit, Somsak; Ge, Ruimin; Tatarishvili, Jemal; Grealish, Shane; Brüstle, Oliver; Skibo, Galina; Parmar, Malin; Schouenborg, Jens; Lindvall, Olle; Kokaia, Zaal
2017-03-01
Transplanted neurons derived from stem cells have been proposed to improve function in animal models of human disease by various mechanisms such as neuronal replacement. However, whether the grafted neurons receive functional synaptic inputs from the recipient's brain and integrate into host neural circuitry is unknown. Here we studied the synaptic inputs from the host brain to grafted cortical neurons derived from human induced pluripotent stem cells after transplantation into stroke-injured rat cerebral cortex. Using the rabies virus-based trans-synaptic tracing method and immunoelectron microscopy, we demonstrate that the grafted neurons receive direct synaptic inputs from neurons in different host brain areas located in a pattern similar to that of neurons projecting to the corresponding endogenous cortical neurons in the intact brain. Electrophysiological in vivo recordings from the cortical implants show that physiological sensory stimuli, i.e. cutaneous stimulation of nose and paw, can activate or inhibit spontaneous activity in grafted neurons, indicating that at least some of the afferent inputs are functional. In agreement, we find using patch-clamp recordings that a portion of grafted neurons respond to photostimulation of virally transfected, channelrhodopsin-2-expressing thalamo-cortical axons in acute brain slices. The present study demonstrates, for the first time, that the host brain regulates the activity of grafted neurons, providing strong evidence that transplanted human induced pluripotent stem cell-derived cortical neurons can become incorporated into injured cortical circuitry. Our findings support the idea that these neurons could contribute to functional recovery in stroke and other conditions causing neuronal loss in cerebral cortex. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
A Diffusion Approximation and Numerical Methods for Adaptive Neuron Models with Stochastic Inputs.
Rosenbaum, Robert
2016-01-01
Characterizing the spiking statistics of neurons receiving noisy synaptic input is a central problem in computational neuroscience. Monte Carlo approaches to this problem are computationally expensive and often fail to provide mechanistic insight. Thus, the field has seen the development of mathematical and numerical approaches, often relying on a Fokker-Planck formalism. These approaches force a compromise between biological realism, accuracy and computational efficiency. In this article we develop an extension of existing diffusion approximations to more accurately approximate the response of neurons with adaptation currents and noisy synaptic currents. The implementation refines existing numerical schemes for solving the associated Fokker-Planck equations to improve computationally efficiency and accuracy. Computer code implementing the developed algorithms is made available to the public.
Characterization of memory states of the Preisach operator with stochastic inputs
Amann, A.; Brokate, M.; McCarthy, S.; Rachinskii, D.; Temnov, G.
2012-05-01
The Preisach operator with inputs defined by a Markov process xt is considered. The question we address is: what is the distribution of the random memory state of the Preisach operator at a given time moment t0 in the limit r→∞ of infinitely long input history xt, t0-r≤t≤t0? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (mk,Mk) of elements from the monotone sequences of the local minimum input values mk and the local maximum input values Mk recorded in the memory state and the index k of the elements plays the role of time. We express the transition probabilities of this Markov chain in terms of the transition probabilities of the input stochastic process and show that the memory state Markov chain and the input process generate the same distribution of the memory states. These results are illustrated by several examples of stochastic inputs such as the Wiener and Bernoulli processes and their mixture (we first discuss a discrete version of these processes and then the continuous time and state setting). The memory state Markov chain is then used to find the distribution of the random number of elements in the memory state sequence. We show that this number has the Poisson distribution for the Wiener and Bernoulli processes inputs. In particular, in the discrete setting, the mean value of the number of elements in the memory state scales as ln N, where N is the number of the input states, while the mean time it takes the input to generate this memory state scales as N2 for the Wiener process and as N for the Bernoulli process. A similar relationship between the dimension of the memory state vector and the number of iterations in the numerical realization of the input is shown for the mixture of the Wiener and Bernoulli processes, thus confirming that the memory state Markov chain is an efficient tool for generating the distribution of the Preisach operator memory states
Characterization of memory states of the Preisach operator with stochastic inputs
International Nuclear Information System (INIS)
Amann, A.; Brokate, M.; McCarthy, S.; Rachinskii, D.; Temnov, G.
2012-01-01
The Preisach operator with inputs defined by a Markov process x t is considered. The question we address is: what is the distribution of the random memory state of the Preisach operator at a given time moment t 0 in the limit r→∞ of infinitely long input history x t , t 0 -r≤t≤t 0 ? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (m k ,M k ) of elements from the monotone sequences of the local minimum input values m k and the local maximum input values M k recorded in the memory state and the index k of the elements plays the role of time. We express the transition probabilities of this Markov chain in terms of the transition probabilities of the input stochastic process and show that the memory state Markov chain and the input process generate the same distribution of the memory states. These results are illustrated by several examples of stochastic inputs such as the Wiener and Bernoulli processes and their mixture (we first discuss a discrete version of these processes and then the continuous time and state setting). The memory state Markov chain is then used to find the distribution of the random number of elements in the memory state sequence. We show that this number has the Poisson distribution for the Wiener and Bernoulli processes inputs. In particular, in the discrete setting, the mean value of the number of elements in the memory state scales as lnN, where N is the number of the input states, while the mean time it takes the input to generate this memory state scales as N 2 for the Wiener process and as N for the Bernoulli process. A similar relationship between the dimension of the memory state vector and the number of iterations in the numerical realization of the input is shown for the mixture of the Wiener and Bernoulli processes, thus confirming that the memory state Markov chain is an efficient tool for generating the distribution of the Preisach operator memory
Cheng, Yu; Ye, Dong; Sun, Zhaowei; Zhang, Shijie
2018-03-01
This paper proposes a novel feedback control law for spacecraft to deal with attitude constraint, input saturation, and stochastic disturbance during the attitude reorientation maneuver process. Applying the parameter selection method to improving the existence conditions for the repulsive potential function, the universality of the potential-function-based algorithm is enhanced. Moreover, utilizing the auxiliary system driven by the difference between saturated torque and command torque, a backstepping control law, which satisfies the input saturation constraint and guarantees the spacecraft stability, is presented. Unlike some methods that passively rely on the inherent characteristic of the existing controller to stabilize the adverse effects of external stochastic disturbance, this paper puts forward a nonlinear disturbance observer to compensate the disturbance in real-time, which achieves a better performance of robustness. The simulation results validate the effectiveness, reliability, and universality of the proposed control law.
Oe, Yuki; Tominaga-Yoshino, Keiko; Ogura, Akihiko
2011-09-01
Long-term potentiation (LTP) in the rodent hippocampus is a popular model for synaptic plasticity, which is considered the cellular basis for brain memory. Because most LTP analysis involves acutely prepared brain slices, however, the longevity of single LTP has not been well documented. Using stable hippocampal slice cultures for long-term examination, we previously found that single LTP disappeared within 1 day. In contrast, repeated induction of LTP led to the development of a distinct type of plasticity that lasted for more than 3 weeks and was accompanied by the formation of new synapses. Naming this novel plastic phenomenon repetitive LTP-induced synaptic enhancement (RISE), we proposed it as a model for the cellular processes involved in long-term memory formation. However, because in those experiments LTP was induced pharmacologically in the whole slice, it is not known whether RISE has input-pathway specificity, an essential property for memory. In this study, we divided the input pathway of CA1 pyramidal neurons by a knife cut and induced LTP three times, the third by tetanic stimulation in one of the divided pathways to express RISE specifically. Voltage-sensitive dye imaging and Golgi-staining performed 2 weeks after the three LTP inductions revealed both enhanced synaptic strength and increased dendritic spine density confined to the tetanized region. These results demonstrate that RISE is a feasible cellular model for long-term memory. Copyright © 2011 Wiley-Liss, Inc.
Distribution of return point memory states for systems with stochastic inputs
International Nuclear Information System (INIS)
Amann, A; Brokate, M; Rachinskii, D; Temnov, G
2011-01-01
We consider the long term effect of stochastic inputs on the state of an open loop system which exhibits the so-called return point memory. An example of such a system is the Preisach model; more generally, systems with the Preisach type input-state relationship, such as in spin-interaction models, are considered. We focus on the characterisation of the expected memory configuration after the system has been effected by the input for sufficiently long period of time. In the case where the input is given by a discrete time random walk process, or the Wiener process, simple closed form expressions for the probability density of the vector of the main input extrema recorded by the memory state, and scaling laws for the dimension of this vector, are derived. If the input is given by a general continuous Markov process, we show that the distribution of previous memory elements can be obtained from a Markov chain scheme which is derived from the solution of an associated one-dimensional escape type problem. Formulas for transition probabilities defining this Markov chain scheme are presented. Moreover, explicit formulas for the conditional probability densities of previous main extrema are obtained for the Ornstein-Uhlenbeck input process. The analytical results are confirmed by numerical experiments.
A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
Babuška, Ivo
2010-01-01
This work proposes and analyzes a stochastic collocation method for solving elliptic partial differential equations with random coefficients and forcing terms. These input data are assumed to depend on a finite number of random variables. The method consists of a Galerkin approximation in space and a collocation in the zeros of suitable tensor product orthogonal polynomials (Gauss points) in the probability space, and naturally leads to the solution of uncoupled deterministic problems as in the Monte Carlo approach. It treats easily a wide range of situations, such as input data that depend nonlinearly on the random variables, diffusivity coefficients with unbounded second moments, and random variables that are correlated or even unbounded. We provide a rigorous convergence analysis and demonstrate exponential convergence of the “probability error” with respect to the number of Gauss points in each direction of the probability space, under some regularity assumptions on the random input data. Numerical examples show the effectiveness of the method. Finally, we include a section with developments posterior to the original publication of this work. There we review sparse grid stochastic collocation methods, which are effective collocation strategies for problems that depend on a moderately large number of random variables.
Directory of Open Access Journals (Sweden)
Peter A Appleby
Full Text Available Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short- or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus
Directory of Open Access Journals (Sweden)
Manuel eLevy
2012-12-01
Full Text Available Uncovering the functional properties of individual synaptic inputs on single neurons is critical for understanding the computational role of synapses and dendrites. Previous studies combined whole-cell patch recording to load neurons with a fluorescent calcium indicator and two-photon imaging to map subcellular changes in fluorescence upon sensory stimulation. By hyperpolarizing the neuron below spike threshold, the patch electrode ensured that changes in fluorescence associated with synaptic events were isolated from those caused by back-propagating action potentials. This technique holds promise for determining whether the existence of unique cortical feature maps across different species may be associated with distinct wiring diagrams. However, the use of whole-cell patch for mapping inputs on dendrites is challenging in large mammals, due to brain pulsations and the accumulation of fluorescent dye in the extracellular milieu. Alternatively, sharp intracellular electrodes have been used to label neurons with fluorescent dyes, but the current passing capabilities of these high impedance electrodes may be insufficient to prevent spiking. In this study, we tested whether sharp electrode recording is suitable for mapping functional inputs on dendrites in the cat visual cortex. We compared three different strategies for suppressing visually evoked spikes: (1 hyperpolarization by intracellular current injection, (2 pharmacological blockade of voltage-gated sodium channels by intracellular QX-314, and (3 GABA iontophoresis from a perisomatic electrode glued to the intracellular electrode. We found that functional inputs on dendrites could be successfully imaged using all three strategies. However, the best method for preventing spikes was GABA iontophoresis with low currents (5 to 10 nA, which minimally affected the local circuit. Our methods advance the possibility of determining functional connectivity in preparations where whole-cell patch may be
Treatment of the response of a reactor to stochastic reactivity input
International Nuclear Information System (INIS)
Bansal, N.K.
1977-08-01
One of the important applications of reactor noise theory, which relies on the methematical methods for treating stochastic processes, is to determine either the confidence limits for the allowed deviations of the measured signals during normal reactor operation, or the statistical properties of their respective expectation values. In this report, we stress mainly the general mathematical aspects for treating this problem. A global description of a reactor system, perturbed by stochastic reactivity input, leads to a stochastic differential equation with parametric excitation. A discrepancy exists in literature about obtaining the correct solution of such an equation in its general frame. We discuss this discrepancy and review the work done for solving such an equation. Some recent work indicates that linearisation of system's equations is justified in most cases of reactor operations. We develop a general scheme for calculating the various covariances and correlation functions in a stable and stationary system, which is perturbed by various noise sources and where linearisation of system's equations is justified. The formulation is easily extendable to an unstable, nonstationary system, like an uncontrolled critical reactor as demonstrated. (orig.) [de
Holcomb, Paul S; Hoffpauir, Brian K; Hoyson, Mitchell C; Jackson, Dakota R; Deerinck, Thomas J; Marrs, Glenn S; Dehoff, Marlin; Wu, Jonathan; Ellisman, Mark H; Spirou, George A
2013-08-07
Hallmark features of neural circuit development include early exuberant innervation followed by competition and pruning to mature innervation topography. Several neural systems, including the neuromuscular junction and climbing fiber innervation of Purkinje cells, are models to study neural development in part because they establish a recognizable endpoint of monoinnervation of their targets and because the presynaptic terminals are large and easily monitored. We demonstrate here that calyx of Held (CH) innervation of its target, which forms a key element of auditory brainstem binaural circuitry, exhibits all of these characteristics. To investigate CH development, we made the first application of serial block-face scanning electron microscopy to neural development with fine temporal resolution and thereby accomplished the first time series for 3D ultrastructural analysis of neural circuit formation. This approach revealed a growth spurt of added apposed surface area (ASA)>200 μm2/d centered on a single age at postnatal day 3 in mice and an initial rapid phase of growth and competition that resolved to monoinnervation in two-thirds of cells within 3 d. This rapid growth occurred in parallel with an increase in action potential threshold, which may mediate selection of the strongest input as the winning competitor. ASAs of competing inputs were segregated on the cell body surface. These data suggest mechanisms to select "winning" inputs by regional reinforcement of postsynaptic membrane to mediate size and strength of competing synaptic inputs.
Virtanen, Mari A.
2018-01-10
Inhibitory control of pyramidal neurons plays a major role in governing the excitability in the brain. While spatial mapping of inhibitory inputs onto pyramidal neurons would provide important structural data on neuronal signaling, studying their distribution at the single cell level is difficult due to the lack of easily identifiable anatomical proxies. Here, we describe an approach where in utero electroporation of a plasmid encoding for fluorescently tagged gephyrin into the precursors of pyramidal cells along with ionotophoretic injection of Lucifer Yellow can reliably and specifically detect GABAergic synapses on the dendritic arbour of single pyramidal neurons. Using this technique and focusing on the basal dendritic arbour of layer 2/3 pyramidal cells of the medial prefrontal cortex, we demonstrate an intense development of GABAergic inputs onto these cells between postnatal days 10 and 20. While the spatial distribution of gephyrin clusters was not affected by the distance from the cell body at postnatal day 10, we found that distal dendritic segments appeared to have a higher gephyrin density at later developmental stages. We also show a transient increase around postnatal day 20 in the percentage of spines that are carrying a gephyrin cluster, indicative of innervation by a GABAergic terminal. Since the precise spatial arrangement of synaptic inputs is an important determinant of neuronal responses, we believe that the method described in this work may allow a better understanding of how inhibition settles together with excitation, and serve as basics for further modelling studies focusing on the geometry of dendritic inhibition during development.
Phase dependent sign changes of GABAergic synaptic input explored in-silicio and in-vitro.
Stiefel, Klaus M; Wespatat, Valérie; Gutkin, Boris; Tennigkeit, Frank; Singer, Wolf
2005-08-01
Inhibitory interactions play a crucial role in the synchronization of neuronal activity. Here we investigate the effect of GABAergic PSPs on spike timing in cortical neurons that exhibit an oscillatory modulation of their membrane potential. To this end we combined numerical simulations with in-vitro patch-clamp recordings from layer II/III pyramidal cells of the rat visual cortex. Special emphasis was placed on exploring how the reversal potential of the GABAergic synaptic currents (EGABA) and the phase relations of the PSPs relative to the oscillation cycles affect the timing of spikes riding on the depolarizing peaks of the oscillations. The simulations predicted: (1) With EGABA more negative than the oscillation minima PSPs are hyperpolarizing at all phases and thus delay or prevent spikes. (2) With EGABA being more positive than the oscillation maxima PSPs are depolarizing in a phase-independent way and lead to a phase advance of spikes. (3) In the intermediate case where EGABA lies within oscillation maxima and minima PSPs are either hyper- or depolarizing depending on their phase relations to the V(m) oscillations and can therefore either delay or advance spikes. Experiments conducted in this most interesting last configuration with biphasic PSPs agreed with the model predictions. Additional theoretical investigations revealed the effect of these PSP induced shifts in spike timing on synchronization in neuronal circuits. The results suggest that GABAergic mechanisms can assume highly specific timing functions in oscillatory networks.
Freed, Michael A
2017-11-15
Bipolar and amacrine cells presynaptic to the ON sustained α cell of mouse retina provide currents with a higher signal-to-noise power ratio (SNR) than those presynaptic to the OFF sustained α cell. Yet the ON cell loses proportionately more SNR from synaptic inputs to spike output than the OFF cell does. The higher SNR of ON bipolar cells at the beginning of the ON pathway compensates for losses incurred by the ON ganglion cell, and improves the processing of positive contrasts. ON and OFF pathways in the retina include functional pairs of neurons that, at first glance, appear to have symmetrically similar responses to brightening and darkening, respectively. Upon careful examination, however, functional pairs exhibit asymmetries in receptive field size and response kinetics. Until now, descriptions of how light-adapted retinal circuitry maintains a preponderance of signal over the noise have not distinguished between ON and OFF pathways. Here I present evidence of marked asymmetries between members of a functional pair of sustained α ganglion cells in the mouse retina. The ON cell exhibited a proportionately greater loss of signal-to-noise power ratio (SNR) from its presynaptic arrays to its postsynaptic currents. Thus the ON cell combines signal and noise from its presynaptic arrays of bipolar and amacrine cells less efficiently than the OFF cell does. Yet the inefficiency of the ON cell is compensated by its presynaptic arrays providing a higher SNR than the arrays presynaptic to the OFF cell, apparently to improve visual processing of positive contrasts. Dynamic clamp experiments were performed that introduced synaptic conductances into ON and OFF cells. When the amacrine-modulated conductance was removed, the ON cell's spike train exhibited an increase in SNR. The OFF cell, however, showed the opposite effect of removing amacrine input, which was a decrease in SNR. Thus ON and OFF cells have different modes of synaptic integration with direct effects on
Vereczki, Viktória K.; Veres, Judit M.; Müller, Kinga; Nagy, Gergö A.; Rácz, Bence; Barsy, Boglárka; Hájos, Norbert
2016-01-01
Spike generation is most effectively controlled by inhibitory inputs that target the perisomatic region of neurons. Despite the critical importance of this functional domain, very little is known about the organization of the GABAergic inputs contacting the perisomatic region of principal cells (PCs) in the basolateral amygdala. Using immunocytochemistry combined with in vitro single-cell labeling we determined the number and sources of GABAergic inputs of PCs at light and electron microscopic levels in mice. We found that the soma and proximal dendrites of PCs were innervated primarily by two neurochemically distinct basket cell types expressing parvalbumin (PVBC) or cholecystokinin and CB1 cannabinoid receptors (CCK/CB1BC). The innervation of the initial segment of PC axons was found to be parceled out by PVBCs and axo-axonic cells (AAC), as the majority of GABAergic inputs onto the region nearest to the soma (between 0 and 10 μm) originated from PVBCs, while the largest portion of the axon initial segment was innervated by AACs. Detailed morphological investigations revealed that the three perisomatic region-targeting interneuron types significantly differed in dendritic and axonal arborization properties. We found that, although individual PVBCs targeted PCs via more terminals than CCK/CB1BCs, similar numbers (15–17) of the two BC types converge onto single PCs, whereas fewer (6–7) AACs innervate the axon initial segment of single PCs. Furthermore, we estimated that a PVBC and a CCK/CB1BC may target 800–900 and 700–800 PCs, respectively, while an AAC can innervate 600–650 PCs. Thus, BCs and AACs innervate ~10 and 20% of PC population, respectively, within their axonal cloud. Our results collectively suggest, that these interneuron types may be differently affiliated within the local amygdalar microcircuits in order to fulfill specific functions in network operation during various brain states. PMID:27013983
Strauch, Christina; Manahan-Vaughan, Denise
2018-02-01
Information encoding by means of persistent changes in synaptic strength supports long-term information storage and memory in structures such as the hippocampus. In the piriform cortex (PC), that engages in the processing of associative memory, only short-term synaptic plasticity has been described to date, both in vitro and in anesthetized rodents in vivo. Whether the PC maintains changes in synaptic strength for longer periods of time is unknown: Such a property would indicate that it can serve as a repository for long-term memories. Here, we report that in freely behaving animals, frequency-dependent synaptic plasticity does not occur in the anterior PC (aPC) following patterned stimulation of the olfactory bulb (OB). Naris closure changed action potential properties of aPC neurons and enabled expression of long-term potentiation (LTP) by OB stimulation, indicating that an intrinsic ability to express synaptic plasticity is present. Odor discrimination and categorization in the aPC is supported by descending inputs from the orbitofrontal cortex (OFC). Here, OFC stimulation resulted in LTP (>4 h), suggesting that this structure plays an important role in promoting information encoding through synaptic plasticity in the aPC. These persistent changes in synaptic strength are likely to comprise a means through which long-term memories are encoded and/or retained in the PC. © The Author 2017. Published by Oxford University Press.
Directory of Open Access Journals (Sweden)
Thomas I Talpalar
2016-09-01
Full Text Available Hyperbaric environments induce the high pressure neurological syndrome (HPNS characterized by hyperexcitability of the central nervous system and memory impairment. Human divers and other animals experience the HPNS at pressures beyond 1.1 MPa. High pressure depresses synaptic transmission and alters its dynamics in various animal models. Medial perforant path (MPP synapses connecting the medial entorhinal cortex with the hippocampal formation are suppressed by 50% at 10.1MPa. Reduction of synaptic inputs is paradoxically associated with enhanced ability of dentate gyrus’ granule cells to generate spikes at high pressure. This mechanism allows MPP inputs to elicit standard granule cell outputs at 0.1 -25 Hz frequencies under hyperbaric conditions. An increased postsynaptic gain of MPP inputs probably allows diving animals to perform in hyperbaric environments, but makes them vulnerable to high intensity/frequency stimuli producing hyperexcitability. Increasing extracellular Ca2+ (Ca2+o partially reverted pressure-mediated depression of MPP inputs and increased MPP’s low-pass filter properties. We postulated that raising Ca2+o in addition to increase synaptic inputs may reduce network excitability in the dentate gyrus potentially improving its function and reducing sensitivity to high intensity and pathologic stimuli. For this matter, we activated the MPP with single and 50 Hz frequency stimuli that simulated physiologic and deleterious conditions, while assessing the granule cell’s output under various conditions of pressure and Ca2+o. Our results reveal that pressure and Ca2+o produce an inverse modulation on synaptic input strength and network excitability. These coincident phenomena suggest a potential general mechanism of networks that adjusts gain as an inverse function of synaptic inputs’ strength. Such mechanism may serve for adaptation to variable pressure and other physiological and pathological conditions and may explain the
Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.
Herzallah, Randa
2015-03-01
Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Chun eYang
2013-06-01
Full Text Available Coffee and tea contain the stimulants caffeine and theophylline. These compounds act as antagonists of adenosine receptors. Adenosine promotes sleep and its extracellular concentration rises in association with prolonged wakefulness, particularly in the basal forebrain (BF region involved in activating the cerebral cortex. However, the effect of adenosine on identified BF neurons, especially non-cholinergic neurons, is incompletely understood. Here we used whole-cell patch-clamp recordings in mouse brain slices prepared from two validated transgenic mouse lines with fluorescent proteins expressed in GABAergic or parvalbumin (PV neurons to determine the effect of adenosine. Whole-cell recordings were made BF cholinergic neurons and from BF GABAergic & PV neurons with the size (>20 µm and intrinsic membrane properties (prominent H-currents corresponding to cortically projecting neurons. A brief (2 min bath application of adenosine (100 μM decreased the frequency but not the amplitude of spontaneous excitatory postsynaptic currents in all groups of BF cholinergic, GABAergic and PV neurons we recorded. In addition, adenosine decreased the frequency of miniature EPSCs in BF cholinergic neurons. Adenosine had no effect on the frequency of spontaneous inhibitory postsynaptic currents in cholinergic neurons or GABAergic neurons with large H-currents but reduced them in a group of GABAergic neurons with smaller H-currents. All effects of adenosine were blocked by a selective, adenosine A1 receptor antagonist, cyclopentyltheophylline (CPT, 1 μM. Adenosine had no postsynaptic effects. Taken together, our work suggests that adenosine promotes sleep by an A1-receptor mediated inhibition of glutamatergic inputs to cortically-projecting cholinergic and GABA/PV neurons. Conversely, caffeine and theophylline promote attentive wakefulness by inhibiting these A1 receptors in BF thereby promoting the high-frequency oscillations in the cortex required for
Energy Technology Data Exchange (ETDEWEB)
Webster, Clayton; Tempone, Raul (Florida State University, Tallahassee, FL); Nobile, Fabio (Politecnico di Milano, Italy)
2007-12-01
This work describes the convergence analysis of a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coefficients and forcing terms (input data of the model). To compute solution statistics, the sparse grid stochastic collocation method uses approximate solutions, produced here by finite elements, corresponding to a deterministic set of points in the random input space. This naturally requires solving uncoupled deterministic problems and, as such, the derived strong error estimates for the fully discrete solution are used to compare the computational efficiency of the proposed method with the Monte Carlo method. Numerical examples illustrate the theoretical results and are used to compare this approach with several others, including the standard Monte Carlo.
Kawaguchi, Minato; Mino, Hiroyuki; Durand, Dominique M
2007-01-01
Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we investigate the effects of the location of background noise input on information transmission in a hippocampal CA1 neuron model. In the computer simulation, random sub-threshold spike trains (signal) generated by a filtered homogeneous Poisson process were presented repeatedly to the middle point of the main apical branch, while the homogeneous Poisson shot noise (background noise) was applied to a location of the dendrite in the hippocampal CA1 model consisting of the soma with a sodium, a calcium, and five potassium channels. The location of the background noise input was varied along the dendrites to investigate the effects of background noise input location on information transmission. The computer simulation results show that the information rate reached a maximum value for an optimal amplitude of the background noise amplitude. It is also shown that this optimal amplitude of the background noise is independent of the distance between the soma and the noise input location. The results also show that the location of the background noise input does not significantly affect the maximum values of the information rates generated by stochastic resonance.
Li, Yongming; Tong, Shaocheng
2017-12-01
In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.
Directory of Open Access Journals (Sweden)
Md. Sakirul Islam Khan
2017-12-01
Full Text Available Spina bifida aperta (SBA, one of the most common congenital malformations, causes lifelong neurological complications, particularly in terms of motor dysfunction. Fetuses with SBA exhibit voluntary leg movements in utero and during early neonatal life, but these disappear within the first few weeks after birth. However, the pathophysiological sequence underlying such motor dysfunction remains unclear. Additionally, because important insights have yet to be obtained from human cases, an appropriate animal model is essential. Here, we investigated the neuropathological mechanisms of progression of SBA-like motor dysfunctions in a neural tube surgery-induced chicken model of SBA at different pathogenesis points ranging from embryonic to posthatch ages. We found that chicks with SBA-like features lose voluntary leg movements and subsequently exhibit lower-limb paralysis within the first 2 weeks after hatching, coinciding with the synaptic change-induced disruption of spinal motor networks at the site of the SBA lesion in the lumbosacral region. Such synaptic changes reduced the ratio of inhibitory-to-excitatory inputs to motor neurons and were associated with a drastic loss of γ-aminobutyric acid (GABAergic inputs and upregulation of the cholinergic activities of motor neurons. Furthermore, most of the neurons in ventral horns, which appeared to be suffering from excitotoxicity during the early postnatal days, underwent apoptosis. However, the triggers of cellular abnormalization and neurodegenerative signaling were evident in the middle- to late-gestational stages, probably attributable to the amniotic fluid-induced in ovo milieu. In conclusion, we found that early neonatal loss of neurons in the ventral horn of exposed spinal cord affords novel insights into the pathophysiology of SBA-like leg dysfunction.
Park, Junchol; Choi, June-Seek
2010-01-01
Plasticity in two input pathways into the lateral nucleus of the amygdala (LA), the medial prefrontal cortex (mPFC) and the sensory thalamus, have been suggested to underlie extinction, suppression of a previously acquired conditioned response (CR) following repeated presentations of the conditioned stimulus (CS). However, little is known about…
'Quantization' of stochastic variables: description and effects on the input noise sources in a BWR
International Nuclear Information System (INIS)
Matthey, M.
1979-01-01
A set of macrostochastic and discrete variables, with Markovian properties, is used to characterize the state of a BWR, whose input noise sources are of interest. The ratio between the auto-power spectral density (APSD) of the neutron noise fluctuations and the square modulus of the transfer function (SMTF) defines 'the total input noise source' (TINS), the components of which are the different noise source corresponding to the relevant variables. A white contribution to TINS arises from the birth and death processes of neutrons in the reactor and corresponds to a 'shot noise' (SN). Non-white contributions arise from fluctuations of the neutron cross-sections caused by fuel temperature and steam content variations. These terms called 'Flicker noises' (FN) are characterized by cut-off frequencies related to time constants of reactivity feedback effects. The respective magnitudes of the shot and flicker noises depend not only on the frequency, the feedback reactivity coefficients or the power of the reactor, but also on the 'quantization' of the continuous variables introduced such as fuel temperature and steam content. The effects of this last 'quantization' on the shapes of the noise sources and their sum are presented in this paper. (author)
Three-Verb Clusters in Interference Frisian: A Stochastic Model over Sequential Syntactic Input.
Hoekstra, Eric; Versloot, Arjen
2016-03-01
Abstract Interference Frisian (IF) is a variety of Frisian, spoken by mostly younger speakers, which is heavily influenced by Dutch. IF exhibits all six logically possible word orders in a cluster of three verbs. This phenomenon has been researched by Koeneman and Postma (2006), who argue for a parameter theory, which leaves frequency differences between various orders unexplained. Rejecting Koeneman and Postma's parameter theory, but accepting their conclusion that Dutch (and Frisian) data are input for the grammar of IF, we will argue that the word order preferences of speakers of IF are determined by frequency and similarity. More specifically, three-verb clusters in IF are sensitive to: their linear left-to-right similarity to two-verb clusters and three-verb clusters in Frisian and in Dutch; the (estimated) frequency of two- and three-verb clusters in Frisian and Dutch. The model will be shown to work best if Dutch and Frisian, and two- and three-verb clusters, have equal impact factors. If different impact factors are taken, the model's predictions do not change substantially, testifying to its robustness. This analysis is in line with recent ideas that the sequential nature of human speech is more important to syntactic processes than commonly assumed, and that less burden need be put on the hierarchical dimension of syntactic structure.
DEFF Research Database (Denmark)
Berg, Rune W.; Ditlevsen, Susanne
2013-01-01
and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, (V) , and the membrane time constant,τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can...... then be inferred from (V) when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method....... The method gives best results if the synaptic input is large compared to other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically...
International Nuclear Information System (INIS)
M. Gross
2004-01-01
The purpose of this scientific analysis is to define the sampled values of stochastic (random) input parameters for (1) rockfall calculations in the lithophysal and nonlithophysal zones under vibratory ground motions, and (2) structural response calculations for the drip shield and waste package under vibratory ground motions. This analysis supplies: (1) Sampled values of ground motion time history and synthetic fracture pattern for analysis of rockfall in emplacement drifts in nonlithophysal rock (Section 6.3 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (2) Sampled values of ground motion time history and rock mechanical properties category for analysis of rockfall in emplacement drifts in lithophysal rock (Section 6.4 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (3) Sampled values of ground motion time history and metal to metal and metal to rock friction coefficient for analysis of waste package and drip shield damage to vibratory motion in ''Structural Calculations of Waste Package Exposed to Vibratory Ground Motion'' (BSC 2004 [DIRS 167083]) and in ''Structural Calculations of Drip Shield Exposed to Vibratory Ground Motion'' (BSC 2003 [DIRS 163425]). The sampled values are indices representing the number of ground motion time histories, number of fracture patterns and rock mass properties categories. These indices are translated into actual values within the respective analysis and model reports or calculations. This report identifies the uncertain parameters and documents the sampled values for these parameters. The sampled values are determined by GoldSim V6.04.007 [DIRS 151202] calculations using appropriate distribution types and parameter ranges. No software development or model development was required for these calculations. The calculation of the sampled values allows parameter uncertainty to be incorporated into the rockfall and structural response calculations that support development of the seismic scenario for the
Zhou, Chunyi; Luo, Z. David
2015-01-01
Background Upregulation of voltage-gated-calcium-channel α2δ1 subunit post spinal nerve ligation injury (SNL) or in α2δ1-overexpressing transgenic (Tg) mice correlates with tactile allodynia, a pain state mediated mainly by Aβ sensory fibers forming synaptic connections with deep dorsal horn neurons. It is not clear however whether dysregulated α2δ1 alters deep dorsal horn synaptic neurotransmission that underlies tactile allodynia development post nerve injury. Methods Tactile allodynia was tested in the SNL and α2δ1 Tg models. Miniature excitatory/inhibitory postsynaptic currents were recorded in deep dorsal horn (DDH) neurons from these animal models using whole cell patch clamp slice recording techniques.. Results There was a significant increase in the frequency, but not amplitude, of miniature excitatory postsynaptic currents (mEPSC) in DDH neurons that correlated with tactile allodynia in SNL and α2δ1 Tg mice. Gabapentin, an α2δ1 ligand that is known to block tactile allodynia in these models, also normalized mEPSC frequency dose-dependently in DDH neurons from SNL and α2δ1 Tg mice. In contrast, neither frequency nor amplitude of miniature inhibitory postsynaptic currents (mIPSC) was altered in DDH neurons from SNL and α2δ1 Tg mice. Conclusion Our data suggest that α2δ1 dysregulation is highly likely contributing to tactile allodynia through a pre-synaptic mechanism involving facilitation of excitatory synaptic neurotransmission in deep dorsal horn of spinal cord. PMID:25691360
International Nuclear Information System (INIS)
Hong, Ying-Yi; Chang, Huei-Lin; Chiu, Ching-Sheng
2010-01-01
Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown.
Hua, Changchun; Zhang, Liuliu; Guan, Xinping
2017-01-01
This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
Missler, Markus; Südhof, Thomas C.; Biederer, Thomas
2012-01-01
Chemical synapses are asymmetric intercellular junctions that mediate synaptic transmission. Synaptic junctions are organized by trans-synaptic cell adhesion molecules bridging the synaptic cleft. Synaptic cell adhesion molecules not only connect pre- and postsynaptic compartments, but also mediate trans-synaptic recognition and signaling processes that are essential for the establishment, specification, and plasticity of synapses. A growing number of synaptic cell adhesion molecules that inc...
Modeling and generating input processes
Energy Technology Data Exchange (ETDEWEB)
Johnson, M.E.
1987-01-01
This tutorial paper provides information relevant to the selection and generation of stochastic inputs to simulation studies. The primary area considered is multivariate but much of the philosophy at least is relevant to univariate inputs as well. 14 refs.
A Unifying Framework of Synaptic and Intrinsic Plasticity in Neural Populations.
Leugering, Johannes; Pipa, Gordon
2018-01-17
A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input signals can be characterized by constant statistical properties, the systematic relationship between its input and output processes determines the computation carried out by a population. When these statistical characteristics unexpectedly change, the population needs to adapt to its new environment if it is to maintain stable operation. Based on the general concept of homeostatic plasticity, we propose a simple compositional model of adaptive networks that achieve invariance with regard to undesired changes in the statistical properties of their input signals and maintain outputs with well-defined joint statistics. To achieve such invariance, the network model combines two functionally distinct types of plasticity. An abstract stochastic process neuron model implements a generalized form of intrinsic plasticity that adapts marginal statistics, relying only on mechanisms locally confined within each neuron and operating continuously in time, while a simple form of Hebbian synaptic plasticity operates on synaptic connections, thus shaping the interrelation between neurons as captured by a copula function. The combined effect of both mechanisms allows a neuron population to discover invariant representations of its inputs that remain stable under a wide range of transformations (e.g., shifting, scaling and (affine linear) mixing). The probabilistic model of homeostatic adaptation on a population level as presented here allows us to isolate and study the individual and the interaction dynamics of both mechanisms of plasticity and could guide the future search for computationally beneficial types of adaptation.
EDITORIAL: Synaptic electronics Synaptic electronics
Demming, Anna; Gimzewski, James K.; Vuillaume, Dominique
2013-09-01
Conventional computers excel in logic and accurate scientific calculations but make hard work of open ended problems that human brains handle easily. Even von Neumann—the mathematician and polymath who first developed the programming architecture that forms the basis of today's computers—was already looking to the brain for future developments before his death in 1957 [1]. Neuromorphic computing uses approaches that better mimic the working of the human brain. Recent developments in nanotechnology are now providing structures with very accommodating properties for neuromorphic approaches. This special issue, with guest editors James K Gimzewski and Dominique Vuillaume, is devoted to research at the serendipitous interface between the two disciplines. 'Synaptic electronics', looks at artificial devices with connections that demonstrate behaviour similar to synapses in the nervous system allowing a new and more powerful approach to computing. Synapses and connecting neurons respond differently to incident signals depending on the history of signals previously experienced, ultimately leading to short term and long term memory behaviour. The basic characteristics of a synapse can be replicated with around ten simple transistors. However with the human brain having around 1011 neurons and 1015 synapses, artificial neurons and synapses from basic transistors are unlikely to accommodate the scalability required. The discovery of nanoscale elements that function as 'memristors' has provided a key tool for the implementation of synaptic connections [2]. Leon Chua first developed the concept of the 'The memristor—the missing circuit element' in 1971 [3]. In this special issue he presents a tutorial describing how memristor research has fed into our understanding of synaptic behaviour and how they can be applied in information processing [4]. He also describes, 'The new principle of local activity, which uncovers a minuscule life-enabling "Goldilocks zone", dubbed the
Ca2+-permeable AMPA receptors in homeostatic synaptic plasticity
Directory of Open Access Journals (Sweden)
Hey-Kyoung eLee
2012-02-01
Full Text Available Neurons possess diverse mechanisms of homeostatic adaptation to overall changes in neural and synaptic activity, which are critical for proper brain functions. Homeostatic regulation of excitatory synapses has been studied in the context of synaptic scaling, which allows neurons to adjust their excitatory synaptic gain to maintain their activity within a dynamic range. Recent evidence suggests that one of the main mechanisms underlying synaptic scaling is by altering the function of postsynaptic AMPA receptors (AMPARs, including synaptic expression of Ca2+-permeable (CP- AMPARs. CP-AMPARs endow synapses with unique properties, which may benefit adaptation of neurons to periods of inactivity as would occur when a major input is lost. This review will summarize how synaptic expression of CP-AMPARs is regulated during homeostatic synaptic plasticity in the context of synaptic scaling, and will address the potential functional consequences of altering synaptic CP-AMPAR content.
Inverse Stochastic Resonance in Cerebellar Purkinje Cells.
Directory of Open Access Journals (Sweden)
Anatoly Buchin
2016-08-01
Full Text Available Purkinje neurons play an important role in cerebellar computation since their axons are the only projection from the cerebellar cortex to deeper cerebellar structures. They have complex internal dynamics, which allow them to fire spontaneously, display bistability, and also to be involved in network phenomena such as high frequency oscillations and travelling waves. Purkinje cells exhibit type II excitability, which can be revealed by a discontinuity in their f-I curves. We show that this excitability mechanism allows Purkinje cells to be efficiently inhibited by noise of a particular variance, a phenomenon known as inverse stochastic resonance (ISR. While ISR has been described in theoretical models of single neurons, here we provide the first experimental evidence for this effect. We find that an adaptive exponential integrate-and-fire model fitted to the basic Purkinje cell characteristics using a modified dynamic IV method displays ISR and bistability between the resting state and a repetitive activity limit cycle. ISR allows the Purkinje cell to operate in different functional regimes: the all-or-none toggle or the linear filter mode, depending on the variance of the synaptic input. We propose that synaptic noise allows Purkinje cells to quickly switch between these functional regimes. Using mutual information analysis, we demonstrate that ISR can lead to a locally optimal information transfer between the input and output spike train of the Purkinje cell. These results provide the first experimental evidence for ISR and suggest a functional role for ISR in cerebellar information processing.
Flexible Proton-Gated Oxide Synaptic Transistors on Si Membrane.
Zhu, Li Qiang; Wan, Chang Jin; Gao, Ping Qi; Liu, Yang Hui; Xiao, Hui; Ye, Ji Chun; Wan, Qing
2016-08-24
Ion-conducting materials have received considerable attention for their applications in fuel cells, electrochemical devices, and sensors. Here, flexible indium zinc oxide (InZnO) synaptic transistors with multiple presynaptic inputs gated by proton-conducting phosphorosilicate glass-based electrolyte films are fabricated on ultrathin Si membranes. Transient characteristics of the proton gated InZnO synaptic transistors are investigated, indicating stable proton-gating behaviors. Short-term synaptic plasticities are mimicked on the proposed proton-gated synaptic transistors. Furthermore, synaptic integration regulations are mimicked on the proposed synaptic transistor networks. Spiking logic modulations are realized based on the transition between superlinear and sublinear synaptic integration. The multigates coupled flexible proton-gated oxide synaptic transistors may be interesting for neuroinspired platforms with sophisticated spatiotemporal information processing.
Estimating short-term synaptic plasticity from pre- and postsynaptic spiking
Malyshev, Aleksey; Stevenson, Ian H.
2017-01-01
Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful
Energy Technology Data Exchange (ETDEWEB)
Pless, Jacquelyn; Arent, Douglas J.; Logan, Jeffrey; Cochran, Jaquelin; Zinaman, Owen
2016-10-01
One energy policy objective in the United States is to promote the adoption of technologies that provide consumers with stable, secure, and clean energy. Recent work provides anecdotal evidence of natural gas (NG) and renewable electricity (RE) synergies in the power sector, however few studies quantify the value of investing in NG and RE systems together as complements. This paper uses discounted cash flow analysis and real options analysis to value hybrid NG-RE systems in distributed applications, focusing on residential and commercial projects assumed to be located in the states of New York and Texas. Technology performance and operational risk profiles are modeled at the hourly level to capture variable RE output and NG prices are modeled stochastically as geometric Ornstein-Uhlenbeck (OU) stochastic processes to capture NG price uncertainty. The findings consistently suggest that NG-RE hybrid distributed systems are more favorable investments in the applications studied relative to their single-technology alternatives when incentives for renewables are available. In some cases, NG-only systems are the favorable investments. Understanding the value of investing in NG-RE hybrid systems provides insights into one avenue towards reducing greenhouse gas emissions, given the important role of NG and RE in the power sector.
Statistical mechanics of attractor neural network models with synaptic depression
International Nuclear Information System (INIS)
Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato
2009-01-01
Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.
International Nuclear Information System (INIS)
Pless, Jacquelyn; Arent, Douglas J.; Logan, Jeffrey; Cochran, Jaquelin; Zinaman, Owen
2016-01-01
One energy policy objective in the United States is to promote the adoption of technologies that provide consumers with stable, secure, and clean energy. Recent work provides anecdotal evidence of natural gas (NG) and renewable electricity (RE) synergies in the power sector, however few studies quantify the value of investing in NG and RE systems together as complements. This paper uses discounted cash flow analysis and real options analysis to value hybrid NG-RE systems in distributed applications, focusing on residential and commercial projects assumed to be located in the states of New York and Texas. Technology performance and operational risk profiles are modeled at the hourly level to capture variable RE output and NG prices are modeled stochastically as geometric Ornstein-Uhlenbeck (OU) stochastic processes to capture NG price uncertainty. The findings consistently suggest that NG-RE hybrid distributed systems are more favorable investments in the applications studied relative to their single-technology alternatives when incentives for renewables are available. In some cases, NG-only systems are the favorable investments. Understanding the value of investing in NG-RE hybrid systems provides insights into one avenue towards reducing greenhouse gas emissions, given the important role of NG and RE in the power sector. - Highlights: • Natural gas and renewable electricity can be viewed as complements. • We model hybrid natural gas and renewable electricity systems at the hourly level. • We incorporate variable renewable power output and uncertain natural gas prices. • Hybrid natural gas and renewable electricity systems can be valuable investments.
Influence of Synaptic Depression on Memory Storage Capacity
Otsubo, Yosuke; Nagata, Kenji; Oizumi, Masafumi; Okada, Masato
2011-08-01
Synaptic efficacy between neurons is known to change within a short time scale dynamically. Neurophysiological experiments show that high-frequency presynaptic inputs decrease synaptic efficacy between neurons. This phenomenon is called synaptic depression, a short term synaptic plasticity. Many researchers have investigated how the synaptic depression affects the memory storage capacity. However, the noise has not been taken into consideration in their analysis. By introducing ``temperature'', which controls the level of the noise, into an update rule of neurons, we investigate the effects of synaptic depression on the memory storage capacity in the presence of the noise. We analytically compute the storage capacity by using a statistical mechanics technique called Self Consistent Signal to Noise Analysis (SCSNA). We find that the synaptic depression decreases the storage capacity in the case of finite temperature in contrast to the case of the low temperature limit, where the storage capacity does not change.
International Nuclear Information System (INIS)
Özkan, Şeyda; Farquharson, Robert J.; Hill, Julian; Malcolm, Bill
2015-01-01
Highlights: • Two different pasture-based dairy feeding systems were evaluated. • The home-grown forage system outperformed the traditional pasture-based system. • Probability of achieving $200,000 income was reduced by imposition of a carbon tax. • Different farming systems will respond to change differently. • The ‘best choice’ for each individual farm is subjective. - Abstract: The imposition of a carbon tax in the economy will have indirect impacts on dairy farmers in Australia. Although there is a great deal of information available regarding mitigation strategies both in Australia and internationally, there seems to be a lack of research investigating the variable prices of carbon-based emissions on dairy farm operating profits in Australia. In this study, a stochastic analysis comparing the uncertainty in income in response to different prices on carbon-based emissions was conducted. The impact of variability in pasture consumption and variable prices of concentrates and hay on farm profitability was also investigated. The two different feeding systems examined were a ryegrass pasture-based system (RM) and a complementary forage-based system (CF). Imposing a carbon price ($20–$60) and not changing the systems reduced the farm operating profits by 28.4% and 25.6% in the RM and CF systems, respectively compared to a scenario where no carbon price was imposed. Different farming businesses will respond to variability in the rapidly changing operating environment such as fluctuations in pasture availability, price of purchased feeds and price of milk or carbon emissions differently. Further, in case there is a carbon price imposed for GHG emissions emanated from dairy farming systems, changing from pasture-based to more complex feeding systems incorporating home-grown double crops may reduce the reductions in farm operating profits. There is opportunity for future studies to focus on the impacts of different mitigation strategies and policy
Daskalou, Olympia; Karanastasi, Maria; Markonis, Yannis; Dimitriadis, Panayiotis; Koukouvinos, Antonis; Efstratiadis, Andreas; Koutsoyiannis, Demetris
2016-04-01
Following the legislative EU targets and taking advantage of its high renewable energy potential, Greece can obtain significant benefits from developing its water, solar and wind energy resources. In this context we present a GIS-based methodology for the optimal sizing and siting of solar and wind energy systems at the regional scale, which is tested in the Prefecture of Thessaly. First, we assess the wind and solar potential, taking into account the stochastic nature of the associated meteorological processes (i.e. wind speed and solar radiation, respectively), which is essential component for both planning (i.e., type selection and sizing of photovoltaic panels and wind turbines) and management purposes (i.e., real-time operation of the system). For the optimal siting, we assess the efficiency and economic performance of the energy system, also accounting for a number of constraints, associated with topographic limitations (e.g., terrain slope, proximity to road and electricity grid network, etc.), the environmental legislation and other land use constraints. Based on this analysis, we investigate favorable alternatives using technical, environmental as well as financial criteria. The final outcome is GIS maps that depict the available energy potential and the optimal layout for photovoltaic panels and wind turbines over the study area. We also consider a hypothetical scenario of future development of the study area, in which we assume the combined operation of the above renewables with major hydroelectric dams and pumped-storage facilities, thus providing a unique hybrid renewable system, extended at the regional scale.
Bi, Zedong; Zhou, Changsong
2016-01-01
In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to...
Stochastic Model Checking of the Stochastic Quality Calculus
DEFF Research Database (Denmark)
Nielson, Flemming; Nielson, Hanne Riis; Zeng, Kebin
2015-01-01
The Quality Calculus uses quality binders for input to express strategies for continuing the computation even when the desired input has not been received. The Stochastic Quality Calculus adds generally distributed delays for output actions and real-time constraints on the quality binders for input...
Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity
Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon
2011-01-01
Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and tempo...
Stochastic volatility and stochastic leverage
DEFF Research Database (Denmark)
Veraart, Almut; Veraart, Luitgard A. M.
This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic...... models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new...
The Role of Short Term Synaptic Plasticity in Temporal Coding of Neuronal Networks
Chandrasekaran, Lakshmi
2008-01-01
Short term synaptic plasticity is a phenomenon which is commonly found in the central nervous system. It could contribute to functions of signal processing namely, temporal integration and coincidence detection by modulating the input synaptic strength. This dissertation has two parts. First, we study the effects of short term synaptic plasticity…
Derivation of Stochastic Equations for Computational Uncertainties ...
African Journals Online (AJOL)
This paper presents a simple mathematical algorithm or procedure for computing the uncertainties at the various percent of data input, using the stochastic approach of simulating the input variables to compute the output variables. A simple algorithm was used to derive stochastic equations for some selected petrophysical ...
Tetzlaff, Christian; Kolodziejski, Christoph; Timme, Marc; Wörgötter, Florentin
2011-01-01
Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks. PMID:22203799
Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity
Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon
2011-01-01
Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800
Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations.
Wei, Yina; Krishnan, Giri P; Bazhenov, Maxim
2016-04-13
Sleep is critical for regulation of synaptic efficacy, memories, and learning. However, the underlying mechanisms of how sleep rhythms contribute to consolidating memories acquired during wakefulness remain unclear. Here we studied the role of slow oscillations, 0.2-1 Hz rhythmic transitions between Up and Down states during stage 3/4 sleep, on dynamics of synaptic connectivity in the thalamocortical network model implementing spike-timing-dependent synaptic plasticity. We found that the spatiotemporal pattern of Up-state propagation determines the changes of synaptic strengths between neurons. Furthermore, an external input, mimicking hippocampal ripples, delivered to the cortical network results in input-specific changes of synaptic weights, which persisted after stimulation was removed. These synaptic changes promoted replay of specific firing sequences of the cortical neurons. Our study proposes a neuronal mechanism on how an interaction between hippocampal input, such as mediated by sharp wave-ripple events, cortical slow oscillations, and synaptic plasticity, may lead to consolidation of memories through preferential replay of cortical cell spike sequences during slow-wave sleep. Sleep is critical for memory and learning. Replay during sleep of temporally ordered spike sequences related to a recent experience was proposed to be a neuronal substrate of memory consolidation. However, specific mechanisms of replay or how spike sequence replay leads to synaptic changes that underlie memory consolidation are still poorly understood. Here we used a detailed computational model of the thalamocortical system to report that interaction between slow cortical oscillations and synaptic plasticity during deep sleep can underlie mapping hippocampal memory traces to persistent cortical representation. This study provided, for the first time, a mechanistic explanation of how slow-wave sleep may promote consolidation of recent memory events. Copyright © 2016 the authors 0270-6474/16/364231-17$15.00/0.
Remodeling of inhibitory synaptic connections in developing ferret visual cortex
Directory of Open Access Journals (Sweden)
Dalva Matthew B
2010-02-01
Full Text Available Abstract Background In the visual cortex, as in many other regions of the developing brain, excitatory synaptic connections undergo substantial remodeling during development. While evidence suggests that local inhibitory synapses may behave similarly, the extent and mechanisms that mediate remodeling of inhibitory connections are not well understood. Results Using scanning laser photostimulation in slices of developing ferret visual cortex, we assessed the overall patterns of developing inhibitory and excitatory synaptic connections converging onto individual neurons. Inhibitory synaptic inputs onto pyramidal neurons in cortical layers 2 and 3 were already present as early as postnatal day 20, well before eye opening, and originated from regions close to the recorded neurons. During the ensuing 2 weeks, the numbers of synaptic inputs increased, with the numbers of inhibitory (and excitatory synaptic inputs peaking near the time of eye opening. The pattern of inhibitory inputs refined rapidly prior to the refinement of excitatory inputs. By uncaging the neurotransmtter GABA in brain slices from animals of different ages, we find that this rapid refinement correlated with a loss of excitatory activity by GABA. Conclusion Inhibitory synapses, like excitatory synapses, undergo significant postnatal remodeling. The time course of the remodeling of inhibitory connections correlates with the emergence of orientation tuning in the visual cortex, implicating these rearrangements in the genesis of adult cortical response properties.
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
Dynamics of stochastic systems
Klyatskin, Valery I
2005-01-01
Fluctuating parameters appear in a variety of physical systems and phenomena. They typically come either as random forces/sources, or advecting velocities, or media (material) parameters, like refraction index, conductivity, diffusivity, etc. The well known example of Brownian particle suspended in fluid and subjected to random molecular bombardment laid the foundation for modern stochastic calculus and statistical physics. Other important examples include turbulent transport and diffusion of particle-tracers (pollutants), or continuous densities (''''oil slicks''''), wave propagation and scattering in randomly inhomogeneous media, for instance light or sound propagating in the turbulent atmosphere.Such models naturally render to statistical description, where the input parameters and solutions are expressed by random processes and fields.The fundamental problem of stochastic dynamics is to identify the essential characteristics of system (its state and evolution), and relate those to the input parameters of ...
Multiplier Models in Stochastic DEA
Directory of Open Access Journals (Sweden)
Mahnaz Mirbolouki
2014-05-01
Full Text Available Data Envelopment Analysis (DEA is a data-oriented performance evaluation method which has treated data as being deterministic. Throughout applications managers may encounter the data which are not recognized deterministically. In this paper a deterministic version of stochastic CCR multiplier model based on chance constrained programming approach is presented. The advantage of this method is that the stochastic essence of input-output variables has been taken into account. Using numerical example, we will demonstrate how this method works.
Indian Academy of Sciences (India)
IAS Admin
V S Borkar is the Institute. Chair Professor of. Electrical Engineering at. IIT Bombay. His research interests are stochastic optimization, theory, algorithms and applica- tions. 1 'Markov Chain Monte Carlo' is another one (see [1]), not to mention schemes that combine both. Stochastic approximation is one of the unsung.
Heterosynaptic Plasticity Prevents Runaway Synaptic Dynamics
Chen, Jen-Yung; Lonjers, Peter; Lee, Christopher; Chistiakova, Marina; Volgushev, Maxim
2013-01-01
Spike timing-dependent plasticity (STDP) and other conventional Hebbian-type plasticity rules are prone to produce runaway dynamics of synaptic weights. Once potentiated, a synapse would have higher probability to lead to spikes and thus to be further potentiated, but once depressed, a synapse would tend to be further depressed. The runaway synaptic dynamics can be prevented by precisely balancing STDP rules for potentiation and depression; however, experimental evidence shows a great variety of potentiation and depression windows and magnitudes. Here we show that modifications of synapses to layer 2/3 pyramidal neurons from rat visual and auditory cortices in slices can be induced by intracellular tetanization: bursts of postsynaptic spikes without presynaptic stimulation. Induction of these heterosynaptic changes depended on the rise of intracellular calcium, and their direction and magnitude correlated with initial state of release mechanisms. We suggest that this type of plasticity serves as a mechanism that stabilizes the distribution of synaptic weights and prevents their runaway dynamics. To test this hypothesis, we develop a cortical neuron model implementing both homosynaptic (STDP) and heterosynaptic plasticity with properties matching the experimental data. We find that heterosynaptic plasticity effectively prevented runaway dynamics for the tested range of STDP and input parameters. Synaptic weights, although shifted from the original, remained normally distributed and nonsaturated. Our study presents a biophysically constrained model of how the interaction of different forms of plasticity—Hebbian and heterosynaptic—may prevent runaway synaptic dynamics and keep synaptic weights unsaturated and thus capable of further plastic changes and formation of new memories. PMID:24089497
Directory of Open Access Journals (Sweden)
Christoph Bauermeister
Full Text Available Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing, along with Gaussian narrow-band noise (a simple example of stochastic oscillations, and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.
Bauermeister, Christoph; Schwalger, Tilo; Russell, David F; Neiman, Alexander B; Lindner, Benjamin
2013-01-01
Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing), along with Gaussian narrow-band noise (a simple example of stochastic oscillations), and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs) and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.
The stochastic quality calculus
DEFF Research Database (Denmark)
Zeng, Kebin; Nielson, Flemming; Nielson, Hanne Riis
2014-01-01
We introduce the Stochastic Quality Calculus in order to model and reason about distributed processes that rely on each other in order to achieve their overall behaviour. The calculus supports broadcast communication in a truly concurrent setting. Generally distributed delays are associated...... with the outputs and at the same time the inputs impose constraints on the waiting times. Consequently, the expected inputs may not be available when needed and therefore the calculus allows to express the absence of data.The communication delays are expressed by general distributions and the resulting semantics...
Parzen, Emanuel
1962-01-01
Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine
McKean, Henry P
2005-01-01
This little book is a brilliant introduction to an important boundary field between the theory of probability and differential equations. -E. B. Dynkin, Mathematical Reviews This well-written book has been used for many years to learn about stochastic integrals. The book starts with the presentation of Brownian motion, then deals with stochastic integrals and differentials, including the famous Itô lemma. The rest of the book is devoted to various topics of stochastic integral equations, including those on smooth manifolds. Originally published in 1969, this classic book is ideal for supplemen
Distinct Subunit Domains Govern Synaptic Stability and Specificity of the Kainate Receptor
Directory of Open Access Journals (Sweden)
Christoph Straub
2016-07-01
Full Text Available Synaptic communication between neurons requires the precise localization of neurotransmitter receptors to the correct synapse type. Kainate-type glutamate receptors restrict synaptic localization that is determined by the afferent presynaptic connection. The mechanisms that govern this input-specific synaptic localization remain unclear. Here, we examine how subunit composition and specific subunit domains contribute to synaptic localization of kainate receptors. The cytoplasmic domain of the GluK2 low-affinity subunit stabilizes kainate receptors at synapses. In contrast, the extracellular domain of the GluK4/5 high-affinity subunit synergistically controls the synaptic specificity of kainate receptors through interaction with C1q-like proteins. Thus, the input-specific synaptic localization of the native kainate receptor complex involves two mechanisms that underlie specificity and stabilization of the receptor at synapses.
Synaptic metaplasticity underlies tetanic potentiation in Lymnaea: a novel paradigm.
Directory of Open Access Journals (Sweden)
Anita Mehta
Full Text Available We present a mathematical model that explains and interprets a novel form of short-term potentiation, which was found to be use-, but not time-dependent, in experiments done on Lymnaea neurons. The high degree of potentiation is explained using a model of synaptic metaplasticity, while the use-dependence (which is critically reliant on the presence of kinase in the experiment is explained using a model of a stochastic and bistable biological switch.
On the estimation of population-specific synaptic currents from laminar multielectrode recordings
Directory of Open Access Journals (Sweden)
Sergey L Gratiy
2011-12-01
Full Text Available Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex is an up-and-coming approach for investigating activity of cortical neuronal circuits. The low-frequency band of extracellular potential, i.e., the local field potential (LFP, is assumed to reflect the synaptic activity and can be used to extract the current source density (CSD profile. However, physiological interpretation of CSD profiles is uncertain because the analysis does not disambiguate synaptic inputs from passive return currents. Here we present a novel mathematical framework for identifying excited neuronal populations and for separating synaptic input currents from return currents based on LFP recordings. This involves a combination of the linear forward model, which predicts population-specific laminar LFP in response to sinusoidal synaptic inputs applied at different locations along the population cells having realistic morphologies and the linear inverse model, which reconstructs laminar profiles of synaptic inputs from the Fourier spectrum of the laminar LFP data based on the forward prediction. The model allows reconstruction of synaptic input profiles on a spatial scale comparable to known anatomical organization of synaptic projections within a cortical column. Assuming spatial correlation of synaptic inputs within individual populations, the model decomposes the columnar LFP into population-specific contributions. Constraining the solution with a priori knowledge of the spatial distribution of synaptic connectivity further allows prediction of active projections from the composite LFP profile. This modeling framework successfully delineates the main relationships between the synaptic input currents and the evoked LFP and can serve as a foundation for modeling more realistic processing of active dendritic conductances.
Stochastic dynamical models for ecological regime shifts
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Carstensen, Jacob; Madsen, Henrik
phytoplankton and benthic vegetation with feedback mechanisms is formulated, and it is demonstrated that bistability can occur for specific parameter settings. When stochastic input and stochastic propagation of the states are applied on the system regime shifts occur more frequently, and the threshold...
Bi, Zedong; Zhou, Changsong
2016-01-01
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-wis...
Directory of Open Access Journals (Sweden)
Zedong eBi
2016-02-01
Full Text Available In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis. Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e. synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons. Neurons (including the post-synaptic neuron in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1 synchronous firing and burstiness tend to increase DiffV, (2 heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3 heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our
Experience-dependent homeostatic synaptic plasticity in neocortex.
Whitt, Jessica L; Petrus, Emily; Lee, Hey-Kyoung
2014-03-01
The organism's ability to adapt to the changing sensory environment is due in part to the ability of the nervous system to change with experience. Input and synapse specific Hebbian plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), are critical for sculpting the nervous system to wire its circuit in tune with the environment and for storing memories. However, these synaptic plasticity mechanisms are innately unstable and require another mode of plasticity that maintains homeostasis to allow neurons to function within a desired dynamic range. Several modes of homeostatic adaptation are known, some of which work at the synaptic level. This review will focus on the known mechanisms of experience-induced homeostatic synaptic plasticity in the neocortex and their potential function in sensory cortex plasticity. This article is part of the Special Issue entitled 'Homeostatic Synaptic Plasticity'. Copyright © 2013 Elsevier Ltd. All rights reserved.
Voltage-dependent amplification of synaptic inputs in respiratory motoneurones
DEFF Research Database (Denmark)
Enríquez Denton, M; Wienecke, Jacob; Zhang, Mengliang
2012-01-01
time, the likely amplifying processes at work in respiratory motoneurones. In phrenic motoneurones, which control the most important respiratory muscle, the diaphragm, we found that the mechanism most favoured by investigations in other motoneurones, the activation of persistent inward currents via...
Input significance analysis: feature ranking through synaptic weights ...
African Journals Online (AJOL)
a selected dataset taken from the UCI Machine Learning Repository and in an online environment and lastly to attest the FR results by using another selected dataset taken from the same source and in the same environment. There are three groups of experiments conducted to accomplish these goals. The results are ...
Lukacs, Eugene; Lukacs, E
1975-01-01
Stochastic Convergence, Second Edition covers the theoretical aspects of random power series dealing with convergence problems. This edition contains eight chapters and starts with an introduction to the basic concepts of stochastic convergence. The succeeding chapters deal with infinite sequences of random variables and their convergences, as well as the consideration of certain sets of random variables as a space. These topics are followed by discussions of the infinite series of random variables, specifically the lemmas of Borel-Cantelli and the zero-one laws. Other chapters evaluate the po
Stochastic partial differential equations
Chow, Pao-Liu
2014-01-01
Preliminaries Introduction Some Examples Brownian Motions and Martingales Stochastic Integrals Stochastic Differential Equations of Itô Type Lévy Processes and Stochastic IntegralsStochastic Differential Equations of Lévy Type Comments Scalar Equations of First Order Introduction Generalized Itô's Formula Linear Stochastic Equations Quasilinear Equations General Remarks Stochastic Parabolic Equations Introduction Preliminaries Solution of Stochastic Heat EquationLinear Equations with Additive Noise Some Regularity Properties Stochastic Reaction-Diffusion Equations Parabolic Equations with Grad
Bayesian inference of synaptic quantal parameters from correlated vesicle release
Directory of Open Access Journals (Sweden)
Alexander D Bird
2016-11-01
Full Text Available Synaptic transmission is both history-dependent and stochastic, resulting in varying responses to presentations of the same presynaptic stimulus. This complicates attempts to infer synaptic parameters and has led to the proposal of a number of different strategies for their quantification. Recently Bayesian approaches have been applied to make more efficient use of the data collected in paired intracellular recordings. Methods have been developed that either provide a complete model of the distribution of amplitudes for isolated responses or approximate the amplitude distributions of a train of post-synaptic potentials, with correct short-term synaptic dynamics but neglecting correlations. In both cases the methods provided significantly improved inference of model parameters as compared to existing mean-variance fitting approaches. However, for synapses with high release probability, low vesicle number or relatively low restock rate and for data in which only one or few repeats of the same pattern are available, correlations between serial events can allow for the extraction of significantly more information from experiment: a more complete Bayesian approach would take this into account also. This has not been possible previously because of the technical difficulty in calculating the likelihood of amplitudes seen in correlated post-synaptic potential trains; however, recent theoretical advances have now rendered the likelihood calculation tractable for a broad class of synaptic dynamics models. Here we present a compact mathematical form for the likelihood in terms of a matrix product and demonstrate how marginals of the posterior provide information on covariance of parameter distributions. The associated computer code for Bayesian parameter inference for a variety of models of synaptic dynamics is provided in the supplementary material allowing for quantal and dynamical parameters to be readily inferred from experimental data sets.
Phasic Dopamine Modifies Sensory-Driven Output of Striatal Neurons through Synaptic Plasticity.
Wieland, Sebastian; Schindler, Sebastian; Huber, Cathrin; Köhr, Georg; Oswald, Manfred J; Kelsch, Wolfgang
2015-07-08
Animals are facing a complex sensory world in which only few stimuli are relevant to guide behavior. Value has to be assigned to relevant stimuli such as odors to select them over concurring information. Phasic dopamine is involved in the value assignment to stimuli in the ventral striatum. The underlying cellular mechanisms are incompletely understood. In striatal projection neurons of the ventral striatum in adult mice, we therefore examined the features and dynamics of phasic dopamine-induced synaptic plasticity and how this plasticity may modify the striatal output. Phasic dopamine is predicted to tag inputs that occur in temporal proximity. Indeed, we observed D1 receptor-dependent synaptic potentiation only when odor-like bursts and optogenetically evoked phasic dopamine release were paired within a time window of synaptic potentiation persisted after the phasic dopamine signal had ceased, but gradually reversed when odor-like bursts continued to be presented. The synaptic plasticity depended on the sensory input rate and was input specific. Importantly, synaptic plasticity amplified the firing response to a given olfactory input as the dendritic integration and the firing threshold remained unchanged during synaptic potentiation. Thus, phasic dopamine-induced synaptic plasticity can change information transfer through dynamic increases of the output of striatal projection neurons to specific sensory inputs. This plasticity may provide a neural substrate for dynamic value assignment in the striatum. Copyright © 2015 the authors 0270-6474/15/359946-11$15.00/0.
Eichhorn, Ralf; Aurell, Erik
2014-04-01
'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response
Crisan, Dan
2011-01-01
"Stochastic Analysis" aims to provide mathematical tools to describe and model high dimensional random systems. Such tools arise in the study of Stochastic Differential Equations and Stochastic Partial Differential Equations, Infinite Dimensional Stochastic Geometry, Random Media and Interacting Particle Systems, Super-processes, Stochastic Filtering, Mathematical Finance, etc. Stochastic Analysis has emerged as a core area of late 20th century Mathematics and is currently undergoing a rapid scientific development. The special volume "Stochastic Analysis 2010" provides a sa
Learning to discriminate through long-term changes of dynamical synaptic transmission.
Leibold, Christian; Bendels, Michael H K
2009-12-01
Short-term synaptic plasticity is modulated by long-term synaptic changes. There is, however, no general agreement on the computational role of this interaction. Here, we derive a learning rule for the release probability and the maximal synaptic conductance in a circuit model with combined recurrent and feedforward connections that allows learning to discriminate among natural inputs. Short-term synaptic plasticity thereby provides a nonlinear expansion of the input space of a linear classifier, whereas the random recurrent network serves to decorrelate the expanded input space. Computer simulations reveal that the twofold increase in the number of input dimensions through short-term synaptic plasticity improves the performance of a standard perceptron up to 100%. The distributions of release probabilities and maximal synaptic conductances at the capacity limit strongly depend on the balance between excitation and inhibition. The model also suggests a new computational interpretation of spikes evoked by stimuli outside the classical receptive field. These neuronal activities may reflect decorrelation of the expanded stimulus space by intracortical synaptic connections.
Borodin, Andrei N
2017-01-01
This book provides a rigorous yet accessible introduction to the theory of stochastic processes. A significant part of the book is devoted to the classic theory of stochastic processes. In turn, it also presents proofs of well-known results, sometimes together with new approaches. Moreover, the book explores topics not previously covered elsewhere, such as distributions of functionals of diffusions stopped at different random times, the Brownian local time, diffusions with jumps, and an invariance principle for random walks and local times. Supported by carefully selected material, the book showcases a wealth of examples that demonstrate how to solve concrete problems by applying theoretical results. It addresses a broad range of applications, focusing on concrete computational techniques rather than on abstract theory. The content presented here is largely self-contained, making it suitable for researchers and graduate students alike.
Synaptic Plasticity and Translation Initiation
Klann, Eric; Antion, Marcia D.; Banko, Jessica L.; Hou, Lingfei
2004-01-01
It is widely accepted that protein synthesis, including local protein synthesis at synapses, is required for several forms of synaptic plasticity. Local protein synthesis enables synapses to control synaptic strength independent of the cell body via rapid protein production from pre-existing mRNA. Therefore, regulation of translation initiation is…
Synaptic electronics: materials, devices and applications.
Kuzum, Duygu; Yu, Shimeng; Wong, H-S Philip
2013-09-27
In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.
Precise synaptic efficacy alignment suggests potentiation dominated learning
Directory of Open Access Journals (Sweden)
Christoph eHartmann
2016-01-01
Full Text Available Recent evidence suggests that parallel synapses from the same axonal branch onto the same dendritic branch have almost identical strength. It has been proposed that this alignment is only possible through learning rules that integrate activity over long time spans. However, learning mechanisms such as spike-timing-dependent plasticity (STDP are commonly assumed to be temporally local. Here, we propose that the combination of temporally local STDP and a multiplicative synaptic normalization mechanism is sufficient to explain the alignment of parallel synapses.To address this issue, we introduce three increasingly complex models: First, we model the idealized interaction of STDP and synaptic normalization in a single neuron as a simple stochastic process and derive analytically that the alignment effect can be described by a so-called Kesten process. From this we can derive that synaptic efficacy alignment requires potentiation-dominated learning regimes. We verify these conditions in a single-neuron model with independent spiking activities but more realistic synapses. As expected, we only observe synaptic efficacy alignment for long-term potentiation-biased STDP. Finally, we explore how well the findings transfer to recurrent neural networks where the learning mechanisms interact with the correlated activity of the network. We find that due to the self-reinforcing correlations in recurrent circuits under STDP, alignment occurs for both long-term potentiation- and depression-biased STDP, because the learning will be potentiation dominated in both cases due to the potentiating events induced by correlated activity. This is in line with recent results demonstrating a dominance of potentiation over depression during waking and normalization during sleep. This leads us to predict that individual spine pairs will be more similar in the morning than they are after sleep depriviation.In conclusion, we show that synaptic normalization in conjunction with
Directory of Open Access Journals (Sweden)
Sébastien Béhuret
Full Text Available The thalamus is the primary gateway that relays sensory information to the cerebral cortex. While a single recipient cortical cell receives the convergence of many principal relay cells of the thalamus, each thalamic cell in turn integrates a dense and distributed synaptic feedback from the cortex. During sensory processing, the influence of this functional loop remains largely ignored. Using dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. The synaptic bombardment of cortical origin was mimicked through the injection of a stochastic mixture of excitatory and inhibitory conductances, resulting in a gradable correlation level of afferent activity shared by thalamic cells. The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii the statistics of the corticothalamic synaptic bombardment and iii the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. This suggests that the transfer efficiency of relay cells could be selectively amplified when they become simultaneously desynchronized by the cortical feedback. When applied to the intact brain, this network regulation mechanism could direct an attentional focus to specific thalamic subassemblies and select the appropriate input lines to the cortex according to the descending
Tavazoie, Saeed
2013-01-01
Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework.
Characterization of emergent synaptic topologies in noisy neural networks
Miller, Aaron James
of a LIF neuron subjected to Gaussian white noise (GWN). The system reduces to the Ornstein-Uhlenbeck first passage time problem, the solution of which we build into the mapping method of Chapter 2. We demonstrate that simulations using the stochastic mapping have reduced computation time compared to traditional Runge-Kutta methods by more than a factor of 150. In Chapter 4, we use the stochastic mapping to study the dynamics of emerging synaptic topologies in noisy networks. With the addition of membrane noise, networks with dynamical synapses can admit states in which the distribution of the synaptic weights is static under spontaneous activity, but the random connectivity between neurons is dynamical. The widely cited problem of instabilities in networks with STDP is avoided with the implementation of a synaptic decay and an activation threshold on each synapse. When such networks are presented with stimulus modeled by a focused excitatory current, chain-like networks can emerge with the addition of an axon-remodeling plasticity rule, a topological constraint on the connectivity modeling the finite resources available to each neuron. The emergent topologies are the result of an iterative stochastic process. The dynamics of the growth process suggest a strong interplay between the network topology and the spike sequences they produce during development. Namely, the existence of an embedded spike sequence alters the distribution of synaptic weights through the entire network. The roles of model parameters that affect the interplay between network structure and activity are elucidated. Finally, we propose two mathematical growth models, which are complementary, that capture the essence of the growth dynamics observed in simulations. In Chapter 5, we present an extension of the stochastic mapping that allows the possibility of neuronal cooperation. We demonstrate that synaptic topologies admitting stereotypical sequences can emerge in yet higher, biologically
International Nuclear Information System (INIS)
Colombino, A.; Mosiello, R.; Norelli, F.; Jorio, V.M.; Pacilio, N.
1975-01-01
A nuclear system kinetics is formulated according to a stochastic approach. The detailed probability balance equations are written for the probability of finding the mixed population of neutrons and detected neutrons, i.e. detectrons, at a given level for a given instant of time. Equations are integrated in search of a probability profile: a series of cases is analyzed through a progressive criterium. It tends to take into account an increasing number of physical processes within the chosen model. The most important contribution is that solutions interpret analytically experimental conditions of equilibrium (moise analysis) and non equilibrium (pulsed neutron measurements, source drop technique, start up procedures)
Circadian Regulation of Synaptic Plasticity
Directory of Open Access Journals (Sweden)
Marcos G. Frank
2016-07-01
Full Text Available Circadian rhythms refer to oscillations in biological processes with a period of approximately 24 h. In addition to the sleep/wake cycle, there are circadian rhythms in metabolism, body temperature, hormone output, organ function and gene expression. There is also evidence of circadian rhythms in synaptic plasticity, in some cases driven by a master central clock and in other cases by peripheral clocks. In this article, I review the evidence for circadian influences on synaptic plasticity. I also discuss ways to disentangle the effects of brain state and rhythms on synaptic plasticity.
Stochastic cortical neurodynamics underlying the memory and cognitive changes in aging.
Rolls, Edmund T; Deco, Gustavo
2015-02-01
The relatively random spiking times of individual neurons provide a source of noise in the brain. We show how this noise interacting with altered depth in the basins of attraction of networks involved in short-term memory, attention, and episodic memory provide an approach to understanding some of the cognitive changes in normal aging. The effects of the neurobiological changes in aging that are considered include reduced synaptic modification and maintenance during learning produced in part through reduced acetylcholine in normal aging, reduced dopamine which reduces NMDA-receptor mediated effects, reduced noradrenaline which increases cAMP and thus shunts excitatory synaptic inputs, and the effects of a reduction in acetylcholine in increasing spike frequency adaptation. Using integrate-and-fire simulations of an attractor network implementing memory recall and short-term memory, it is shown that all these changes associated with aging reduce the firing rates of the excitatory neurons, which in turn reduce the depth of the basins of attraction, resulting in a much decreased probability in maintaining in short-term memory what has been recalled from the attractor network. This stochastic dynamics approach opens up new ways to understand and potentially treat the effects of normal aging on memory and cognitive functions. Copyright © 2014 Elsevier Inc. All rights reserved.
Stochastic models: theory and simulation.
Energy Technology Data Exchange (ETDEWEB)
Field, Richard V., Jr.
2008-03-01
Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.
Isolation of Synaptosomes, Synaptic Plasma Membranes, and Synaptic Junctional Complexes.
Michaelis, Mary L; Jiang, Lei; Michaelis, Elias K
2017-01-01
Isolation of synaptic nerve terminals or synaptosomes provides an opportunity to study the process of neurotransmission at many levels and with a variety of approaches. For example, structural features of the synaptic terminals and the organelles within them, such as synaptic vesicles and mitochondria, have been elucidated with electron microscopy. The postsynaptic membranes are joined to the presynaptic "active zone" of transmitter release through cell adhesion molecules and remain attached throughout the isolation of synaptosomes. These "post synaptic densities" or "PSDs" contain the receptors for the transmitters released from the nerve terminals and can easily be seen with electron microscopy. Biochemical and cell biological studies with synaptosomes have revealed which proteins and lipids are most actively involved in synaptic release of neurotransmitters. The functional properties of the nerve terminals, such as responses to depolarization and the uptake or release of signaling molecules, have also been characterized through the use of fluorescent dyes, tagged transmitters, and transporter substrates. In addition, isolated synaptosomes can serve as the starting material for the isolation of relatively pure synaptic plasma membranes (SPMs) that are devoid of organelles from the internal environment of the nerve terminal, such as mitochondria and synaptic vesicles. The isolated SPMs can reseal and form vesicular structures in which transport of ions such as sodium and calcium, as well as solutes such as neurotransmitters can be studied. The PSDs also remain associated with the presynaptic membranes during isolation of SPM fractions, making it possible to isolate the synaptic junctional complexes (SJCs) devoid of the rest of the plasma membranes of the nerve terminals and postsynaptic membrane components. Isolated SJCs can be used to identify the proteins that constitute this highly specialized region of neurons. In this chapter, we describe the steps involved
Endocannabinoid signaling and synaptic function
Castillo, Pablo E.; Younts, Thomas J.; Chávez, Andrés E.; Hashimotodani, Yuki
2012-01-01
Endocannabinoids are key modulators of synaptic function. By activating cannabinoid receptors expressed in the central nervous system, these lipid messengers can regulate several neural functions and behaviors. As experimental tools advance, the repertoire of known endocannabinoid-mediated effects at the synapse, and their underlying mechanism, continues to expand. Retrograde signaling is the principal mode by which endocannabinoids mediate short- and long-term forms of plasticity at both excitatory and inhibitory synapses. However, growing evidence suggests that endocannabinoids can also signal in a non-retrograde manner. In addition to mediating synaptic plasticity, the endocannabinoid system is itself subject to plastic changes. Multiple points of interaction with other neuromodulatory and signaling systems have now been identified. Synaptic endocannabinoid signaling is thus mechanistically more complex and diverse than originally thought. In this review, we focus on new advances in endocannabinoid signaling and highlight their role as potent regulators of synaptic function in the mammalian brain. PMID:23040807
Lanchier, Nicolas
2017-01-01
Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes. The first part of the text begins with a brief review of measure theory and revisits the main concepts of probability theory, from random variables to the standard limit theorems. The second part covers traditional material on stochastic processes, including martingales, discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding applications such as the ...
Stochastic resonance in models of neuronal ensembles
International Nuclear Information System (INIS)
Chialvo, D.R.; Longtin, A.; Mueller-Gerkin, J.
1997-01-01
Two recently suggested mechanisms for the neuronal encoding of sensory information involving the effect of stochastic resonance with aperiodic time-varying inputs are considered. It is shown, using theoretical arguments and numerical simulations, that the nonmonotonic behavior with increasing noise of the correlation measures used for the so-called aperiodic stochastic resonance (ASR) scenario does not rely on the cooperative effect typical of stochastic resonance in bistable and excitable systems. Rather, ASR with slowly varying signals is more properly interpreted as linearization by noise. Consequently, the broadening of the open-quotes resonance curveclose quotes in the multineuron stochastic resonance without tuning scenario can also be explained by this linearization. Computation of the input-output correlation as a function of both signal frequency and noise for the model system further reveals conditions where noise-induced firing with aperiodic inputs will benefit from stochastic resonance rather than linearization by noise. Thus, our study clarifies the tuning requirements for the optimal transduction of subthreshold aperiodic signals. It also shows that a single deterministic neuron can perform as well as a network when biased into a suprathreshold regime. Finally, we show that the inclusion of a refractory period in the spike-detection scheme produces a better correlation between instantaneous firing rate and input signal. copyright 1997 The American Physical Society
Brain-inspired Stochastic Models and Implementations
Al-Shedivat, Maruan
2015-05-12
One of the approaches to building artificial intelligence (AI) is to decipher the princi- ples of the brain function and to employ similar mechanisms for solving cognitive tasks, such as visual perception or natural language understanding, using machines. The recent breakthrough, named deep learning, demonstrated that large multi-layer networks of arti- ficial neural-like computing units attain remarkable performance on some of these tasks. Nevertheless, such artificial networks remain to be very loosely inspired by the brain, which rich structures and mechanisms may further suggest new algorithms or even new paradigms of computation. In this thesis, we explore brain-inspired probabilistic mechanisms, such as neural and synaptic stochasticity, in the context of generative models. The two questions we ask here are: (i) what kind of models can describe a neural learning system built of stochastic components? and (ii) how can we implement such systems e ̆ciently? To give specific answers, we consider two well known models and the corresponding neural architectures: the Naive Bayes model implemented with a winner-take-all spiking neural network and the Boltzmann machine implemented in a spiking or non-spiking fashion. We propose and analyze an e ̆cient neuromorphic implementation of the stochastic neu- ral firing mechanism and study the e ̄ects of synaptic unreliability on learning generative energy-based models implemented with neural networks.
Stochastic Averaging and Stochastic Extremum Seeking
Liu, Shu-Jun
2012-01-01
Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering and analysis of bacterial convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...
Stability Analysis on Sparsely Encoded Associative Memory with Short-Term Synaptic Dynamics
Xu, Muyuan; Katori, Yuichi; Aihara, Kazuyuki
This study investigates the stability of sparsely encoded associative memory in a network composed of stochastic neurons. The incorporation of short-term synaptic dynamics significantly changes the stability with respect to synaptic properties. Various states including static and oscillatory states are found in the network dynamics. Specifically, the sparseness of memory patterns raises the problem of spurious states. A mean field model is used to analyze the detailed structure in the stability and show that the performance of memory retrieval is recovered by appropriate feedback.
Synaptic Determinants of Rett Syndrome
Boggio, Elena M.; Lonetti, Giuseppina; Pizzorusso, Tommaso; Giustetto, Maurizio
2010-01-01
There is mounting evidence showing that the structural and molecular organization of synaptic connections is affected both in human patients and in animal models of neurological and psychiatric diseases. As a consequence of these experimental observations, it has been introduced the concept of synapsopathies, a notion describing brain disorders of synaptic function and plasticity. A close correlation between neurological diseases and synaptic abnormalities is especially relevant for those syndromes including also mental retardation in their symptomatology, such as Rett syndrome (RS). RS (MIM312750) is an X-linked dominant neurological disorder that is caused in the majority of cases by mutations in methyl-CpG-binding protein 2 (MeCP2). This review will focus on the current knowledge of the synaptic alterations produced by mutations of the gene MeCP2 in mouse models of RS and will highlight prospects experimental therapies currently in use. Different experimental approaches have revealed that RS could be the consequence of an impairment in the homeostasis of synaptic transmission in specific brain regions. Indeed, several forms of experience-induced neuronal plasticity are impaired in the absence of MeCP2. Based on the results presented in this review, it is reasonable to propose that understanding how the brain is affected by diseases such as RS is at reach. This effort will bring us closer to identify the neurobiological bases of human cognition. PMID:21423514
Synaptic determinants of Rett syndrome
Directory of Open Access Journals (Sweden)
Elena M B Boggio
2010-08-01
Full Text Available There is mounting evidence showing that the structural and molecular organization of synaptic connections are affected both in human patients and in animal models of neurological and psychiatric diseases. As a consequence of these experimental observations, it has been introduced the concept of synapsopathies, a notion describing brain disorders of synaptic function and plasticity. A close correlation between neurological diseases and synaptic abnormalities is especially relevant for those syndromes including also mental retardation in their symptomatology, such as Rett Syndrome (RS. RS (MIM312750 is an X-linked dominant neurological disorder that is caused, in the majority of cases by mutations in methyl-CpG-binding protein 2 (MeCP2. This review will focus on the current knowledge of the synaptic alterations produced by mutations of the gene MeCP2 in mouse models of RS and will highlight prospects experimental therapies currently in use. Different experimental approaches have revealed that RS could be the consequence of an impairment in the homeostasis of synaptic transmission in specific brain regions. Indeed, several forms of experience-induced neuronal plasticity are impaired in the absence of MeCP2. Based on the results presented in this review, it is reasonable to propose that understanding how the brain is affected by diseases such as RS is at reach. This effort will bring us closer to identify the neurobiological bases of human cognition.
Directory of Open Access Journals (Sweden)
Denise eBecker
2013-12-01
Full Text Available Neurons which lose part of their input respond with a compensatory increase in excitatory synaptic strength. This observation is of particular interest in the context of neurological diseases, which are accompanied by the loss of neurons and subsequent denervation of connected brain regions. However, while the cellular and molecular mechanisms of pharmacologically induced homeostatic synaptic plasticity have been identified to a certain degree, denervation-induced homeostatic synaptic plasticity remains not well understood. Here, we employed the entorhinal denervation in vitro model to study the role of tumor necrosis factor alpha (TNFα on changes in excitatory synaptic strength of mouse dentate granule cells following partial deafferentation. Our experiments disclose that TNFα is required for the maintenance of a compensatory increase in excitatory synaptic strength at 3/4 days postlesion (dpl, but not for the induction of synaptic scaling at 1 - 2 dpl. Furthermore, laser capture microdissection (LMD combined with quantitative PCR (qPCR demonstrates an increase in TNFα-mRNA levels in the denervated zone, which is consistent with our previous finding on a local, i.e., layer-specific increase in excitatory synaptic strength at 3 - 4 dpl. Immunostainings for the glial fibrillary acidic protein (GFAP and TNFα suggest that astrocytes are a source of TNFα in our experimental setting. We conclude that TNFα-signaling is a major regulatory system that aims at maintaining the homeostatic synaptic response of denervated neurons.
Stochastic resonance and chaotic resonance in bimodal maps: A ...
Indian Academy of Sciences (India)
We present the results of an extensive numerical study on the phenomenon of stochastic resonance in a bimodal cubic map. Both Gaussian random noise as well as deterministic chaos are used as input to drive the system between the basins. Our main result is that when two identical systems capable of stochastic ...
Fauth, Michael; Wörgötter, Florentin; Tetzlaff, Christian
2015-01-01
Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3–8) of synapses. In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have. We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems. Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities. Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes. These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms. PMID:25590330
International Nuclear Information System (INIS)
Wellens, Thomas; Shatokhin, Vyacheslav; Buchleitner, Andreas
2004-01-01
We are taught by conventional wisdom that the transmission and detection of signals is hindered by noise. However, during the last two decades, the paradigm of stochastic resonance (SR) proved this assertion wrong: indeed, addition of the appropriate amount of noise can boost a signal and hence facilitate its detection in a noisy environment. Due to its simplicity and robustness, SR has been implemented by mother nature on almost every scale, thus attracting interdisciplinary interest from physicists, geologists, engineers, biologists and medical doctors, who nowadays use it as an instrument for their specific purposes. At the present time, there exist a lot of diversified models of SR. Taking into account the progress achieved in both theoretical understanding and practical application of this phenomenon, we put the focus of the present review not on discussing in depth technical details of different models and approaches but rather on presenting a general and clear physical picture of SR on a pedagogical level. Particular emphasis will be given to the implementation of SR in generic quantum systems-an issue that has received limited attention in earlier review papers on the topic. The major part of our presentation relies on the two-state model of SR (or on simple variants thereof), which is general enough to exhibit the main features of SR and, in fact, covers many (if not most) of the examples of SR published so far. In order to highlight the diversity of the two-state model, we shall discuss several examples from such different fields as condensed matter, nonlinear and quantum optics and biophysics. Finally, we also discuss some situations that go beyond the generic SR scenario but are still characterized by a constructive role of noise
Wellens, Thomas; Shatokhin, Vyacheslav; Buchleitner, Andreas
2004-01-01
We are taught by conventional wisdom that the transmission and detection of signals is hindered by noise. However, during the last two decades, the paradigm of stochastic resonance (SR) proved this assertion wrong: indeed, addition of the appropriate amount of noise can boost a signal and hence facilitate its detection in a noisy environment. Due to its simplicity and robustness, SR has been implemented by mother nature on almost every scale, thus attracting interdisciplinary interest from physicists, geologists, engineers, biologists and medical doctors, who nowadays use it as an instrument for their specific purposes. At the present time, there exist a lot of diversified models of SR. Taking into account the progress achieved in both theoretical understanding and practical application of this phenomenon, we put the focus of the present review not on discussing in depth technical details of different models and approaches but rather on presenting a general and clear physical picture of SR on a pedagogical level. Particular emphasis will be given to the implementation of SR in generic quantum systems—an issue that has received limited attention in earlier review papers on the topic. The major part of our presentation relies on the two-state model of SR (or on simple variants thereof), which is general enough to exhibit the main features of SR and, in fact, covers many (if not most) of the examples of SR published so far. In order to highlight the diversity of the two-state model, we shall discuss several examples from such different fields as condensed matter, nonlinear and quantum optics and biophysics. Finally, we also discuss some situations that go beyond the generic SR scenario but are still characterized by a constructive role of noise.
Extending Stochastic Network Calculus to Loss Analysis
Directory of Open Access Journals (Sweden)
Chao Luo
2013-01-01
Full Text Available Loss is an important parameter of Quality of Service (QoS. Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.
How do astrocytes shape synaptic transmission? Insights from electrophysiology
Directory of Open Access Journals (Sweden)
Glenn eDallérac
2013-10-01
Full Text Available A major breakthrough in neuroscience has been the realization in the last decades that the dogmatic view of astroglial cells as being merely fostering and buffering elements of the nervous system is simplistic. A wealth of investigations now shows that astrocytes actually participate in the control of synaptic transmission in an active manner. This was first hinted by the intimate contacts glial processes make with neurons, particularly at the synaptic level, and evidenced using electrophysiological and calcium imaging techniques. Calcium imaging has provided critical evidence demonstrating that astrocytic regulation of synaptic efficacy is not a passive phenomenon. However, given that cellular activation is not only represented by calcium signaling, it is also crucial to assess concomitant mechanisms. We and others have used electrophysiological techniques to simultaneously record neuronal and astrocytic activity, thus enabling the study of multiple ionic currents and in depth investigation of neuro-glial dialogues. In the current review, we focus on the input such approach has provided in the understanding of astrocyte-neuron interactions underlying control of synaptic efficacy.
Intense synaptic activity enhances temporal resolution in spinal motoneurons.
Directory of Open Access Journals (Sweden)
Rune W Berg
Full Text Available In neurons, spike timing is determined by integration of synaptic potentials in delicate concert with intrinsic properties. Although the integration time is functionally crucial, it remains elusive during network activity. While mechanisms of rapid processing are well documented in sensory systems, agility in motor systems has received little attention. Here we analyze how intense synaptic activity affects integration time in spinal motoneurons during functional motor activity and report a 10-fold decrease. As a result, action potentials can only be predicted from the membrane potential within 10 ms of their occurrence and detected for less than 10 ms after their occurrence. Being shorter than the average inter-spike interval, the AHP has little effect on integration time and spike timing, which instead is entirely determined by fluctuations in membrane potential caused by the barrage of inhibitory and excitatory synaptic activity. By shortening the effective integration time, this intense synaptic input may serve to facilitate the generation of rapid changes in movements.
Directory of Open Access Journals (Sweden)
Nicolangelo L Iannella
2010-07-01
Full Text Available Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP. Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibres with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organisation in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite.Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a dendritic efficacy mosaic. Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite.
On the Adaptivity Gap of Stochastic Orienteering
N. Bansal (Nikhil); V. Nagarajan
2013-01-01
htmlabstractThe input to the stochastic orienteering problem consists of a budget B and metric (V,d) where each vertex v has a job with deterministic reward and random processing time (drawn from a known distribution). The processing times are independent across vertices. The goal is to obtain a
Active fault diagnosis based on stochastic tests
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2008-01-01
The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...
Optogenetic analysis of synaptic function
Liewald, Jana F.; Brauner, Martin; Stephens, Greg J.; Bouhours, Magali; Schultheis, Christian; Zhen, Mei; Gottschalk, Alexander
2008-01-01
We introduce optogenetic investigation of neurotransmission (OptIoN) for time-resolved and quantitative assessment of synaptic function via behavioral and electrophysiological analyses. We photo-triggered release of acetylcholine or γ-aminobutyric acid at Caenorhabditis elegans neuromuscular
Synaptic AMPA receptor plasticity and behavior
Kessels, Helmut W.; Malinow, Roberto
2009-01-01
The ability to change behavior likely depends on the selective strengthening and weakening of brain synapses. The cellular models of synaptic plasticity, long-term potentiation (LTP) and depression (LTD) of synaptic strength, can be expressed by the synaptic insertion or removal of AMPA receptors
Spike Pattern Structure Influences Efficacy Variability under STDP and Synaptic Homeostasis
Bi, Zedong; Zhou, Changsong; Zhou, Hai-Jun
2015-01-01
In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons, synapses and networks, spike trains typically exhibit externally uncontrollable variability such as spatial heterogeneity and temporal stochasticity, resulting in variability of synapses, which we call efficacy variability. Spike patterns with the same population rate but inducing different efficacy variability may result in neuronal networks with sharply different structures and f...
Bi, Zedong; Zhou, Changsong
2016-01-01
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). PMID:27555816
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.
DEFF Research Database (Denmark)
Carlsen, Eva Maria Meier; Perrier, Jean-Francois Marie
2014-01-01
. Neurons responded to electrical stimulation by monosynaptic EPSCs (excitatory monosynaptic postsynaptic currents). We used mice expressing the enhanced green fluorescent protein under the promoter of the glial fibrillary acidic protein to identify astrocytes. Chelating calcium with BAPTA in a single...... by different neuromodulators. These substances are usually thought of being released by dedicated neurons. However, in other networks from the central nervous system synaptic transmission is also modulated by transmitters released from astrocytes. The star-shaped glial cell responds to neurotransmitters...... by releasing gliotransmitters, which in turn modulate synaptic transmission. Here we investigated if astrocytes present in the ventral horn of the spinal cord modulate synaptic transmission. We evoked synaptic inputs in ventral horn neurons recorded in a slice preparation from the spinal cord of neonatal mice...
Stochastic symmetries of Wick type stochastic ordinary differential equations
Ünal, Gazanfer
2015-04-01
We consider Wick type stochastic ordinary differential equations with Gaussian white noise. We define the stochastic symmetry transformations and Lie equations in Kondratiev space (S)-1N. We derive the determining system of Wick type stochastic partial differential equations with Gaussian white noise. Stochastic symmetries for stochastic Bernoulli, Riccati and general stochastic linear equation in (S)-1N are obtained. A stochastic version of canonical variables is also introduced.
Functional dissection of synaptic circuits: in vivo patch-clamp recording in neuroscience.
Tao, Can; Zhang, Guangwei; Xiong, Ying; Zhou, Yi
2015-01-01
Neuronal activity is dominated by synaptic inputs from excitatory or inhibitory neural circuits. With the development of in vivo patch-clamp recording, especially in vivo voltage-clamp recording, researchers can not only directly measure neuronal activity, such as spiking responses or membrane potential dynamics, but also quantify synaptic inputs from excitatory and inhibitory circuits in living animals. This approach enables researchers to directly unravel different synaptic components and to understand their underlying roles in particular brain functions. Combining in vivo patch-clamp recording with other techniques, such as two-photon imaging or optogenetics, can provide even clearer functional dissection of the synaptic contributions of different neurons or nuclei. Here, we summarized current applications and recent research progress using the in vivo patch-clamp recording method and focused on its role in the functional dissection of different synaptic inputs. The key factors of a successful in vivo patch-clamp experiment and possible solutions based on references and our experiences were also discussed.
Ogawa, Shigeyoshi
2017-01-01
This book presents an elementary introduction to the theory of noncausal stochastic calculus that arises as a natural alternative to the standard theory of stochastic calculus founded in 1944 by Professor Kiyoshi Itô. As is generally known, Itô Calculus is essentially based on the "hypothesis of causality", asking random functions to be adapted to a natural filtration generated by Brownian motion or more generally by square integrable martingale. The intention in this book is to establish a stochastic calculus that is free from this "hypothesis of causality". To be more precise, a noncausal theory of stochastic calculus is developed in this book, based on the noncausal integral introduced by the author in 1979. After studying basic properties of the noncausal stochastic integral, various concrete problems of noncausal nature are considered, mostly concerning stochastic functional equations such as SDE, SIE, SPDE, and others, to show not only the necessity of such theory of noncausal stochastic calculus but ...
Molecular Recognition within Synaptic Scaffolds
DEFF Research Database (Denmark)
Erlendsson, Simon
-length structural model of the PICK1 dimer in-solution. We found the PICK1 BAR dimer to resemble an elongated crescent-shaped structure, spanning ~160 Å, with the PICK1 PDZ domains loosely attached to the BAR domain. This finding is in contrast to previous findings for other BAR domain proteins, where adjacent......Scaffolding proteins are abundant participants and regulators of the extensive intracellular framework required for maintaining cellular functions such as cellular adhesion and signal transduction cascades. In excitatory neuronal synapses these scaffolding proteins often contain one or more PDZ...... domains, responsible for tethering their respective synaptic protein ligands. Therefore, understanding the specificity and binding mechanisms of PDZ domain proteins is essential to understand regulation of synaptic plasticity. PICK1 is a PDZ domain-containing scaffolding protein predominantly expressed...
Stochastic Systems Uncertainty Quantification and Propagation
Grigoriu, Mircea
2012-01-01
Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: · A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis · Probabilistic models for random variables an...
High-speed Stochastic Fatigue Testing
DEFF Research Database (Denmark)
Brincker, Rune; Sørensen, John Dalsgaard
1990-01-01
Good stochastic fatigue tests are difficult to perform. One of the major reasons is that ordinary servohydraulic loading systems realize the prescribed load history accurately at very low testing speeds only. If the speeds used for constant amplitude testing are applied to stochastic fatigue...... the analog control device remain as the basic control mechanism in the system, but distorting the input signal by computer in order to minimize the errors of the load history extremes. The principle proves to be very efficient to reduce all kinds of system errors and has shown to be able to increase...
Directory of Open Access Journals (Sweden)
Ingie Hong
Full Text Available It is generally believed that after memory consolidation, memory-encoding synaptic circuits are persistently modified and become less plastic. This, however, may hinder the remaining capacity of information storage in a given neural circuit. Here we consider the hypothesis that memory-encoding synaptic circuits still retain reversible plasticity even after memory consolidation. To test this, we employed a protocol of auditory fear conditioning which recruited the vast majority of the thalamic input synaptic circuit to the lateral amygdala (T-LA synaptic circuit; a storage site for fear memory with fear conditioning-induced synaptic plasticity. Subsequently the fear memory-encoding synaptic circuits were challenged with fear extinction and re-conditioning to determine whether these circuits exhibit reversible plasticity. We found that fear memory-encoding T-LA synaptic circuit exhibited dynamic efficacy changes in tight correlation with fear memory strength even after fear memory consolidation. Initial conditioning or re-conditioning brought T-LA synaptic circuit near the ceiling of their modification range (occluding LTP and enhancing depotentiation in brain slices prepared from conditioned or re-conditioned rats, while extinction reversed this change (reinstating LTP and occluding depotentiation in brain slices prepared from extinguished rats. Consistently, fear conditioning-induced synaptic potentiation at T-LA synapses was functionally reversed by extinction and reinstated by subsequent re-conditioning. These results suggest reversible plasticity of fear memory-encoding circuits even after fear memory consolidation. This reversible plasticity of memory-encoding synapses may be involved in updating the contents of original memory even after memory consolidation.
The stochastic properties of input spike trains control neuronal arithmetic
Czech Academy of Sciences Publication Activity Database
Bureš, Zbyněk
2012-01-01
Roč. 106, č. 2 (2012), s. 111-122 ISSN 0340-1200 R&D Projects: GA ČR(CZ) GAP303/12/1347; GA ČR(CZ) GAP304/12/1342; GA ČR(CZ) GBP304/12/G069 Grant - others:GA MŠk(CZ) M00176 Institutional research plan: CEZ:AV0Z50390512 Institutional support: RVO:68378041 Keywords : aerosol * simulation of human breathing * porcine lung equivalent Subject RIV: ED - Physiology Impact factor: 2.067, year: 2012
Short-term memories with a stochastic perturbation
Energy Technology Data Exchange (ETDEWEB)
Pontes, Jose C.A. de [Departamento de Fisica, Universidade Federal do Parana, 81531-990 Curitiba, PR (Brazil); Batista, Antonio M. [Departamento de Matematica e Estatistica, Universidade Estadual de Ponta Grossa, 84030-900 Ponta Grossa, PR (Brazil); Viana, Ricardo L. [Departamento de Fisica, Universidade Federal do Parana, 81531-990 Curitiba, PR (Brazil); Lopes, Sergio R. [Departamento de Fisica, Universidade Federal do Parana, 81531-990 Curitiba, PR (Brazil)
2005-03-01
We investigate short-term memories in linear and weakly nonlinear coupled map lattices with a periodic external input. We use locally coupled maps to present numerical results about short-term memory formation adding a stochastic perturbation in the maps and in the external input.
Short-term memories with a stochastic perturbation
International Nuclear Information System (INIS)
Pontes, Jose C.A. de; Batista, Antonio M.; Viana, Ricardo L.; Lopes, Sergio R.
2005-01-01
We investigate short-term memories in linear and weakly nonlinear coupled map lattices with a periodic external input. We use locally coupled maps to present numerical results about short-term memory formation adding a stochastic perturbation in the maps and in the external input
Directory of Open Access Journals (Sweden)
Akira Takashima
Full Text Available The synaptic integration in individual central neuron is critically affected by how active conductances are distributed over dendrites. It has been well known that the dendrites of central neurons are richly endowed with voltage- and ligand-regulated ion conductances. Nonspiking interneurons (NSIs, almost exclusively characteristic to arthropod central nervous systems, do not generate action potentials and hence lack voltage-regulated sodium channels, yet having a variety of voltage-regulated potassium conductances on their dendritic membrane including the one similar to the delayed-rectifier type potassium conductance. It remains unknown, however, how the active conductances are distributed over dendrites and how the synaptic integration is affected by those conductances in NSIs and other invertebrate neurons where the cell body is not included in the signal pathway from input synapses to output sites. In the present study, we quantitatively investigated the functional significance of active conductance distribution pattern in the spatio-temporal spread of synaptic potentials over dendrites of an identified NSI in the crayfish central nervous system by computer simulation. We systematically changed the distribution pattern of active conductances in the neuron's multicompartment model and examined how the synaptic potential waveform was affected by each distribution pattern. It was revealed that specific patterns of nonuniform distribution of potassium conductances were consistent, while other patterns were not, with the waveform of compound synaptic potentials recorded physiologically in the major input-output pathway of the cell, suggesting that the possibility of nonuniform distribution of potassium conductances over the dendrite cannot be excluded as well as the possibility of uniform distribution. Local synaptic circuits involving input and output synapses on the same branch or on the same side were found to be potentially affected under
Stochastic optimal control of single neuron spike trains
DEFF Research Database (Denmark)
Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë
2014-01-01
stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...... into account physiological constraints on the control. A precise and robust targeting of neural activity based on stochastic optimal control has great potential for regulating neural activity in e.g. prosthetic applications and to improve our understanding of the basic mechanisms by which neuronal firing...
SRC Inhibition Reduces NR2B Surface Expression and Synaptic Plasticity in the Amygdala
Sinai, Laleh; Duffy, Steven; Roder, John C.
2010-01-01
The Src protein tyrosine kinase plays a central role in the regulation of N-methyl-d-aspartate receptor (NMDAR) activity by regulating NMDAR subunit 2B (NR2B) surface expression. In the amygdala, NMDA-dependent synaptic plasticity resulting from convergent somatosensory and auditory inputs contributes to emotional memory; however, the role of Src…
Models of the stochastic activity of neurones
Holden, Arun Vivian
1976-01-01
These notes have grown from a series of seminars given at Leeds between 1972 and 1975. They represent an attempt to gather together the different kinds of model which have been proposed to account for the stochastic activity of neurones, and to provide an introduction to this area of mathematical biology. A striking feature of the electrical activity of the nervous system is that it appears stochastic: this is apparent at all levels of recording, ranging from intracellular recordings to the electroencephalogram. The chapters start with fluctuations in membrane potential, proceed through single unit and synaptic activity and end with the behaviour of large aggregates of neurones: L have chgaen this seque~~e\\/~~';uggest that the interesting behaviourr~f :the nervous system - its individuality, variability and dynamic forms - may in part result from the stochastic behaviour of its components. I would like to thank Dr. Julio Rubio for reading and commenting on the drafts, Mrs. Doris Beighton for producing the fin...
Stochastic quantization and supersymmetry
International Nuclear Information System (INIS)
Kirschner, R.
1984-04-01
In the last years interest in stochastic quantization has increased. The method of quantization by stochastic relaxation processes has been proposed by Parisi and Wu, inspired by the extensive application of Monte Carlo simulations to quantum systems. Starting with the classical equations of motion of the system (field theory) and adding random force terms - the random force obeys a Gaussian distribution (white noise) - stochastic differential equations are obtained, in this context called Langevin equations, which are a central object in the theory of stochastic processes. (author)
Network response synchronization enhanced by synaptic plasticity
Lobov, S.; Simonov, A.; Kastalskiy, I.; Kazantsev, V.
2016-02-01
Synchronization of neural network response on spatially localized periodic stimulation was studied. The network consisted of synaptically coupled spiking neurons with spike-timing-dependent synaptic plasticity (STDP). Network connectivity was defined by time evolving matrix of synaptic weights. We found that the steady-state spatial pattern of the weights could be rearranged due to locally applied external periodic stimulation. A method for visualization of synaptic weights as vector field was introduced to monitor the evolving connectivity matrix. We demonstrated that changes in the vector field and associated weight rearrangements underlay an enhancement of synchronization range.
Binocular Rivalry in a Competitive Neural Network with Synaptic Depression
Kilpatrick, Zachary P.
2010-01-01
We study binocular rivalry in a competitive neural network with synaptic depression. In particular, we consider two coupled hypercolums within primary visual cortex (V1), representing orientation selective cells responding to either left or right eye inputs. Coupling between hypercolumns is dominated by inhibition, especially for neurons with dissimilar orientation preferences. Within hypercolumns, recurrent connectivity is excitatory for similar orientations and inhibitory for different orientations. All synaptic connections are modifiable by local synaptic depression. When the hypercolumns are driven by orthogonal oriented stimuli, it is possible to induce oscillations that are representative of binocular rivalry. We first analyze the occurrence of oscillations in a space-clamped version of the model using a fast-slow analys is, taking advantage of the fact that depression evolves much slower than population activity. We th en analyze the onset of oscillations in the full spatially extended system by carrying out a piecewise smooth stability analysis of single (winner-take-all) and double (fusion) bumps within the network. Although our stability analysis takes into account only instabilities associated with real eigenvalues, it identifies points of instability that are consistent with what is found numerically. In particular, we show that, in regions of parameter space where double bumps are unstable and no single bumps exist, binocular rivalry can arise as a slow alternation between either population supporting a bump. © 2010 Society for Industrial and Applied Mathematics.
The computational power of astrocyte mediated synaptic plasticity
Directory of Open Access Journals (Sweden)
Rogier eMin
2012-11-01
Full Text Available Research in the last two decades has made clear that astrocytes play a crucial role in the brain beyond their functions in energy metabolism and homeostasis. Many studies have shown that astrocytes can dynamically modulate neuronal excitability and synaptic plasticity, and might participate in higher brain functions like learning and memory. With the plethora of astrocyte-mediated signaling processes described in the literature today, the current challenge is to identify which of these processes happen under what physiological condition, and how this shapes information processing and, ultimately, behavior. To answer these questions will require a combination of advanced physiological, genetical and behavioral experiments. Additionally, mathematical modeling will prove crucial for testing predictions on the possible functions of astrocytes in neuronal networks, and to generate novel ideas as to how astrocytes can contribute to the complexity of the brain. Here, we aim to provide an outline of how astrocytes can interact with neurons. We do this by reviewing recent experimental literature on astrocyte-neuron interactions, discussing the dynamic effects of astrocytes on neuronal excitability and short- and long-term synaptic plasticity. Finally, we will outline the potential computational functions that astrocyte-neuron interactions can serve in the brain. We will discuss how astrocytes could govern metaplasticity in the brain, how they might organize the clustering of synaptic inputs, and how they could function as memory elements for neuronal activity. We conclude that astrocytes can enhance the computational power of neuronal networks in previously unexpected ways.
Robust Short-Term Memory without Synaptic Learning
Johnson, Samuel; Marro, J.; Torres, Joaquín J.
2013-01-01
Short-term memory in the brain cannot in general be explained the way long-term memory can – as a gradual modification of synaptic weights – since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings. PMID:23349664
Synaptic Plasticity and Memory Formation
1993-06-30
suspected of being the substrate of several forms of memory encoded by synapses in the forebrain of humans and other mammals. Work in the past year...of LTP will enhance the encoding of memory . Aniracetam , as noted, prolongs the open time of the AMPA receptor and in this way facilitates excitatory...121 t Iffw,,a" S. FUNO4NG mUMSERS Synaptic Plasticity and Memory Formation F 49620-92-0307 C (ci) b.q F Gary Lynch 7. Pf(RfO*INN ORGAMIZAMNIO NMMW(S
Liyanagedera, Chamika M.; Sengupta, Abhronil; Jaiswal, Akhilesh; Roy, Kaushik
2017-12-01
Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.
Operant conditioning of synaptic and spiking activity patterns in single hippocampal neurons.
Ishikawa, Daisuke; Matsumoto, Nobuyoshi; Sakaguchi, Tetsuya; Matsuki, Norio; Ikegaya, Yuji
2014-04-02
Learning is a process of plastic adaptation through which a neural circuit generates a more preferable outcome; however, at a microscopic level, little is known about how synaptic activity is patterned into a desired configuration. Here, we report that animals can generate a specific form of synaptic activity in a given neuron in the hippocampus. In awake, head-restricted mice, we applied electrical stimulation to the lateral hypothalamus, a reward-associated brain region, when whole-cell patch-clamped CA1 neurons exhibited spontaneous synaptic activity that met preset criteria. Within 15 min, the mice learned to generate frequently the excitatory synaptic input pattern that satisfied the criteria. This reinforcement learning of synaptic activity was not observed for inhibitory input patterns. When a burst unit activity pattern was conditioned in paired and nonpaired paradigms, the frequency of burst-spiking events increased and decreased, respectively. The burst reinforcement occurred in the conditioned neuron but not in other adjacent neurons; however, ripple field oscillations were concomitantly reinforced. Neural conditioning depended on activation of NMDA receptors and dopamine D1 receptors. Acutely stressed mice and depression model mice that were subjected to forced swimming failed to exhibit the neural conditioning. This learning deficit was rescued by repetitive treatment with fluoxetine, an antidepressant. Therefore, internally motivated animals are capable of routing an ongoing action potential series into a specific neural pathway of the hippocampal network.
Torsin A Localization in the Mouse Cerebellar Synaptic Circuitry.
Directory of Open Access Journals (Sweden)
Francesca Puglisi
Full Text Available Torsin A (TA is a ubiquitous protein belonging to the superfamily of proteins called "ATPases associated with a variety of cellular activities" (AAA(+ ATPase. To date, a great deal of attention has been focused on neuronal TA since its mutant form causes early-onset (DYT1 torsion dystonia, an inherited movement disorder characterized by sustained muscle contractions and abnormal postures. Interestingly, it has been proposed that TA, by interacting with the cytoskeletal network, may contribute to the control of neurite outgrowth and/or by acting as a chaperone at synapses could affect synaptic vesicle turnover and neurotransmitter release. Accordingly, both its peculiar developmental expression in striatum and cerebellum and evidence from DYT1 knock-in mice suggest that TA may influence dendritic arborization and synaptogenesis in the brain. Therefore, to better understand TA function a detailed description of its localization at synaptic level is required. Here, we characterized by means of rigorous quantitative confocal analysis TA distribution in the mouse cerebellum at postnatal day 14 (P14, when both cerebellar synaptogenesis and TA expression peak. We observed that the protein is broadly distributed both in cerebellar cortex and in the deep cerebellar nuclei (DCN. Of note, Purkinje cells (PC express high levels of TA also in the spines and axonal terminals. In addition, abundant expression of the protein was found in the main GABA-ergic and glutamatergic inputs of the cerebellar cortex. Finally, TA was observed also in glial cells, a cellular population little explored so far. These results extend our knowledge on TA synaptic localization providing a clue to its potential role in synaptic development.
Stochastic Reservoir Characterization Constrained by Seismic Data
Energy Technology Data Exchange (ETDEWEB)
Eide, Alfhild Lien
1999-07-01
In order to predict future production of oil and gas from a petroleum reservoir, it is important to have a good description of the reservoir in terms of geometry and physical parameters. This description is used as input to large numerical models for the fluid flow in the reservoir. With increased quality of seismic data, it is becoming possible to extend their use from the study of large geologic structures such as seismic horizons to characterization of the properties of the reservoir between the horizons. Uncertainties because of the low resolution of seismic data can be successfully handled by means of stochastic modeling, and spatial statistics can provide tools for interpolation and simulation of reservoir properties not completely resolved by seismic data. This thesis deals with stochastic reservoir modeling conditioned to seismic data and well data. Part I presents a new model for stochastic reservoir characterization conditioned to seismic traces. Part II deals with stochastic simulation of high resolution impedance conditioned to measured impedance. Part III develops a new stochastic model for calcite cemented objects in a sandstone background; it is a superposition of a marked point model for the calcites and a continuous model for the background.
Short-Term Synaptic Plasticity at Interneuronal Synapses Could Sculpt Rhythmic Motor Patterns.
Jia, Yan; Parker, David
2016-01-01
The output of a neuronal network depends on the organization and functional properties of its component cells and synapses. While the characterization of synaptic properties has lagged cellular analyses, a potentially important aspect in rhythmically active networks is how network synapses affect, and are in turn affected by, network activity. This could lead to a potential circular interaction where short-term activity-dependent synaptic plasticity is both influenced by and influences the network output. The analysis of synaptic plasticity in the lamprey locomotor network was extended here to characterize the short-term plasticity of connections between network interneurons and to try and address its potential network role. Paired recordings from identified interneurons in quiescent networks showed synapse-specific synaptic properties and plasticity that supported the presence of two hemisegmental groups that could influence bursting: depression in an excitatory interneuron group, and facilitation in an inhibitory feedback circuit. The influence of activity-dependent synaptic plasticity on network activity was investigated experimentally by changing Ringer Ca(2+) levels, and in a simple computer model. A potential caveat of the experimental analyses was that changes in Ringer Ca(2+) (and compensatory adjustments in Mg(2+) in some cases) could alter several other cellular and synaptic properties. Several of these properties were tested, and while there was some variability, these were not usually significantly affected by the Ringer changes. The experimental analyses suggested that depression of excitatory inputs had the strongest influence on the patterning of network activity. The simulation supported a role for this effect, and also suggested that the inhibitory facilitating group could modulate the influence of the excitatory synaptic depression. Short-term activity-dependent synaptic plasticity has not generally been considered in spinal cord models. These
Gottwald, G.A.; Crommelin, D.T.; Franzke, C.L.E.; Franzke, C.L.E.; O'Kane, T.J.
2017-01-01
In this chapter we review stochastic modelling methods in climate science. First we provide a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism. The Mori-Zwanzig equations contain a Markov term, a memory term and a term suggestive of
CSIR Research Space (South Africa)
Roux, FS
2013-09-01
Full Text Available Roux Presented at the International Conference on Correlation Optics 2013 Chernivtsi, Ukraine 18-20 September 2013 CSIR National Laser Centre, Pretoria, South Africa – p. 1/24 Contents ⊲ Defining Stochastic Singular Optics (SSO) ⊲ Tools of Stochastic...
Stochastic analytic regularization
International Nuclear Information System (INIS)
Alfaro, J.
1984-07-01
Stochastic regularization is reexamined, pointing out a restriction on its use due to a new type of divergence which is not present in the unregulated theory. Furthermore, we introduce a new form of stochastic regularization which permits the use of a minimal subtraction scheme to define the renormalized Green functions. (author)
A Stochastic Employment Problem
Wu, Teng
2013-01-01
The Stochastic Employment Problem(SEP) is a variation of the Stochastic Assignment Problem which analyzes the scenario that one assigns balls into boxes. Balls arrive sequentially with each one having a binary vector X = (X[subscript 1], X[subscript 2],...,X[subscript n]) attached, with the interpretation being that if X[subscript i] = 1 the ball…
Stochastic Non-Parametric Frontier Analysis
Mohammad Rahmani; Gholamreza Jahanshahloo
2014-01-01
In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production eciency. Specically, our approach rst allows for statistical noise, similar to Stochastic frontier analysis , and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelih...
Greenwood, Priscilla E
2016-01-01
This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...
Stochastic quantization and gravity
International Nuclear Information System (INIS)
Rumpf, H.
1984-01-01
We give a preliminary account of the application of stochastic quantization to the gravitational field. We start in Section I from Nelson's formulation of quantum mechanics as Newtonian stochastic mechanics and only then introduce the Parisi-Wu stochastic quantization scheme on which all the later discussion will be based. In Section II we present a generalization of the scheme that is applicable to fields in physical (i.e. Lorentzian) space-time and treat the free linearized gravitational field in this manner. The most remarkable result of this is the noncausal propagation of conformal gravitons. Moreover the concept of stochastic gauge-fixing is introduced and a complete discussion of all the covariant gauges is given. A special symmetry relating two classes of covariant gauges is exhibited. Finally Section III contains some preliminary remarks on full nonlinear gravity. In particular we argue that in contrast to gauge fields the stochastic gravitational field cannot be transformed to a Gaussian process. (Author)
Klotho regulates CA1 hippocampal synaptic plasticity.
Li, Qin; Vo, Hai T; Wang, Jing; Fox-Quick, Stephanie; Dobrunz, Lynn E; King, Gwendalyn D
2017-04-07
Global klotho overexpression extends lifespan while global klotho-deficiency shortens it. As well, klotho protein manipulations inversely regulate cognitive function. Mice without klotho develop rapid onset cognitive impairment before they are 2months old. Meanwhile, adult mice overexpressing klotho show enhanced cognitive function, particularly in hippocampal-dependent tasks. The cognitive enhancing effects of klotho extend to humans with a klotho polymorphism that increases circulating klotho and executive function. To affect cognitive function, klotho could act in or on the synapse to modulate synaptic transmission or plasticity. However, it is not yet known if klotho is located at synapses, and little is known about its effects on synaptic function. To test this, we fractionated hippocampi and detected klotho expression in both pre and post-synaptic compartments. We find that loss of klotho enhances both pre and post-synaptic measures of CA1 hippocampal synaptic plasticity at 5weeks of age. However, a rapid loss of synaptic enhancement occurs such that by 7weeks, when mice are cognitively impaired, there is no difference from wild-type controls. Klotho overexpressing mice show no early life effects on synaptic plasticity, but decreased CA1 hippocampal long-term potentiation was measured at 6months of age. Together these data suggest that klotho affects cognition, at least in part, by regulating hippocampal synaptic plasticity. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.
The developmental stages of synaptic plasticity
Lohmann, Christian; Kessels, Helmut W.
2014-01-01
The brain is programmed to drive behaviour by precisely wiring the appropriate neuronal circuits. Wiring and rewiring of neuronal circuits largely depends on the orchestrated changes in the strengths of synaptic contacts. Here, we review how the rules of synaptic plasticity change during development
Stochastic Investigation of Natural Frequency for Functionally Graded Plates
Karsh, P. K.; Mukhopadhyay, T.; Dey, S.
2018-03-01
This paper presents the stochastic natural frequency analysis of functionally graded plates by applying artificial neural network (ANN) approach. Latin hypercube sampling is utilised to train the ANN model. The proposed algorithm for stochastic natural frequency analysis of FGM plates is validated and verified with original finite element method and Monte Carlo simulation (MCS). The combined stochastic variation of input parameters such as, elastic modulus, shear modulus, Poisson ratio, and mass density are considered. Power law is applied to distribute the material properties across the thickness. The present ANN model reduces the sample size and computationally found efficient as compared to conventional Monte Carlo simulation.
Pushing synaptic vesicles over the RIM.
Kaeser, Pascal S
2011-05-01
In a presynaptic nerve terminal, neurotransmitter release is largely restricted to specialized sites called active zones. Active zones consist of a complex protein network, and they organize fusion of synaptic vesicles with the presynaptic plasma membrane in response to action potentials. Rab3-interacting molecules (RIMs) are central components of active zones. In a recent series of experiments, we have systematically dissected the molecular mechanisms by which RIMs operate in synaptic vesicle release. We found that RIMs execute two critical functions of active zones by virtue of independent protein domains. They tether presyanptic Ca(2+) channels to the active zone, and they activate priming of synaptic vesicles by monomerizing homodimeric, constitutively inactive Munc13. These data indicate that RIMs orchestrate synaptic vesicle release into a coherent process. In conjunction with previous studies, they suggest that RIMs form a molecular platform on which plasticity of synaptic vesicle release can operate.
2017-01-01
Abstract Humans instantly recognize a previously seen face as “familiar.” To deepen our understanding of familiarity-novelty detection, we simulated biologically plausible neural network models of generic cortical microcircuits consisting of spiking neurons with random recurrent synaptic connections. NMDA receptor (NMDAR)-dependent synaptic plasticity was implemented to allow for unsupervised learning and bidirectional modifications. Network spiking activity evoked by sensory inputs consisting of face images altered synaptic efficacy, which resulted in the network responding more strongly to a previously seen face than a novel face. Network size determined how many faces could be accurately recognized as familiar. When the simulated model became sufficiently complex in structure, multiple familiarity traces could be retained in the same network by forming partially-overlapping subnetworks that differ slightly from each other, thereby resulting in a high storage capacity. Fisher’s discriminant analysis was applied to identify critical neurons whose spiking activity predicted familiar input patterns. Intriguingly, as sensory exposure was prolonged, the selected critical neurons tended to appear at deeper layers of the network model, suggesting recruitment of additional circuits in the network for incremental information storage. We conclude that generic cortical microcircuits with bidirectional synaptic plasticity have an intrinsic ability to detect familiar inputs. This ability does not require a specialized wiring diagram or supervision and can therefore be expected to emerge naturally in developing cortical circuits. PMID:28534043
Feedforward inhibition and synaptic scaling--two sides of the same coin?
Directory of Open Access Journals (Sweden)
Christian Keck
Full Text Available Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.
Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise.
Bressloff, Paul C
2011-05-03
We extend the theory of noise-induced phase synchronization to the case of a neural master equation describing the stochastic dynamics of an ensemble of uncoupled neuronal population oscillators with intrinsic and extrinsic noise. The master equation formulation of stochastic neurodynamics represents the state of each population by the number of currently active neurons, and the state transitions are chosen so that deterministic Wilson-Cowan rate equations are recovered in the mean-field limit. We apply phase reduction and averaging methods to a corresponding Langevin approximation of the master equation in order to determine how intrinsic noise disrupts synchronization of the population oscillators driven by a common extrinsic noise source. We illustrate our analysis by considering one of the simplest networks known to generate limit cycle oscillations at the population level, namely, a pair of mutually coupled excitatory (E) and inhibitory (I) subpopulations. We show how the combination of intrinsic independent noise and extrinsic common noise can lead to clustering of the population oscillators due to the multiplicative nature of both noise sources under the Langevin approximation. Finally, we show how a similar analysis can be carried out for another simple population model that exhibits limit cycle oscillations in the deterministic limit, namely, a recurrent excitatory network with synaptic depression; inclusion of synaptic depression into the neural master equation now generates a stochastic hybrid system.
The interpolation method of stochastic functions and the stochastic variational principle
International Nuclear Information System (INIS)
Liu Xianbin; Chen Qiu
1993-01-01
Uncertainties have been attaching more importance to increasingly in modern engineering structural design. Viewed on an appropriate scale, the inherent physical attributes (material properties) of many structural systems always exhibit some patterns of random variation in space and time, generally the random variation shows a small parameter fluctuation. For a linear mechanical system, the random variation is modeled as a random one of a linear partial differential operator and, in stochastic finite element method, a random variation of a stiffness matrix. Besides the stochasticity of the structural physical properties, the influences of random loads which always represent themselves as the random boundary conditions bring about much more complexities in structural analysis. Now the stochastic finite element method or the probabilistic finite element method is used to study the structural systems with random physical parameters, whether or not the loads are random. Differing from the general finite element theory, the main difficulty which the stochastic finite element method faces is the inverse operation of stochastic operators and stochastic matrices, since the inverse operators and the inverse matrices are statistically correlated to the random parameters and random loads. So far, many efforts have been made to obtain the reasonably approximate expressions of the inverse operators and inverse matrices, such as Perturbation Method, Neumann Expansion Method, Galerkin Method (in appropriate Hilbert Spaces defined for random functions), Orthogonal Expansion Method. Among these methods, Perturbation Method appear to be the most available. The advantage of these methods is that the fairly accurate response statistics can be obtained under the condition of the finite information of the input. However, the second-order statistics obtained by use of Perturbation Method and Neumann Expansion Method are not always the appropriate ones, because the relevant second
Sequential stochastic optimization
Cairoli, Renzo
1996-01-01
Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic optimization problems can be formulated and solved. Offering much material that is either new or has never before appeared in book form, it lucidly presents a unified theory of optimal stopping and optimal sequential control of stochastic processes. This book has been carefully organized so that little prior knowledge of the subject is assumed; its only prerequisites are a standard graduate course in probability theory and some familiarity with discrete-paramet
Remarks on stochastic acceleration
International Nuclear Information System (INIS)
Graeff, P.
1982-12-01
Stochastic acceleration and turbulent diffusion are strong turbulence problems since no expansion parameter exists. Hence the problem of finding rigorous results is of major interest both for checking approximations and for reference models. Since we have found a way of constructing such models in the turbulent diffusion case the question of the extension to stochastic acceleration now arises. The paper offers some possibilities illustrated by the case of 'stochastic free fall' which may be particularly interesting in the context of linear response theory. (orig.)
Cushman, John H.
1987-04-01
In a recent review article, G. Sposito et al. (1986) examined the various stochastic theories which are concerned with transport of solutes in porous media. In this short note we expand on their discussion to include several topics which had been omitted. We begin by looking at two definitions of probability theory and their relation to the concept of an ensemble. An REV ensemble of soils is defined and examined. The concept of ergodicity is reviewed, and it is pointed out that most stochastic models are theoretically unverifiable. The relationship between scale of measurement and stochasticity is briefly reviewed, and an equation that combines the two concepts is presented.
Emergent spatial synaptic structure from diffusive plasticity.
Sweeney, Yann; Clopath, Claudia
2017-04-01
Some neurotransmitters can diffuse freely across cell membranes, influencing neighbouring neurons regardless of their synaptic coupling. This provides a means of neural communication, alternative to synaptic transmission, which can influence the way in which neural networks process information. Here, we ask whether diffusive neurotransmission can also influence the structure of synaptic connectivity in a network undergoing plasticity. We propose a form of Hebbian synaptic plasticity which is mediated by a diffusive neurotransmitter. Whenever a synapse is modified at an individual neuron through our proposed mechanism, similar but smaller modifications occur in synapses connecting to neighbouring neurons. The effects of this diffusive plasticity are explored in networks of rate-based neurons. This leads to the emergence of spatial structure in the synaptic connectivity of the network. We show that this spatial structure can coexist with other forms of structure in the synaptic connectivity, such as with groups of strongly interconnected neurons that form in response to correlated external drive. Finally, we explore diffusive plasticity in a simple feedforward network model of receptive field development. We show that, as widely observed across sensory cortex, the preferred stimulus identity of neurons in our network become spatially correlated due to diffusion. Our proposed mechanism of diffusive plasticity provides an efficient mechanism for generating these spatial correlations in stimulus preference which can flexibly interact with other forms of synaptic organisation. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Optimal input design for fault detection and diagnosis
DEFF Research Database (Denmark)
Sadegh, Payman; Madsen, Henrik; Holst, J.
1995-01-01
In the paper, the design of optimal input signals for detection and diagnosis in a stochastic dynamical system is investigated. The design is based on maximization of Kullback measure between the model under fault and the model under normal operation conditions. It is established that the optimal...
QUALITATIVE DATA AND ERROR MEASUREMENT IN INPUT-OUTPUT-ANALYSIS
NIJKAMP, P; OOSTERHAVEN, J; OUWERSLOOT, H; RIETVELD, P
1992-01-01
This paper is a contribution to the rapidly emerging field of qualitative data analysis in economics. Ordinal data techniques and error measurement in input-output analysis are here combined in order to test the reliability of a low level of measurement and precision of data by means of a stochastic
International Nuclear Information System (INIS)
Ichiki, Akihisa; Shiino, Masatoshi
2007-01-01
Effects of synaptic noise on the retrieval process of associative memory neural networks are studied from the viewpoint of neurobiological and biophysical understanding of information processing in the brain. We investigate the statistical mechanical properties of stochastic analogue neural networks with temporally fluctuating synaptic noise, which is assumed to be white noise. Such networks, in general, defy the use of the replica method, since they have no energy concept. The self-consistent signal-to-noise analysis (SCSNA), which is an alternative to the replica method for deriving a set of order parameter equations, requires no energy concept and thus becomes available in studying networks without energy functions. Applying the SCSNA to stochastic networks requires the knowledge of the Thouless-Anderson-Palmer (TAP) equation which defines the deterministic networks equivalent to the original stochastic ones. The study of the TAP equation which is of particular interest for the case without energy concept is very less, while it is closely related to the SCSNA in the case with energy concept. This paper aims to derive the TAP equation for networks with synaptic noise together with a set of order parameter equations by a hybrid use of the cavity method and the SCSNA
The penumbra of learning: a statistical theory of synaptic tagging and capture.
Gershman, Samuel J
2014-01-01
Learning in humans and animals is accompanied by a penumbra: Learning one task benefits from learning an unrelated task shortly before or after. At the cellular level, the penumbra of learning appears when weak potentiation of one synapse is amplified by strong potentiation of another synapse on the same neuron during a critical time window. Weak potentiation sets a molecular tag that enables the synapse to capture plasticity-related proteins synthesized in response to strong potentiation at another synapse. This paper describes a computational model which formalizes synaptic tagging and capture in terms of statistical learning mechanisms. According to this model, synaptic strength encodes a probabilistic inference about the dynamically changing association between pre- and post-synaptic firing rates. The rate of change is itself inferred, coupling together different synapses on the same neuron. When the inputs to one synapse change rapidly, the inferred rate of change increases, amplifying learning at other synapses.
Models of Short-Term Synaptic Plasticity.
Barroso-Flores, Janet; Herrera-Valdez, Marco A; Galarraga, Elvira; Bargas, José
2017-01-01
We focus on dynamical descriptions of short-term synaptic plasticity. Instead of focusing on the molecular machinery that has been reviewed recently by several authors, we concentrate on the dynamics and functional significance of synaptic plasticity, and review some mathematical models that reproduce different properties of the dynamics of short term synaptic plasticity that have been observed experimentally. The complexity and shortcomings of these models point to the need of simple, yet physiologically meaningful models. We propose a simplified model to be tested in synapses displaying different types of short-term plasticity.
Doberkat, Ernst-Erich
2009-01-01
Combining coalgebraic reasoning, stochastic systems and logic, this volume presents the principles of coalgebraic logic from a categorical perspective. Modal logics are also discussed, including probabilistic interpretations and an analysis of Kripke models.
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Sanderson, Jennifer L; Scott, John D; Dell'Acqua, Mark L
2018-02-13
Neuronal information processing requires multiple forms of synaptic plasticity mediated by NMDA and AMPA-type glutamate receptors (NMDAR, AMPAR). These plasticity mechanisms include long-term potentiation (LTP) and depression (LTD), which are Hebbian, homosynaptic mechanisms locally regulating synaptic strength of specific inputs, and homeostatic synaptic scaling, which is a heterosynaptic mechanism globally regulating synaptic strength across all inputs. In many cases, LTP and homeostatic scaling regulate AMPAR subunit composition to increase synaptic strength via incorporation of Ca 2+ -permeable receptors (CP-AMPAR) containing GluA1, but lacking GluA2, subunits. Previous work by our group and others demonstrated that anchoring of the kinase PKA and the phosphatase calcineurin (CaN) to A-kinase anchoring protein (AKAP) 150 play opposing roles in regulation of GluA1 Ser845 phosphorylation and CP-AMPAR synaptic incorporation during hippocampal LTP and LTD. Here, using both male and female knock-in mice that are deficient in PKA or CaN anchoring, we show that AKAP150-anchored PKA and CaN also play novel roles in controlling CP-AMPAR synaptic incorporation during homeostatic plasticity in hippocampal neurons. We found that genetic disruption of AKAP-PKA anchoring prevented increases in Ser845 phosphorylation and CP-AMPAR synaptic recruitment during rapid homeostatic synaptic scaling-up induced by combined blockade of action potential firing and NMDAR activity. In contrast, genetic disruption of AKAP-CaN anchoring resulted in basal increases in Ser845 phosphorylation and CP-AMPAR synaptic activity that blocked subsequent scaling-up by preventing additional CP-AMPAR recruitment. Thus, the balanced, opposing phospho-regulation provided by AKAP-anchored PKA and CaN is essential for control of both Hebbian and homeostatic plasticity mechanisms that require CP-AMPARs. Significance statement: Neuronal circuit function is shaped by multiple forms of activity
Stochastic Models of Evolution
Bezruchko, Boris P.; Smirnov, Dmitry A.
To continue the discussion of randomness given in Sect. 2.2.1, we briefly touch on stochastic models of temporal evolution (random processes). They can be specified either via explicit definition of their statistical properties (probability density functions, correlation functions, etc., Sects. 4.1, 4.2 and 4.3) or via stochastic difference or differential equations. Some of the most widely known equations, their properties and applications are discussed in Sects. 4.4 and 4.5.
Jiang, Mingchen C; Elbasiouny, Sherif M; Collins, William F; Heckman, C J
2015-09-01
Synaptic plasticity is fundamental in shaping the output of neural networks. The transformation of synaptic plasticity at the cellular level into plasticity at the system level involves multiple factors, including behavior of local networks of interneurons. Here we investigate the synaptic to system transformation for plasticity in motor output in an in vitro preparation of the adult mouse spinal cord. System plasticity was assessed from compound action potentials (APs) in spinal ventral roots, which were generated simultaneously by the axons of many motoneurons (MNs). Synaptic plasticity was assessed from intracellular recordings of MNs. A computer model of the MN pool was used to identify the middle steps in the transformation from synaptic to system behavior. Two input systems that converge on the same MN pool were studied: one sensory and one descending. The two synaptic input systems generated very different motor outputs, with sensory stimulation consistently evoking short-term depression (STD) whereas descending stimulation had bimodal plasticity: STD at low frequencies but short-term facilitation (STF) at high frequencies. Intracellular and pharmacological studies revealed contributions from monosynaptic excitation and stimulus time-locked inhibition but also considerable asynchronous excitation sustained from local network activity. The computer simulations showed that STD in the monosynaptic excitatory input was the primary driver of the system STD in the sensory input whereas network excitation underlies the bimodal plasticity in the descending system. These results provide insight on the roles of plasticity in the monosynaptic and polysynaptic inputs converging on the same MN pool to overall motor plasticity. Copyright © 2015 the American Physiological Society.
Dideriksen, Jakob L.; Negro, Francesco; Enoka, Roger M.
2012-01-01
Motoneurons receive synaptic inputs from tens of thousands of connections that cause membrane potential to fluctuate continuously (synaptic noise), which introduces variability in discharge times of action potentials. We hypothesized that the influence of synaptic noise on force steadiness during voluntary contractions is limited to low muscle forces. The hypothesis was examined with an analytical description of transduction of motor unit spike trains into muscle force, a computational model of motor unit recruitment and rate coding, and experimental analysis of interspike interval variability during steady contractions with the abductor digiti minimi muscle. Simulations varied contraction force, level of synaptic noise, size of motor unit population, recruitment range, twitch contraction times, and level of motor unit short-term synchronization. Consistent with the analytical derivations, simulations and experimental data showed that force variability at target forces above a threshold was primarily due to low-frequency oscillations in neural drive, whereas the influence of synaptic noise was almost completely attenuated by two low-pass filters, one related to convolution of motoneuron spike trains with motor unit twitches (temporal summation) and the other attributable to summation of single motor unit forces (spatial summation). The threshold force above which synaptic noise ceased to influence force steadiness depended on recruitment range, size of motor unit population, and muscle contractile properties. This threshold was low (motor unit recruitment and muscle properties of a typical muscle are tuned to limit the influence of synaptic noise on force steadiness to low forces and that the inability to produce a constant force during stronger contractions is mainly attributable to the common low-frequency oscillations in motoneuron discharge rates. PMID:22423000
International Nuclear Information System (INIS)
Dupuy, R.
1970-01-01
The input-output supervisor is the program which monitors the flow of informations between core storage and peripheral equipments of a computer. This work is composed of three parts: 1 - Study of a generalized input-output supervisor. With sample modifications it looks like most of input-output supervisors which are running now on computers. 2 - Application of this theory on a magnetic drum. 3 - Hardware requirement for time-sharing. (author) [fr
Stochastic efficiency analysis of bovine tuberculosis-surveillance programs in the Netherlands
Asseldonk, van M.A.P.M.; Roermund, van H.J.W.; Fischer, E.A.J.; Jong, de M.C.M.; Huirne, R.B.M.
2005-01-01
We constructed a stochastic bio-economic model to determine the optimal cost-efficient surveillance program for bovine tuberculosis. The surveillance programs differed in combinations of one or more detection methods and/or sampling frequency. Stochastic input variables in the epidemiological module
Martinez, Tara L; Kong, Lingling; Wang, Xueyong; Osborne, Melissa A; Crowder, Melissa E; Van Meerbeke, James P; Xu, Xixi; Davis, Crystal; Wooley, Joe; Goldhamer, David J; Lutz, Cathleen M; Rich, Mark M; Sumner, Charlotte J
2012-06-20
The inherited motor neuron disease spinal muscular atrophy (SMA) is caused by deficient expression of survival motor neuron (SMN) protein and results in severe muscle weakness. In SMA mice, synaptic dysfunction of both neuromuscular junctions (NMJs) and central sensorimotor synapses precedes motor neuron cell death. To address whether this synaptic dysfunction is due to SMN deficiency in motor neurons, muscle, or both, we generated three lines of conditional SMA mice with tissue-specific increases in SMN expression. All three lines of mice showed increased survival, weights, and improved motor behavior. While increased SMN expression in motor neurons prevented synaptic dysfunction at the NMJ and restored motor neuron somal synapses, increased SMN expression in muscle did not affect synaptic function although it did improve myofiber size. Together these data indicate that both peripheral and central synaptic integrity are dependent on motor neurons in SMA, but SMN may have variable roles in the maintenance of these different synapses. At the NMJ, it functions at the presynaptic terminal in a cell-autonomous fashion, but may be necessary for retrograde trophic signaling to presynaptic inputs onto motor neurons. Importantly, SMN also appears to function in muscle growth and/or maintenance independent of motor neurons. Our data suggest that SMN plays distinct roles in muscle, NMJs, and motor neuron somal synapses and that restored function of SMN at all three sites will be necessary for full recovery of muscle power.
Ishikawa, Masago; Otaka, Mami; Neumann, Peter A; Wang, Zhijian; Cook, James M; Schlüter, Oliver M; Dong, Yan; Huang, Yanhua H
2013-10-01
Synaptic projections from the ventral tegmental area (VTA) to the nucleus accumbens (NAc) make up the backbone of the brain reward pathway, a neural circuit that mediates behavioural responses elicited by natural rewards as well as by cocaine and other drugs of abuse. In addition to the well-known modulatory dopaminergic projection, the VTA also provides fast excitatory and inhibitory synaptic input to the NAc, directly regulating NAc medium spiny neurons (MSNs). However, the cellular nature of VTA-to-NAc fast synaptic transmission and its roles in drug-induced adaptations are not well understood. Using viral-mediated in vivo expression of channelrhodopsin 2, the present study dissected fast excitatory and inhibitory synaptic transmission from the VTA to NAc MSNs in rats. Our results suggest that, following repeated exposure to cocaine (15 mg kg(-1) day(-1) × 5 days, i.p., 1 or 21 day withdrawal), a presynaptic enhancement of excitatory transmission and suppression of inhibitory transmission occurred at different withdrawal time points at VTA-to-NAc core synapses. In contrast, no postsynaptic alterations were detected at either type of synapse. These results suggest that changes in VTA-to-NAc fast excitatory and inhibitory synaptic transmissions may contribute to cocaine-induced alteration of the brain reward circuitry.
Collective stochastic coherence in recurrent neuronal networks
Sancristóbal, Belén; Rebollo, Beatriz; Boada, Pol; Sanchez-Vives, Maria V.; Garcia-Ojalvo, Jordi
2016-09-01
Recurrent networks of dynamic elements frequently exhibit emergent collective oscillations, which can show substantial regularity even when the individual elements are considerably noisy. How noise-induced dynamics at the local level coexists with regular oscillations at the global level is still unclear. Here we show that a combination of stochastic recurrence-based initiation with deterministic refractoriness in an excitable network can reconcile these two features, leading to maximum collective coherence for an intermediate noise level. We report this behaviour in the slow oscillation regime exhibited by a cerebral cortex network under dynamical conditions resembling slow-wave sleep and anaesthesia. Computational analysis of a biologically realistic network model reveals that an intermediate level of background noise leads to quasi-regular dynamics. We verify this prediction experimentally in cortical slices subject to varying amounts of extracellular potassium, which modulates neuronal excitability and thus synaptic noise. The model also predicts that this effectively regular state should exhibit noise-induced memory of the spatial propagation profile of the collective oscillations, which is also verified experimentally. Taken together, these results allow us to construe the high regularity observed experimentally in the brain as an instance of collective stochastic coherence.
Directory of Open Access Journals (Sweden)
Atsushi eUeda
2015-02-01
Full Text Available Homeostasis is the ability of physiological systems to regain functional balance following environment or experimental insults and synaptic homeostasis has been demonstrated in various species following genetic or pharmacological disruptions. Among environmental challenges, homeostatic responses to temperature extremes are critical to animal survival under natural conditions. We previously reported that axon terminal arborization in Drosophila larval neuromuscular junctions is enhanced at elevated temperatures; however, the amplitude of excitatory junctional potentials (EJPs remains unaltered despite the increase in synaptic bouton numbers. Here we determine the cellular basis of this homeostatic adjustment in larvae reared at high temperature (HT, 29 ˚C. We found that synaptic current focally recorded from individual synaptic boutons was unaffected by rearing temperature (30 ˚C. However, HT rearing decreased the quantal size (amplitude of spontaneous miniature EJPs, or mEJPs, which compensates for the increased number of synaptic releasing sites to retain a normal EJP size. The quantal size decrease is accounted for by a decrease in input resistance of the postsynaptic muscle fiber, indicating an increase in membrane area that matches the synaptic growth at HT. Interestingly, a mutation in rutabaga (rut encoding adenylyl cyclase (AC exhibited no obvious changes in quantal size or input resistance of postsynaptic muscle cells after HT rearing, suggesting an important role for rut AC in temperature-induced synaptic homeostasis in Drosophila. This extends our previous finding of rut-dependent synaptic homeostasis in hyperexcitable mutants, e.g. slowpoke (slo. In slo larvae, the lack of BK channel function is partially ameliorated by upregulation of presynaptic Sh IA current to limit excessive transmitter release in addition to postsynaptic glutamate receptor recomposition that reduces the quantal size.
Molecular mechanisms of synaptic remodeling in alcoholism.
Kyzar, Evan J; Pandey, Subhash C
2015-08-05
Alcohol use and alcohol addiction represent dysfunctional brain circuits resulting from neuroadaptive changes during protracted alcohol exposure and its withdrawal. Alcohol exerts a potent effect on synaptic plasticity and dendritic spine formation in specific brain regions, providing a neuroanatomical substrate for the pathophysiology of alcoholism. Epigenetics has recently emerged as a critical regulator of gene expression and synaptic plasticity-related events in the brain. Alcohol exposure and withdrawal induce changes in crucial epigenetic processes in the emotional brain circuitry (amygdala) that may be relevant to the negative affective state defined as the "dark side" of addiction. Here, we review the literature concerning synaptic plasticity and epigenetics, with a particular focus on molecular events related to dendritic remodeling during alcohol abuse and alcoholism. Targeting epigenetic processes that modulate synaptic plasticity may yield novel treatments for alcoholism. Published by Elsevier Ireland Ltd.
2015-01-01
Synaptic mitochondria are essential for maintaining calcium homeostasis and producing ATP, processes vital for neuronal integrity and synaptic transmission. Synaptic mitochondria exhibit increased oxidative damage during aging and are more vulnerable to calcium insult than nonsynaptic mitochondria. Why synaptic mitochondria are specifically more susceptible to cumulative damage remains to be determined. In this study, the generation of a super-SILAC mix that served as an appropriate internal standard for mouse brain mitochondria mass spectrometry based analysis allowed for the quantification of the proteomic differences between synaptic and nonsynaptic mitochondria isolated from 10-month-old mice. We identified a total of 2260 common proteins between synaptic and nonsynaptic mitochondria of which 1629 were annotated as mitochondrial. Quantitative proteomic analysis of the proteins common between synaptic and nonsynaptic mitochondria revealed significant differential expression of 522 proteins involved in several pathways including oxidative phosphorylation, mitochondrial fission/fusion, calcium transport, and mitochondrial DNA replication and maintenance. In comparison to nonsynaptic mitochondria, synaptic mitochondria exhibited increased age-associated mitochondrial DNA deletions and decreased bioenergetic function. These findings provide insights into synaptic mitochondrial susceptibility to damage. PMID:24708184
Inter-Synaptic Lateral Diffusion of GABAA Receptors Shapes Inhibitory Synaptic Currents.
de Luca, Emanuela; Ravasenga, Tiziana; Petrini, Enrica Maria; Polenghi, Alice; Nieus, Thierry; Guazzi, Stefania; Barberis, Andrea
2017-07-05
The lateral mobility of neurotransmitter receptors has been shown to tune synaptic signals. Here we report that GABAA receptors (GABAARs) can diffuse between adjacent dendritic GABAergic synapses in long-living desensitized states, thus laterally spreading "activation memories" between inhibitory synapses. Glutamatergic activity limits this inter-synaptic diffusion by trapping GABAARs at excitatory synapses. This novel form of activity-dependent hetero-synaptic interplay is likely to modulate dendritic synaptic signaling. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
Yakel, Jerrel L
2014-10-01
Acetylcholine (ACh) can regulate neuronal excitability in the hippocampus, an important area in the brain for learning and memory, by acting on both nicotinic (nAChRs) and muscarinic ACh receptors. The primary cholinergic input to the hippocampus arises from the medial septum and diagonal band of Broca (MS-DBB), and we investigated how their activation regulated hippocampal synaptic plasticity. We found that activation of these endogenous cholinergic inputs can directly induce different forms of hippocampal synaptic plasticity with a timing precision in the millisecond range. Furthermore, we observed a prolonged enhancement of excitability both pre- and postsynaptically. Lastly we found that the presence of the α7 nAChR subtype to both pre- and postsynaptic sites appeared to be required to induce this plasticity. We propose that α7 nAChRs coordinate pre- and postsynaptic activities to induce glutamatergic synaptic plasticity, and thus provide a novel mechanism underlying physiological neuronal communication that could lead to timing-dependent synaptic plasticity in the hippocampus. © 2014 The Authors. The Journal of Physiology © 2014 The Physiological Society.
Directory of Open Access Journals (Sweden)
Yazmín Ramiro-Cortés
Full Text Available Neuronal circuits modify their response to synaptic inputs in an experience-dependent fashion. Increases in synaptic weights are accompanied by structural modifications, and activity dependent, long lasting growth of dendritic spines requires new protein synthesis. When multiple spines are potentiated within a dendritic domain, they show dynamic structural plasticity changes, indicating that spines can undergo bidirectional physical modifications. However, it is unclear whether protein synthesis dependent synaptic depression leads to long lasting structural changes. Here, we investigate the structural correlates of protein synthesis dependent long-term depression (LTD mediated by metabotropic glutamate receptors (mGluRs through two-photon imaging of dendritic spines on hippocampal pyramidal neurons. We find that induction of mGluR-LTD leads to robust and long lasting spine shrinkage and elimination that lasts for up to 24 hours. These effects depend on signaling through group I mGluRs, require protein synthesis, and activity. These data reveal a mechanism for long lasting remodeling of synaptic inputs, and offer potential insights into mental retardation.
Lateral regulation of synaptic transmission by astrocytes.
Covelo, A; Araque, A
2016-05-26
Fifteen years ago the concept of the "tripartite synapse" was proposed to conceptualize the functional view that astrocytes are integral elements of synapses. The signaling exchange between astrocytes and neurons within the tripartite synapse results in the synaptic regulation of synaptic transmission and plasticity through an autocrine form of communication. However, recent evidence indicates that the astrocyte synaptic regulation is not restricted to the active tripartite synapse but can be manifested through astrocyte signaling at synapses relatively distant from active synapses, a process termed lateral astrocyte synaptic regulation. This phenomenon resembles the classical heterosynaptic modulation but is mechanistically different because it involves astrocytes and its properties critically depend on the morphological and functional features of astrocytes. Therefore, the functional concept of the tripartite synapse as a fundamental unit must be expanded to include the interaction between tripartite synapses. Through lateral synaptic regulation, astrocytes serve as an active processing bridge for synaptic interaction and crosstalk between synapses with no direct neuronal connectivity, supporting the idea that neural network function results from the coordinated activity of astrocytes and neurons. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.
An electronic power supply using pulse width modulated (PWM) voltage regulation provides a regulated output for a wide range of input voltages. Thus...switch to change the level of voltage regulation and the turns ratio of the primary winding of the power supply output transformer, thereby obtaining increased tolerance to input voltage change. (Author)
International Nuclear Information System (INIS)
Meyder, R.
1980-11-01
The codes system SSYST-2 is designed to analyse the thermal and mechanical behaviour of a fuel rod during a LOCA. The report contains a short introduction into the SSYST structure, a complete input-list for all modules and several tested input-list for a LOCA-analysis. (orig.) [de
Johnson-Throop, Kathy A.; Vowell, C. W.; Smith, Byron; Darcy, Jeannette
2006-01-01
This viewgraph presentation reviews the inputs to the MDS Medical Information Communique (MIC) catalog. The purpose of the group is to provide input for updating the MDS MIC Catalog and to request that MMOP assign Action Item to other working groups and FSs to support the MITWG Process for developing MIC-DDs.
Stochastic Watershed Models for Risk Based Decision Making
Vogel, R. M.
2017-12-01
Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation
Kim, Sang-Yoon; Lim, Woochang
2018-01-01
We consider the Watts-Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation of synaptic strengths, while bad synchronization gets worse via long-term depression. Emergences of long-term potentiation and long-term depression of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on states) on the SSS are studied and discussed in comparison with the case of additive STDP (independent of states). These effects of STDP on the SSS in the SWN are also compared with those in the regular lattice and the random graph. Copyright © 2017 Elsevier Ltd. All rights reserved.
Chen, Yung-Cheng; Chiao, Chuan-Chin
2008-05-01
Inputs from starburst amacrine cells (SACs) to ON-OFF direction selective ganglion cells (DSGCs) in the rabbit retina are themselves directional. However, the synaptic asymmetry between SACs and DSGCs required for generating direction selectivity has been controversial. We investigated dendritic contacts and distribution of inhibitory synapses between SACs and their overlapped DSGCs. Double injection of SAC/DSGC pairs and quantitative analysis revealed no obvious asymmetry of dendritic contacts between SACs and DSGCs. Furthermore, examination of the inhibitory input pattern on the dendrites of DSGCs using antibodies against GABA(A) receptors also suggested an isotropic arrangement with the overlapping SACs in both the preferred and the null directions. Therefore, the presynaptic mechanism of direction selectivity upon DSGCs may not result from a simple asymmetric arrangement with overlapping SACs. Multiple layer interactions and sophisticated synaptic connections between SACs and DSGCs are necessary. (c) 2008 Wiley-Liss, Inc.
Characterizing phonon dynamics using stochastic sampling
International Nuclear Information System (INIS)
Kunal, K.; Aluru, N. R.
2016-01-01
Predicting phonon relaxation time from molecular dynamics (MD) requires a long simulation time to compute the mode energy auto-correlation function. Here, we present an alternative approach to infer the phonon life-time from an approximate form of the energy auto-correlation function. The method requires as an input a set of sampled equilibrium configurations. A stochastic sampling method is used to generate the equilibrium configurations. We consider a truncated Taylor series expansion of the phonon energy auto-correlation function. The different terms in the truncated correlation function are obtained using the stochastic sampling approach. The expansion terms, thus, obtained are in good agreement with the corresponding values obtained using MD. We then use the approximate function to compute the phonon relaxation time. The relaxation time computed using this method is compared with that obtained from the exact correlation function. The two values are in agreement with each other.
Finite post synaptic potentials cause a fast neuronal response
Directory of Open Access Journals (Sweden)
Moritz eHelias
2011-02-01
Full Text Available A generic property of the communication between neurons is the exchange of pulsesat discrete time points, the action potentials. However, the prevalenttheory of spiking neuronal networks of integrate-and-fire model neuronsrelies on two assumptions: the superposition of many afferent synapticimpulses is approximated by Gaussian white noise, equivalent to avanishing magnitude of the synaptic impulses, and the transfer oftime varying signals by neurons is assessable by linearization. Goingbeyond both approximations, we find that in the presence of synapticimpulses the response to transient inputs differs qualitatively fromprevious predictions. It is instantaneous rather than exhibiting low-passcharacteristics, depends non-linearly on the amplitude of the impulse,is asymmetric for excitation and inhibition and is promoted by a characteristiclevel of synaptic background noise. These findings resolve contradictionsbetween the earlier theory and experimental observations. Here wereview the recent theoretical progress that enabled these insights.We explain why the membrane potential near threshold is sensitiveto properties of the afferent noise and show how this shapes the neuralresponse. A further extension of the theory to time evolution in discretesteps quantifies simulation artifacts and yields improved methodsto cross check results.
Local Order within Global Disorder: Synaptic Architecture of Visual Space.
Scholl, Benjamin; Wilson, Daniel E; Fitzpatrick, David
2017-12-06
Substantial evidence at the subcellular level indicates that the spatial arrangement of synaptic inputs onto dendrites could play a significant role in cortical computations, but how synapses of functionally defined cortical networks are arranged within the dendrites of individual neurons remains unclear. Here we assessed one-dimensional spatial receptive fields of individual dendritic spines within individual layer 2/3 neuron dendrites. Spatial receptive field properties of dendritic spines were strikingly diverse, with no evidence of large-scale topographic organization. At a fine scale, organization was evident: neighboring spines separated by less than 10 μm shared similar spatial receptive field properties and exhibited a distance-dependent correlation in sensory-driven and spontaneous activity patterns. Fine-scale dendritic organization was supported by the fact that functional groups of spines defined by dimensionality reduction of receptive field properties exhibited non-random dendritic clustering. Our results demonstrate that functional synaptic clustering is a robust feature existing at a local spatial scale. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.
Stochastic sensitivity analysis using HDMR and score function
Indian Academy of Sciences (India)
The method involves high dimensional model representation and score functions associated with probability distribution of a random input. The proposed approach facilitates first-and second-order approximation of stochastic sensitivity measures and statistical simulation. The formulation is general such that any simulation ...
Stochastic Change Detection based on an Active Fault Diagnosis Approach
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2007-01-01
The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an error...
Deterministic Versus Stochastic Interpretation of Continuously Monitored Sewer Systems
DEFF Research Database (Denmark)
Harremoës, Poul; Carstensen, Niels Jacob
1994-01-01
An analysis has been made of the uncertainty of input parameters to deterministic models for sewer systems. The analysis reveals a very significant uncertainty, which can be decreased, but not eliminated and has to be considered for engineering application. Stochastic models have a potential for ...
New insights into the stochastic ray production frontier
DEFF Research Database (Denmark)
Henningsen, Arne; Bělín, Matěj; Henningsen, Géraldine
The stochastic ray production frontier was developed as an alternative to the traditional output distance function to model production processes with multiple inputs and multiple outputs. Its main advantage over the traditional approach is that it can be used when some output quantities of some...
Stochastic disturbance rejection in model predictive control by randomized algorithms
Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep
2001-01-01
In this paper we consider model predictive control with stochastic disturbances and input constraints. We present an algorithm which can solve this problem approximately but with arbitrary high accuracy. The optimization at each time step is a closed loop optimization and therefore takes into
A Prediction Error Estimator for Nonlinear Stochastic Systems
Leontaritis, I.J.; Billings, S.A.
1986-01-01
A prediction error estimation algorithm incorporating model selection and validation techniques is developed for a class of multivariable discrete time stochastic nonlinear systems which can be represented by the NARMAX (Nonlinear AutoRegressive Moving Average Model with eXogenous inputs)
New insights into the stochastic ray production frontier
DEFF Research Database (Denmark)
Henningsen, Arne; Bělín, Matěj; Henningsen, Geraldine
2017-01-01
The stochastic ray production frontier was developed as an alternative to the traditional output distance function to model production processes with multiple inputs and multiple outputs. Its main advantage over the traditional approach is that it can be used when some output quantities of some o...
Energy Technology Data Exchange (ETDEWEB)
2017-02-01
The PLEXOS Input Data Generator (PIDG) is a tool that enables PLEXOS users to better version their data, automate data processing, collaborate in developing inputs, and transfer data between different production cost modeling and other power systems analysis software. PIDG can process data that is in a generalized format from multiple input sources, including CSV files, PostgreSQL databases, and PSS/E .raw files and write it to an Excel file that can be imported into PLEXOS with only limited manual intervention.
Sajikumar, Sreedharan; Korte, Martin
2011-01-01
The consolidation process from short- to long-term memory depends on the type of stimulation received from a specific neuronal network and on the cooperativity and associativity between different synaptic inputs converging onto a specific neuron. We show here that the plasticity thresholds for inducing LTP are different in proximal and distal…
Synaptic adhesion molecule IgSF11 regulates synaptic transmission and plasticity
Shin, Hyewon; van Riesen, Christoph; Whitcomb, Daniel; Warburton, Julia M.; Jo, Jihoon; Kim, Doyoun; Kim, Sun Gyun; Um, Seung Min; Kwon, Seok-kyu; Kim, Myoung-Hwan; Roh, Junyeop Daniel; Woo, Jooyeon; Jun, Heejung; Lee, Dongmin; Mah, Won; Kim, Hyun; Kaang, Bong-Kiun; Cho, Kwangwook; Rhee, Jeong-Seop; Choquet, Daniel; Kim, Eunjoon
2016-01-01
Summary Synaptic adhesion molecules regulate synapse development and plasticity through mechanisms including trans-synaptic adhesion and recruitment of diverse synaptic proteins. We report here that the immunoglobulin superfamily member 11 (IgSF11), a homophilic adhesion molecule preferentially expressed in the brain, is a novel and dual-binding partner of the postsynaptic scaffolding protein PSD-95 and AMPAR glutamate receptors (AMPARs). IgSF11 requires PSD-95 binding for its excitatory synaptic localization. In addition, IgSF11 stabilizes synaptic AMPARs, as shown by IgSF11 knockdown-induced suppression of AMPAR-mediated synaptic transmission and increased surface mobility of AMPARs, measured by high-throughput, single-molecule tracking. IgSF11 deletion in mice leads to suppression of AMPAR-mediated synaptic transmission in the dentate gyrus and long-term potentiation in the CA1 region of the hippocampus. IgSF11 does not regulate the functional characteristics of AMPARs, including desensitization, deactivation, or recovery. These results suggest that IgSF11 regulates excitatory synaptic transmission and plasticity through its tripartite interactions with PSD-95 and AMPARs. PMID:26595655
Stochastic cooling at Fermilab
International Nuclear Information System (INIS)
Marriner, J.
1986-08-01
The topics discussed are the stochastic cooling systems in use at Fermilab and some of the techniques that have been employed to meet the particular requirements of the anti-proton source. Stochastic cooling at Fermilab became of paramount importance about 5 years ago when the anti-proton source group at Fermilab abandoned the electron cooling ring in favor of a high flux anti-proton source which relied solely on stochastic cooling to achieve the phase space densities necessary for colliding proton and anti-proton beams. The Fermilab systems have constituted a substantial advance in the techniques of cooling including: large pickup arrays operating at microwave frequencies, extensive use of cryogenic techniques to reduce thermal noise, super-conducting notch filters, and the development of tools for controlling and for accurately phasing the system
Stochastic dynamics and irreversibility
Tomé, Tânia
2015-01-01
This textbook presents an exposition of stochastic dynamics and irreversibility. It comprises the principles of probability theory and the stochastic dynamics in continuous spaces, described by Langevin and Fokker-Planck equations, and in discrete spaces, described by Markov chains and master equations. Special concern is given to the study of irreversibility, both in systems that evolve to equilibrium and in nonequilibrium stationary states. Attention is also given to the study of models displaying phase transitions and critical phenomema both in thermodynamic equilibrium and out of equilibrium. These models include the linear Glauber model, the Glauber-Ising model, lattice models with absorbing states such as the contact process and those used in population dynamic and spreading of epidemic, probabilistic cellular automata, reaction-diffusion processes, random sequential adsorption and dynamic percolation. A stochastic approach to chemical reaction is also presented.The textbook is intended for students of ...
International Nuclear Information System (INIS)
Rumpf, H.
1987-01-01
We begin with a naive application of the Parisi-Wu scheme to linearized gravity. This will lead into trouble as one peculiarity of the full theory, the indefiniteness of the Euclidean action, shows up already at this level. After discussing some proposals to overcome this problem, Minkowski space stochastic quantization will be introduced. This will still not result in an acceptable quantum theory of linearized gravity, as the Feynman propagator turns out to be non-causal. This defect will be remedied only after a careful analysis of general covariance in stochastic quantization has been performed. The analysis requires the notion of a metric on the manifold of metrics, and a natural candidate for this is singled out. With this a consistent stochastic quantization of Einstein gravity becomes possible. It is even possible, at least perturbatively, to return to the Euclidean regime. 25 refs. (Author)
Stochastic optimization methods
Marti, Kurt
2005-01-01
Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.
Separable quadratic stochastic operators
International Nuclear Information System (INIS)
Rozikov, U.A.; Nazir, S.
2009-04-01
We consider quadratic stochastic operators, which are separable as a product of two linear operators. Depending on properties of these linear operators we classify the set of the separable quadratic stochastic operators: first class of constant operators, second class of linear and third class of nonlinear (separable) quadratic stochastic operators. Since the properties of operators from the first and second classes are well known, we mainly study the properties of the operators of the third class. We describe some Lyapunov functions of the operators and apply them to study ω-limit sets of the trajectories generated by the operators. We also compare our results with known results of the theory of quadratic operators and give some open problems. (author)
Directory of Open Access Journals (Sweden)
Caixia Lv
2016-06-01
Full Text Available Synaptic ribbons are structures made largely of the protein Ribeye that hold synaptic vesicles near release sites in non-spiking cells in some sensory systems. Here, we introduce frameshift mutations in the two zebrafish genes encoding for Ribeye and thus remove Ribeye protein from neuromast hair cells. Despite Ribeye depletion, vesicles collect around ribbon-like structures that lack electron density, which we term “ghost ribbons.” Ghost ribbons are smaller in size but possess a similar number of smaller vesicles and are poorly localized to synapses and calcium channels. These hair cells exhibit enhanced exocytosis, as measured by capacitance, and recordings from afferent neurons post-synaptic to hair cells show no significant difference in spike rates. Our results suggest that Ribeye makes up most of the synaptic ribbon density in neuromast hair cells and is necessary for proper localization of calcium channels and synaptic ribbons.
GABA Metabolism and Transport: Effects on Synaptic Efficacy
Directory of Open Access Journals (Sweden)
Fabian C. Roth
2012-01-01
Full Text Available GABAergic inhibition is an important regulator of excitability in neuronal networks. In addition, inhibitory synaptic signals contribute crucially to the organization of spatiotemporal patterns of network activity, especially during coherent oscillations. In order to maintain stable network states, the release of GABA by interneurons must be plastic in timing and amount. This homeostatic regulation is achieved by several pre- and postsynaptic mechanisms and is triggered by various activity-dependent local signals such as excitatory input or ambient levels of neurotransmitters. Here, we review findings on the availability of GABA for release at presynaptic terminals of interneurons. Presynaptic GABA content seems to be an important determinant of inhibitory efficacy and can be differentially regulated by changing synthesis, transport, and degradation of GABA or related molecules. We will discuss the functional impact of such regulations on neuronal network patterns and, finally, point towards pharmacological approaches targeting these processes.
Stochastic dynamics and control
Sun, Jian-Qiao; Zaslavsky, George
2006-01-01
This book is a result of many years of author's research and teaching on random vibration and control. It was used as lecture notes for a graduate course. It provides a systematic review of theory of probability, stochastic processes, and stochastic calculus. The feedback control is also reviewed in the book. Random vibration analyses of SDOF, MDOF and continuous structural systems are presented in a pedagogical order. The application of the random vibration theory to reliability and fatigue analysis is also discussed. Recent research results on fatigue analysis of non-Gaussian stress proc
Foundations of stochastic analysis
Rao, M M; Lukacs, E
1981-01-01
Foundations of Stochastic Analysis deals with the foundations of the theory of Kolmogorov and Bochner and its impact on the growth of stochastic analysis. Topics covered range from conditional expectations and probabilities to projective and direct limits, as well as martingales and likelihood ratios. Abstract martingales and their applications are also discussed. Comprised of five chapters, this volume begins with an overview of the basic Kolmogorov-Bochner theorem, followed by a discussion on conditional expectations and probabilities containing several characterizations of operators and mea
Stochastic models, estimation, and control
Maybeck, Peter S
1982-01-01
This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
Diacylglycerol Kinases in the Coordination of Synaptic Plasticity.
Lee, Dongwon; Kim, Eunjoon; Tanaka-Yamamoto, Keiko
2016-01-01
Synaptic plasticity is activity-dependent modification of the efficacy of synaptic transmission. Although, detailed mechanisms underlying synaptic plasticity are diverse and vary at different types of synapses, diacylglycerol (DAG)-associated signaling has been considered as an important regulator of many forms of synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD). Recent evidences indicate that DAG kinases (DGKs), which phosphorylate DAG to phosphatidic acid to terminate DAG signaling, are important regulators of LTP and LTD, as supported by the results from mice lacking specific DGK isoforms. This review will summarize these studies and discuss how specific DGK isoforms distinctly regulate different forms of synaptic plasticity at pre- and postsynaptic sites. In addition, we propose a general role of DGKs as coordinators of synaptic plasticity that make local synaptic environments more permissive for synaptic plasticity by regulating DAG concentration and interacting with other synaptic proteins.
Cohen, Laurie D.; Zuchman, Rina; Sorokina, Oksana; Müller, Anke; Dieterich, Daniela C.; Armstrong, J. Douglas; Ziv, Tamar; Ziv, Noam E.
2013-01-01
Chemical synapses contain multitudes of proteins, which in common with all proteins, have finite lifetimes and therefore need to be continuously replaced. Given the huge numbers of synaptic connections typical neurons form, the demand to maintain the protein contents of these connections might be expected to place considerable metabolic demands on each neuron. Moreover, synaptic proteostasis might differ according to distance from global protein synthesis sites, the availability of distributed protein synthesis facilities, trafficking rates and synaptic protein dynamics. To date, the turnover kinetics of synaptic proteins have not been studied or analyzed systematically, and thus metabolic demands or the aforementioned relationships remain largely unknown. In the current study we used dynamic Stable Isotope Labeling with Amino acids in Cell culture (SILAC), mass spectrometry (MS), Fluorescent Non–Canonical Amino acid Tagging (FUNCAT), quantitative immunohistochemistry and bioinformatics to systematically measure the metabolic half-lives of hundreds of synaptic proteins, examine how these depend on their pre/postsynaptic affiliation or their association with particular molecular complexes, and assess the metabolic load of synaptic proteostasis. We found that nearly all synaptic proteins identified here exhibited half-lifetimes in the range of 2–5 days. Unexpectedly, metabolic turnover rates were not significantly different for presynaptic and postsynaptic proteins, or for proteins for which mRNAs are consistently found in dendrites. Some functionally or structurally related proteins exhibited very similar turnover rates, indicating that their biogenesis and degradation might be coupled, a possibility further supported by bioinformatics-based analyses. The relatively low turnover rates measured here (∼0.7% of synaptic protein content per hour) are in good agreement with imaging-based studies of synaptic protein trafficking, yet indicate that the metabolic load
Stochastic Decoding of Turbo Codes
Dong , Q. T.; ARZEL , Matthieu; Jego , Christophe; Gross , W. J.
2010-01-01
International audience; Stochastic computation is a technique in which operations on probabilities are performed on random bit streams. Stochastic decoding of forward error-correction (FEC) codes is inspired by this technique. This paper extends the application of the stochastic decoding approach to the families of convolutional codes and turbo codes. It demonstrates that stochastic computation is a promising solution to improve the data throughput of turbo decoders with very simple implement...
Stochastic quantization of Proca field
International Nuclear Information System (INIS)
Lim, S.C.
1981-03-01
We discuss the complications that arise in the application of Nelson's stochastic quantization scheme to classical Proca field. One consistent way to obtain spin-one massive stochastic field is given. It is found that the result of Guerra et al on the connection between ground state stochastic field and the corresponding Euclidean-Markov field extends to the spin-one case. (author)
Inter-synaptic learning of combination rules in a cortical network model
Directory of Open Access Journals (Sweden)
Frédéric eLavigne
2014-08-01
Full Text Available Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons.Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.
Input modeling with phase-type distributions and Markov models theory and applications
Buchholz, Peter; Felko, Iryna
2014-01-01
Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models. The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system...
Schrager, D.F.
2006-01-01
We propose a new model for stochastic mortality. The model is based on the literature on affine term structure models. It satisfies three important requirements for application in practice: analytical tractibility, clear interpretation of the factors and compatibility with financial option pricing
Indian Academy of Sciences (India)
andoh
1Indian Institute of Science Education and Research, Mohali. 2The Institute of Mathematical Sciences, Chennai ... Bottomline of this work: One can obtain very reliable Logic Circuit. Elements by exploiting nonlinearity in the presence of ... with the truth tables of the basic logic operations. Sudeshna Sinha. Logical Stochastic ...
Energy Technology Data Exchange (ETDEWEB)
Tollestrup, A.V.; Dugan, G
1983-12-01
Major headings in this review include: proton sources; antiproton production; antiproton sources and Liouville, the role of the Debuncher; transverse stochastic cooling, time domain; the accumulator; frequency domain; pickups and kickers; Fokker-Planck equation; calculation of constants in the Fokker-Planck equation; and beam feedback. (GHT)
Stochastic nonlinear beam equations
Czech Academy of Sciences Publication Activity Database
Brzezniak, Z.; Maslowski, Bohdan; Seidler, Jan
2005-01-01
Roč. 132, č. 1 (2005), s. 119-149 ISSN 0178-8051 R&D Projects: GA ČR(CZ) GA201/01/1197 Institutional research plan: CEZ:AV0Z10190503 Keywords : stochastic beam equation * stability Subject RIV: BA - General Mathematics Impact factor: 0.896, year: 2005
Wolff, Hans
This paper deals with a stochastic process for the approximation of the root of a regression equation. This process was first suggested by Robbins and Monro. The main result here is a necessary and sufficient condition on the iteration coefficients for convergence of the process (convergence with probability one and convergence in the quadratic…
Stochastic modelling of turbulence
DEFF Research Database (Denmark)
Sørensen, Emil Hedevang Lohse
previously been shown to be closely connected to the energy dissipation. The incorporation of the small scale dynamics into the spatial model opens the door to a fully fledged stochastic model of turbulence. Concerning the interaction of wind and wind turbine, a new method is proposed to extract wind turbine...
Composite stochastic processes
Kampen, N.G. van
Certain problems in physics and chemistry lead to the definition of a class of stochastic processes. Although they are not Markovian they can be treated explicitly to some extent. In particular, the probability distribution for large times can be found. It is shown to obey a master equation. This
Entropy Production in Stochastics
Directory of Open Access Journals (Sweden)
Demetris Koutsoyiannis
2017-10-01
Full Text Available While the modern definition of entropy is genuinely probabilistic, in entropy production the classical thermodynamic definition, as in heat transfer, is typically used. Here we explore the concept of entropy production within stochastics and, particularly, two forms of entropy production in logarithmic time, unconditionally (EPLT or conditionally on the past and present having been observed (CEPLT. We study the theoretical properties of both forms, in general and in application to a broad set of stochastic processes. A main question investigated, related to model identification and fitting from data, is how to estimate the entropy production from a time series. It turns out that there is a link of the EPLT with the climacogram, and of the CEPLT with two additional tools introduced here, namely the differenced climacogram and the climacospectrum. In particular, EPLT and CEPLT are related to slopes of log-log plots of these tools, with the asymptotic slopes at the tails being most important as they justify the emergence of scaling laws of second-order characteristics of stochastic processes. As a real-world application, we use an extraordinary long time series of turbulent velocity and show how a parsimonious stochastic model can be identified and fitted using the tools developed.
Stochastic cooling equipment at the ISR
1983-01-01
The photo shows (centre) an experimental set-up for stochastic cooling of vertical betatron oscillations, used at the ISR in the years before the ICE ring was built. Cooling times of about 30 min were obtained in the low intensity range (~0.3 A). To be noted the four 50 Ohm brass input/output connections with cooling fins, and the baking-out sheet around the cylinder. On the left one sees a clearing electrode box allowing the electrode current to be measured, and the pressure seen by the beam to be evaluated.
Synaptic Correlates of Working Memory Capacity.
Mi, Yuanyuan; Katkov, Mikhail; Tsodyks, Misha
2017-01-18
Psychological studies indicate that human ability to keep information in readily accessible working memory is limited to four items for most people. This extremely low capacity severely limits execution of many cognitive tasks, but its neuronal underpinnings remain unclear. Here we show that in the framework of synaptic theory of working memory, capacity can be analytically estimated to scale with characteristic time of short-term synaptic depression relative to synaptic current time constant. The number of items in working memory can be regulated by external excitation, enabling the system to be tuned to the desired load and to clear the working memory of currently held items to make room for new ones. Copyright © 2017 Elsevier Inc. All rights reserved.
Astrocytes optimize the synaptic transmission of information.
Directory of Open Access Journals (Sweden)
Suhita Nadkarni
2008-05-01
Full Text Available Chemical synapses transmit information via the release of neurotransmitter-filled vesicles from the presynaptic terminal. Using computational modeling, we predict that the limited availability of neurotransmitter resources in combination with the spontaneous release of vesicles limits the maximum degree of enhancement of synaptic transmission. This gives rise to an optimal tuning that depends on the number of active zones. There is strong experimental evidence that astrocytes that enwrap synapses can modulate the probabilities of vesicle release through bidirectional signaling and hence regulate synaptic transmission. For low-fidelity hippocampal synapses, which typically have only one or two active zones, the predicted optimal values lie close to those determined by experimentally measured astrocytic feedback, suggesting that astrocytes optimize synaptic transmission of information.
Directory of Open Access Journals (Sweden)
Rozan Vroman
2014-05-01
Full Text Available Neuronal computations strongly depend on inhibitory interactions. One such example occurs at the first retinal synapse, where horizontal cells inhibit photoreceptors. This interaction generates the center/surround organization of bipolar cell receptive fields and is crucial for contrast enhancement. Despite its essential role in vision, the underlying synaptic mechanism has puzzled the neuroscience community for decades. Two competing hypotheses are currently considered: an ephaptic and a proton-mediated mechanism. Here we show that horizontal cells feed back to photoreceptors via an unexpected synthesis of the two. The first one is a very fast ephaptic mechanism that has no synaptic delay, making it one of the fastest inhibitory synapses known. The second one is a relatively slow (τ≈200 ms, highly intriguing mechanism. It depends on ATP release via Pannexin 1 channels located on horizontal cell dendrites invaginating the cone synaptic terminal. The ecto-ATPase NTPDase1 hydrolyses extracellular ATP to AMP, phosphate groups, and protons. The phosphate groups and protons form a pH buffer with a pKa of 7.2, which keeps the pH in the synaptic cleft relatively acidic. This inhibits the cone Ca²⁺ channels and consequently reduces the glutamate release by the cones. When horizontal cells hyperpolarize, the pannexin 1 channels decrease their conductance, the ATP release decreases, and the formation of the pH buffer reduces. The resulting alkalization in the synaptic cleft consequently increases cone glutamate release. Surprisingly, the hydrolysis of ATP instead of ATP itself mediates the synaptic modulation. Our results not only solve longstanding issues regarding horizontal cell to photoreceptor feedback, they also demonstrate a new form of synaptic modulation. Because pannexin 1 channels and ecto-ATPases are strongly expressed in the nervous system and pannexin 1 function is implicated in synaptic plasticity, we anticipate that this novel form
Stochastic processes in cell biology
Bressloff, Paul C
2014-01-01
This book develops the theory of continuous and discrete stochastic processes within the context of cell biology. A wide range of biological topics are covered including normal and anomalous diffusion in complex cellular environments, stochastic ion channels and excitable systems, stochastic calcium signaling, molecular motors, intracellular transport, signal transduction, bacterial chemotaxis, robustness in gene networks, genetic switches and oscillators, cell polarization, polymerization, cellular length control, and branching processes. The book also provides a pedagogical introduction to the theory of stochastic process – Fokker Planck equations, stochastic differential equations, master equations and jump Markov processes, diffusion approximations and the system size expansion, first passage time problems, stochastic hybrid systems, reaction-diffusion equations, exclusion processes, WKB methods, martingales and branching processes, stochastic calculus, and numerical methods. This text is primarily...
Obesity-driven synaptic remodeling affects endocannabinoid control of orexinergic neurons
Cristino, Luigia; Busetto, Giuseppe; Imperatore, Roberta; Ferrandino, Ida; Palomba, Letizia; Silvestri, Cristoforo; Petrosino, Stefania; Orlando, Pierangelo; Bentivoglio, Marina; Mackie, Kenneth; Di Marzo, Vincenzo
2013-01-01
Acute or chronic alterations in energy status alter the balance between excitatory and inhibitory synaptic transmission and associated synaptic plasticity to allow for the adaptation of energy metabolism to new homeostatic requirements. The impact of such changes on endocannabinoid and cannabinoid receptor type 1 (CB1)-mediated modulation of synaptic transmission and strength is not known, despite the fact that this signaling system is an important target for the development of new drugs against obesity. We investigated whether CB1-expressing excitatory vs. inhibitory inputs to orexin-A–containing neurons in the lateral hypothalamus are altered in obesity and how this modifies endocannabinoid control of these neurons. In lean mice, these inputs are mostly excitatory. By confocal and ultrastructural microscopic analyses, we observed that in leptin-knockout (ob/ob) obese mice, and in mice with diet-induced obesity, orexinergic neurons receive predominantly inhibitory CB1-expressing inputs and overexpress the biosynthetic enzyme for the endocannabinoid 2-arachidonoylglycerol, which retrogradely inhibits synaptic transmission at CB1-expressing axon terminals. Patch-clamp recordings also showed increased CB1-sensitive inhibitory innervation of orexinergic neurons in ob/ob mice. These alterations are reversed by leptin administration, partly through activation of the mammalian target of rapamycin pathway in neuropeptide-Y-ergic neurons of the arcuate nucleus, and are accompanied by CB1-mediated enhancement of orexinergic innervation of target brain areas. We propose that enhanced inhibitory control of orexin-A neurons, and their CB1-mediated disinhibition, are a consequence of leptin signaling impairment in the arcuate nucleus. We also provide initial evidence of the participation of this phenomenon in hyperphagia and hormonal dysregulation in obesity. PMID:23630288
3-Dimensional Stochastic Seepage Analysis of a Yangtze River Embankment
Directory of Open Access Journals (Sweden)
Yajun Wang
2015-01-01
Full Text Available Three-dimensional stochastic simulation was performed to investigate the complexity of the seepage field of an embankment. Three-dimensional anisotropic heterogeneous steady state random seepage finite element model was developed. The material input data were derived from a statistical analysis of strata soil characteristics and geological columns. The Kolmogorov-Smirnov test was used to validate the hypothesis that the Gaussian probability distribution is applicable to the random permeability tensors. A stochastic boundary condition, the random variation of upstream and downstream water level, was taken into account in the three-dimensional finite element modelling. Furthermore, the functions of sheet-pile breakwater and catchwater were also incorporated as turbulent sources. This case, together with the variability of soil permeability, has been analyzed to investigate their influence on the hydraulic potential distributed and the random evolution of stochastic seepage field. Results from stochastic analyses have also been compared against those of deterministic analyses. The insights gained in this study suggest it is necessary, feasible, and practical to employ stochastic studies in seepage field problems. The method provides a more comprehensive stochastic algorithm than conventional ones to characterize and analyze three-dimensional random seepage field problems.
Deciphering resting microglial morphology and process motility from a synaptic prospect
Directory of Open Access Journals (Sweden)
Ines eHristovska
2016-01-01
Full Text Available Microglia, the resident immune cells of the central nervous system (CNS, were traditionally believed to be set into action only in case of injury or disease. Accordingly, microglia were assumed to be inactive or resting in the healthy brain. However, recent studies revealed that microglia carry out active tissue sampling in the intact brain by extending and retracting their ramified processes while periodically contacting synapses. Microglial morphology and motility as well as the frequency and duration of physical contacts with synaptic elements were found to be modulated by neuronal activity, sensory experience and neurotransmission; however findings have not been straightforward. Microglial cells are the most morphologically plastic element of the CNS. This unique feature confers them the possibility to locally sense activity, and to respond adequately by establishing synaptic contacts to regulate synaptic inputs by the secretion of signaling molecules. Indeed, microglial cells can hold new roles as critical players in maintaining brain homeostasis and regulating synaptic number, maturation and plasticity. For this reason, a better characterization of microglial cells and cues mediating neuron-to-microglia communication under physiological conditions may help advance our understanding of the microglial behavior and its regulation in the healthy brain. This review highlights recent findings on the instructive role of neuronal activity on microglial motility and microglia-synapse interactions, focusing on the main transmitters involved in this communication and including newly described communication at the tripartite synapse.
Directory of Open Access Journals (Sweden)
Katharine L. Dobson
2015-01-01
Full Text Available In the cerebellar molecular layer parallel fibre terminals release glutamate from both the active zone and from extrasynaptic “ectopic” sites. Ectopic release mediates transmission to the Bergmann glia that ensheathe the synapse, activating Ca2+-permeable AMPA receptors and glutamate transporters. Parallel fibre terminals exhibit several forms of presynaptic plasticity, including cAMP-dependent long-term potentiation and endocannabinoid-dependent long-term depression, but it is not known whether these presynaptic forms of long-term plasticity also influence ectopic transmission to Bergmann glia. Stimulation of parallel fibre inputs at 16 Hz evoked LTP of synaptic transmission, but LTD of ectopic transmission. Pharmacological activation of adenylyl cyclase by forskolin caused LTP at Purkinje neurons, but only transient potentiation at Bergmann glia, reinforcing the concept that ectopic sites lack the capacity to express sustained cAMP-dependent potentiation. Activation of mGluR1 caused depression of synaptic transmission via retrograde endocannabinoid signalling but had no significant effect at ectopic sites. In contrast, activation of NMDA receptors suppressed both synaptic and ectopic transmission. The results suggest that the signalling mechanisms for presynaptic LTP and retrograde depression by endocannabinoids are restricted to the active zone at parallel fibre synapses, allowing independent modulation of synaptic transmission to Purkinje neurons and ectopic transmission to Bergmann glia.
Astroglial calcium signaling displays short-term plasticity and adjusts synaptic efficacy
Directory of Open Access Journals (Sweden)
Jeremie eSibille
2015-05-01
Full Text Available Astrocytes are dynamic signaling brain elements able to sense neuronal inputs and to respond by complex calcium signals, which are thought to represent their excitability. Such signaling has been proposed to modulate, or not, neuronal activities ranging from basal synaptic transmission to epileptiform discharges. However, whether calcium signaling in astrocytes exhibits activity-dependent changes and acutely modulates short-term synaptic plasticity is currently unclear. We here show, using dual recordings of astroglial calcium signals and synaptic transmission, that calcium signaling in astrocytes displays, concomitantly to excitatory synapses, short-term plasticity in response to prolonged repetitive and tetanic stimulations of Schaffer collaterals. We also found that acute inhibition of calcium signaling in astrocytes by intracellular calcium chelation rapidly potentiates excitatory synaptic transmission and short-term plasticity of Shaffer collateral CA1 synapses, i.e. paired-pulse facilitation and responses to tetanic and prolonged repetitive stimulation. These data reveal that calcium signaling of astrocytes is plastic and down-regulates basal transmission and short-term plasticity of hippocampal CA1 glutamatergic synapses.
Linear stochastic degenerate Sobolev equations and applications†
Liaskos, Konstantinos B.; Pantelous, Athanasios A.; Stratis, Ioannis G.
2015-12-01
In this paper, a general class of linear stochastic degenerate Sobolev equations with additive noise is considered. This class of systems is the infinite-dimensional analogue of linear descriptor systems in finite dimensions. Under appropriate assumptions, the mild and strong well-posedness for the initial value problem are studied using elements of the semigroup theory and properties of the stochastic convolution. The final value problem is also examined and it is proved that this is uniquely strongly solvable and the solution is continuously dependent on the final data. Based on the results of the forward and backward problem, the conditions for the exact controllability are investigated for a special but important class of these equations. The abstract results are illustrated by applications in complex media electromagnetics, in the one-dimensional stochastic Dirac equation in the non-relativistic limit and in a potential application in input-output analysis in economics. Dedicated to Professor Grigoris Kalogeropoulos on the occasion of his seventieth birthday.
Stochastic Non-Parametric Frontier Analysis
Directory of Open Access Journals (Sweden)
Mohammad Rahmani
2014-05-01
Full Text Available In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production eciency. Specically, our approach rst allows for statistical noise, similar to Stochastic frontier analysis , and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Kumbhakar et al. We will use local-spherical coordinate system to transform multi-input multi-output data to more exible system which we can use in our approach. We also illustrate the performance of our approach with simulated example
DEFF Research Database (Denmark)
2013-01-01
you to input a text file with a raw list of lexemes that appear in the construction under investigation. This is converted into an a frequency with the format described above. Open this file in a spreadsheet and fill in the corpus-wide word frequencies and save the file as a text file. You can now use...
Recurrent network models for perfect temporal integration of fluctuating correlated inputs.
Directory of Open Access Journals (Sweden)
Hiroshi Okamoto
2009-06-01
Full Text Available Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.
Stochastic approach for radionuclides quantification
Clement, A.; Saurel, N.; Perrin, G.
2018-01-01
Gamma spectrometry is a passive non-destructive assay used to quantify radionuclides present in more or less complex objects. Basic methods using empirical calibration with a standard in order to quantify the activity of nuclear materials by determining the calibration coefficient are useless on non-reproducible, complex and single nuclear objects such as waste packages. Package specifications as composition or geometry change from one package to another and involve a high variability of objects. Current quantification process uses numerical modelling of the measured scene with few available data such as geometry or composition. These data are density, material, screen, geometric shape, matrix composition, matrix and source distribution. Some of them are strongly dependent on package data knowledge and operator backgrounds. The French Commissariat à l'Energie Atomique (CEA) is developing a new methodology to quantify nuclear materials in waste packages and waste drums without operator adjustment and internal package configuration knowledge. This method suggests combining a global stochastic approach which uses, among others, surrogate models available to simulate the gamma attenuation behaviour, a Bayesian approach which considers conditional probability densities of problem inputs, and Markov Chains Monte Carlo algorithms (MCMC) which solve inverse problems, with gamma ray emission radionuclide spectrum, and outside dimensions of interest objects. The methodology is testing to quantify actinide activity in different kind of matrix, composition, and configuration of sources standard in terms of actinide masses, locations and distributions. Activity uncertainties are taken into account by this adjustment methodology.
Synaptic plasticity and the warburg effect
Magistretti, Pierre J.
2014-01-01
Functional brain imaging studies show that in certain brain regions glucose utilization exceeds oxygen consumption, indicating the predominance of aerobic glycolysis. In this issue, Goyal et al. (2014) report that this metabolic profile is associated with an enrichment in the expression of genes involved in synaptic plasticity and remodeling processes. © 2014 Elsevier Inc.
P2X Receptors and Synaptic Plasticity
Czech Academy of Sciences Publication Activity Database
Pankratov, Y.; Lalo, U.; Krishtal, A.; Verkhratsky, Alexei
2009-01-01
Roč. 158, č. 1 (2009), s. 137-148 ISSN 0306-4522 Institutional research plan: CEZ:AV0Z50390512 Keywords : ATP * P2X receptors * synaptic plasticity Subject RIV: FH - Neurology Impact factor: 3.292, year: 2009
Neuronal cytoskeleton in synaptic plasticity and regeneration.
Gordon-Weeks, Phillip R; Fournier, Alyson E
2014-04-01
During development, dynamic changes in the axonal growth cone and dendrite are necessary for exploratory movements underlying initial axo-dendritic contact and ultimately the formation of a functional synapse. In the adult central nervous system, an impressive degree of plasticity is retained through morphological and molecular rearrangements in the pre- and post-synaptic compartments that underlie the strengthening or weakening of synaptic pathways. Plasticity is regulated by the interplay of permissive and inhibitory extracellular cues, which signal through receptors at the synapse to regulate the closure of critical periods of developmental plasticity as well as by acute changes in plasticity in response to experience and activity in the adult. The molecular underpinnings of synaptic plasticity are actively studied and it is clear that the cytoskeleton is a key substrate for many cues that affect plasticity. Many of the cues that restrict synaptic plasticity exhibit residual activity in the injured adult CNS and restrict regenerative growth by targeting the cytoskeleton. Here, we review some of the latest insights into how cytoskeletal remodeling affects neuronal plasticity and discuss how the cytoskeleton is being targeted in an effort to promote plasticity and repair following traumatic injury in the central nervous system. © 2013 International Society for Neurochemistry.
Stochastic Modeling of Radioactive Material Releases
International Nuclear Information System (INIS)
Andrus, Jason; Pope, Chad
2015-01-01
Nonreactor nuclear facilities operated under the approval authority of the U.S. Department of Energy use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge or exceed dose evaluation guidelines. Unmitigated design basis events that sufficiently challenge dose evaluation guidelines or exceed the guidelines for members of the public or workers, merit selection of safety structures, systems, or components or other controls to prevent or mitigate the hazard. Idaho State University, in collaboration with Idaho National Laboratory, has developed a portable and simple to use software application called SODA (Stochastic Objective Decision-Aide) that stochastically calculates the radiation dose associated with hypothetical radiological material release scenarios. Rather than producing a point estimate of the dose, SODA produces a dose distribution result to allow a deeper understanding of the dose potential. SODA allows users to select the distribution type and parameter values for all of the input variables used to perform the dose calculation. SODA then randomly samples each distribution input variable and calculates the overall resulting dose distribution. In cases where an input variable distribution is unknown, a traditional single point value can be used. SODA was developed using the MATLAB coding framework. The software application has a graphical user input. SODA can be installed on both Windows and Mac computers and does not require MATLAB to function. SODA provides improved risk understanding leading to better informed decision making associated with establishing nuclear facility material-at-risk limits and safety structure, system, or component selection. It is important to note that SODA does not replace or compete with codes such as MACCS or RSAC, rather it is viewed as an easy to use supplemental tool to help improve risk understanding and support better informed decisions. The work was
Stochastic Modeling of Radioactive Material Releases
Energy Technology Data Exchange (ETDEWEB)
Andrus, Jason [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pope, Chad [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-09-01
Nonreactor nuclear facilities operated under the approval authority of the U.S. Department of Energy use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge or exceed dose evaluation guidelines. Unmitigated design basis events that sufficiently challenge dose evaluation guidelines or exceed the guidelines for members of the public or workers, merit selection of safety structures, systems, or components or other controls to prevent or mitigate the hazard. Idaho State University, in collaboration with Idaho National Laboratory, has developed a portable and simple to use software application called SODA (Stochastic Objective Decision-Aide) that stochastically calculates the radiation dose associated with hypothetical radiological material release scenarios. Rather than producing a point estimate of the dose, SODA produces a dose distribution result to allow a deeper understanding of the dose potential. SODA allows users to select the distribution type and parameter values for all of the input variables used to perform the dose calculation. SODA then randomly samples each distribution input variable and calculates the overall resulting dose distribution. In cases where an input variable distribution is unknown, a traditional single point value can be used. SODA was developed using the MATLAB coding framework. The software application has a graphical user input. SODA can be installed on both Windows and Mac computers and does not require MATLAB to function. SODA provides improved risk understanding leading to better informed decision making associated with establishing nuclear facility material-at-risk limits and safety structure, system, or component selection. It is important to note that SODA does not replace or compete with codes such as MACCS or RSAC, rather it is viewed as an easy to use supplemental tool to help improve risk understanding and support better informed decisions. The work was
Basic mechanisms for recognition and transport of synaptic cargos
M.A. Schlager (Max); C.C. Hoogenraad (Casper)
2009-01-01
textabstractSynaptic cargo trafficking is essential for synapse formation, function and plasticity. In order to transport synaptic cargo, such as synaptic vesicle precursors, mitochondria, neurotransmitter receptors and signaling proteins to their site of action, neurons make use of molecular motor
DEFF Research Database (Denmark)
Crone, C.; Hultborn, H.; Kiehn, O.
1988-01-01
1. During investigation of the tonic stretch reflex in the unanaesthetized decerebrate cat we observed that a short train of impulses in Ia afferents from the soleus muscle (or its synergists) may cause a prolonged activity in the soleus muscle as judged by EMG and tension recordings. This excita......1. During investigation of the tonic stretch reflex in the unanaesthetized decerebrate cat we observed that a short train of impulses in Ia afferents from the soleus muscle (or its synergists) may cause a prolonged activity in the soleus muscle as judged by EMG and tension recordings...
DEFF Research Database (Denmark)
McCarthy, E. V.; Wu, Y.; deCarvalho, T.
2011-01-01
Neuropeptide PDF (pigment-dispersing factor)-secreting large ventrolateral neurons (lLN(v)s) in the Drosophila brain regulate daily patterns of rest and arousal. These bilateral wake-promoting neurons are light responsive and integrate information from the circadian system, sleep circuits...
Evidence for Long-Timescale Patterns of Synaptic Inputs in CA1 of Awake Behaving Mice.
Kolb, Ilya; Talei Franzesi, Giovanni; Wang, Michael; Kodandaramaiah, Suhasa B; Forest, Craig R; Boyden, Edward S; Singer, Annabelle C
2018-02-14
Repeated sequences of neural activity are a pervasive feature of neural networks in vivo and in vitro In the hippocampus, sequential firing of many neurons over periods of 100-300 ms reoccurs during behavior and during periods of quiescence. However, it is not known whether the hippocampus produces longer sequences of activity or whether such sequences are restricted to specific network states. Furthermore, whether long repeated patterns of activity are transmitted to single cells downstream is unclear. To answer these questions, we recorded intracellularly from hippocampal CA1 of awake, behaving male mice to examine both subthreshold activity and spiking output in single neurons. In eight of nine recordings, we discovered long (900 ms) reoccurring subthreshold fluctuations or "repeats." Repeats generally were high-amplitude, nonoscillatory events reoccurring with 10 ms precision. Using statistical controls, we determined that repeats occurred more often than would be expected from unstructured network activity (e.g., by chance). Most spikes occurred during a repeat, and when a repeat contained a spike, the spike reoccurred with precision on the order of ≤20 ms, showing that long repeated patterns of subthreshold activity are strongly connected to spike output. Unexpectedly, we found that repeats occurred independently of classic hippocampal network states like theta oscillations or sharp-wave ripples. Together, these results reveal surprisingly long patterns of repeated activity in the hippocampal network that occur nonstochastically, are transmitted to single downstream neurons, and strongly shape their output. This suggests that the timescale of information transmission in the hippocampal network is much longer than previously thought. SIGNIFICANCE STATEMENT We found long (≥900 ms), repeated, subthreshold patterns of activity in CA1 of awake, behaving mice. These repeated patterns ("repeats") occurred more often than expected by chance and with 10 ms precision. Most spikes occurred within repeats and reoccurred with a precision on the order of 20 ms. Surprisingly, there was no correlation between repeat occurrence and classical network states such as theta oscillations and sharp-wave ripples. These results provide strong evidence that long patterns of activity are repeated and transmitted to downstream neurons, suggesting that the hippocampus can generate longer sequences of repeated activity than previously thought. Copyright © 2018 the authors 0270-6474/18/381822-14$15.00/0.
Essentials of stochastic processes
Durrett, Richard
2016-01-01
Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader’s understanding. Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatm...
Fractional Stochastic Field Theory
Honkonen, Juha
2018-02-01
Models describing evolution of physical, chemical, biological, social and financial processes are often formulated as differential equations with the understanding that they are large-scale equations for averages of quantities describing intrinsically random processes. Explicit account of randomness may lead to significant changes in the asymptotic behaviour (anomalous scaling) in such models especially in low spatial dimensions, which in many cases may be captured with the use of the renormalization group. Anomalous scaling and memory effects may also be introduced with the use of fractional derivatives and fractional noise. Construction of renormalized stochastic field theory with fractional derivatives and fractional noise in the underlying stochastic differential equations and master equations and the interplay between fluctuation-induced and built-in anomalous scaling behaviour is reviewed and discussed.
Stochastic ice stream dynamics.
Mantelli, Elisa; Bertagni, Matteo Bernard; Ridolfi, Luca
2016-08-09
Ice streams are narrow corridors of fast-flowing ice that constitute the arterial drainage network of ice sheets. Therefore, changes in ice stream flow are key to understanding paleoclimate, sea level changes, and rapid disintegration of ice sheets during deglaciation. The dynamics of ice flow are tightly coupled to the climate system through atmospheric temperature and snow recharge, which are known exhibit stochastic variability. Here we focus on the interplay between stochastic climate forcing and ice stream temporal dynamics. Our work demonstrates that realistic climate fluctuations are able to (i) induce the coexistence of dynamic behaviors that would be incompatible in a purely deterministic system and (ii) drive ice stream flow away from the regime expected in a steady climate. We conclude that environmental noise appears to be crucial to interpreting the past behavior of ice sheets, as well as to predicting their future evolution.
Dynamic stochastic optimization
Ermoliev, Yuri; Pflug, Georg
2004-01-01
Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu tions. Objective an...
Stochastic calculus and applications
Cohen, Samuel N
2015-01-01
Completely revised and greatly expanded, the new edition of this text takes readers who have been exposed to only basic courses in analysis through the modern general theory of random processes and stochastic integrals as used by systems theorists, electronic engineers and, more recently, those working in quantitative and mathematical finance. Building upon the original release of this title, this text will be of great interest to research mathematicians and graduate students working in those fields, as well as quants in the finance industry. New features of this edition include: End of chapter exercises; New chapters on basic measure theory and Backward SDEs; Reworked proofs, examples and explanatory material; Increased focus on motivating the mathematics; Extensive topical index. "Such a self-contained and complete exposition of stochastic calculus and applications fills an existing gap in the literature. The book can be recommended for first-year graduate studies. It will be useful for all who intend to wo...
Multistage stochastic optimization
Pflug, Georg Ch
2014-01-01
Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book
Directory of Open Access Journals (Sweden)
William Margulies
2004-11-01
Full Text Available In this paper, we study a specific stochastic differential equation depending on a parameter and obtain a representation of its probability density function in terms of Jacobi Functions. The equation arose in a control problem with a quadratic performance criteria. The quadratic performance is used to eliminate the control in the standard Hamilton-Jacobi variational technique. The resulting stochastic differential equation has a noise amplitude which complicates the solution. We then solve Kolmogorov's partial differential equation for the probability density function by using Jacobi Functions. A particular value of the parameter makes the solution a Martingale and in this case we prove that the solution goes to zero almost surely as time tends to infinity.
Stochastic porous media equations
Barbu, Viorel; Röckner, Michael
2016-01-01
Focusing on stochastic porous media equations, this book places an emphasis on existence theorems, asymptotic behavior and ergodic properties of the associated transition semigroup. Stochastic perturbations of the porous media equation have reviously been considered by physicists, but rigorous mathematical existence results have only recently been found. The porous media equation models a number of different physical phenomena, including the flow of an ideal gas and the diffusion of a compressible fluid through porous media, and also thermal propagation in plasma and plasma radiation. Another important application is to a model of the standard self-organized criticality process, called the "sand-pile model" or the "Bak-Tang-Wiesenfeld model". The book will be of interest to PhD students and researchers in mathematics, physics and biology.
Some illustrations of stochasticity
International Nuclear Information System (INIS)
Laslett, L.J.
1977-01-01
A complex, and apparently stochastic, character frequently can be seen to occur in the solutions to simple Hamiltonian problems. Such behavior is of interest, and potentially of importance, to designers of particle accelerators--as well as to workers in other fields of physics and related disciplines. Even a slow development of disorder in the motion of particles in a circular accelerator or storage ring could be troublesome, because a practical design requires the beam particles to remain confined in an orderly manner within a narrow beam tube for literally tens of billions of revolutions. The material presented is primarily the result of computer calculations made to investigate the occurrence of ''stochasticity,'' and is organized in a manner similar to that adopted for presentation at a 1974 accelerator conference
Stochastic Matrix Factorization
Adams, Christopher
2016-01-01
This paper considers a restriction to non-negative matrix factorization in which at least one matrix factor is stochastic. That is, the elements of the matrix factors are non-negative and the columns of one matrix factor sum to 1. This restriction includes topic models, a popular method for analyzing unstructured data. It also includes a method for storing and finding pictures. The paper presents necessary and sufficient conditions on the observed data such that the factorization is unique. I...
Stochastic conditional intensity processes
DEFF Research Database (Denmark)
Bauwens, Luc; Hautsch, Nikolaus
2006-01-01
In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell’s (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the propos...... for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process...
Research in Stochastic Processes.
1984-10-01
description, from the stochastic point of view, of the celebrated Hodgkin - Huxley equations. Ph.D. students under Gopinath Kallianpur Victor Perez-Abreu...optimal filter in the general white noise model is shown to be a Markov process. More precisely, it is shown that Ft( Y ) and rt( y ) - the normalized...and unnormalized conditional distribution (where y is the white noise observation) regards as measure-valued processes either on the quasi- P
Holmes-Cerfon, Miranda
2016-11-01
We study a model of rolling particles subject to stochastic fluctuations, which may be relevant in systems of nano- or microscale particles where rolling is an approximation for strong static friction. We consider the simplest possible nontrivial system: a linear polymer of three disks constrained to remain in contact and immersed in an equilibrium heat bath so the internal angle of the polymer changes due to stochastic fluctuations. We compare two cases: one where the disks can slide relative to each other and the other where they are constrained to roll, like gears. Starting from the Langevin equations with arbitrary linear velocity constraints, we use formal homogenization theory to derive the overdamped equations that describe the process in configuration space only. The resulting dynamics have the formal structure of a Brownian motion on a Riemannian or sub-Riemannian manifold, depending on if the velocity constraints are holonomic or nonholonomic. We use this to compute the trimer's equilibrium distribution with and without the rolling constraints. Surprisingly, the two distributions are different. We suggest two possible interpretations of this result: either (i) dry friction (or other dissipative, nonequilibrium forces) changes basic thermodynamic quantities like the free energy of a system, a statement that could be tested experimentally, or (ii) as a lesson in modeling rolling or friction more generally as a velocity constraint when stochastic fluctuations are present. In the latter case, we speculate there could be a "roughness" entropy whose inclusion as an effective force could compensate the constraint and preserve classical Boltzmann statistics. Regardless of the interpretation, our calculation shows the word "rolling" must be used with care when stochastic fluctuations are present.
Identifiability in stochastic models
1992-01-01
The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of ""characterization problems"" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.
Stochastic quantization of instantons
International Nuclear Information System (INIS)
Grandati, Y.; Berard, A.; Grange, P.
1996-01-01
The method of Parisi and Wu to quantize classical fields is applied to instanton solutions var-phi I of euclidian non-linear theory in one dimension. The solution var-phi var-epsilon of the corresponding Langevin equation is built through a singular perturbative expansion in var-epsilon=h 1/2 in the frame of the center of the mass of the instanton, where the difference var-phi var-epsilon -var-phi I carries only fluctuations of the instanton form. The relevance of the method is shown for the stochastic K dV equation with uniform noise in space: the exact solution usually obtained by the inverse scattering method is retrieved easily by the singular expansion. A general diagrammatic representation of the solution is then established which makes a thorough use of regrouping properties of stochastic diagrams derived in scalar field theory. Averaging over the noise and in the limit of infinite stochastic time, the authors obtain explicit expressions for the first two orders in var-epsilon of the pertrubed instanton of its Green function. Specializing to the Sine-Gordon and var-phi 4 models, the first anaharmonic correction is obtained analytically. The calculation is carried to second order for the var-phi 4 model, showing good convergence. 21 refs., 5 fig
Stochasticity Modeling in Memristors
Naous, Rawan
2015-10-26
Diverse models have been proposed over the past years to explain the exhibiting behavior of memristors, the fourth fundamental circuit element. The models varied in complexity ranging from a description of physical mechanisms to a more generalized mathematical modeling. Nonetheless, stochasticity, a widespread observed phenomenon, has been immensely overlooked from the modeling perspective. This inherent variability within the operation of the memristor is a vital feature for the integration of this nonlinear device into the stochastic electronics realm of study. In this paper, experimentally observed innate stochasticity is modeled in a circuit compatible format. The model proposed is generic and could be incorporated into variants of threshold-based memristor models in which apparent variations in the output hysteresis convey the switching threshold shift. Further application as a noise injection alternative paves the way for novel approaches in the fields of neuromorphic engineering circuits design. On the other hand, extra caution needs to be paid to variability intolerant digital designs based on non-deterministic memristor logic.
Stochastic modeling of thermal fatigue crack growth
Radu, Vasile
2015-01-01
The book describes a systematic stochastic modeling approach for assessing thermal-fatigue crack-growth in mixing tees, based on the power spectral density of temperature fluctuation at the inner pipe surface. It shows the development of a frequency-temperature response function in the framework of single-input, single-output (SISO) methodology from random noise/signal theory under sinusoidal input. The frequency response of stress intensity factor (SIF) is obtained by a polynomial fitting procedure of thermal stress profiles at various instants of time. The method, which takes into account the variability of material properties, and has been implemented in a real-world application, estimates the probabilities of failure by considering a limit state function and Monte Carlo analysis, which are based on the proposed stochastic model. Written in a comprehensive and accessible style, this book presents a new and effective method for assessing thermal fatigue crack, and it is intended as a concise and practice-or...
Differential regulation of synaptic AP-2/clathrin vesicle uncoating in synaptic plasticity.
Candiello, Ermes; Mishra, Ratnakar; Schmidt, Bernhard; Jahn, Olaf; Schu, Peter
2017-11-17
AP-1/σ1B-deficiency causes X-linked intellectual disability. AP-1/σ1B -/- mice have impaired synaptic vesicle recycling, fewer synaptic vesicles and enhanced endosome maturation mediated by AP-1/σ1A. Despite defects in synaptic vesicle recycling synapses contain two times more endocytic AP-2 clathrin-coated vesicles. We demonstrate increased formation of two classes of AP-2/clathrin coated vesicles. One which uncoats readily and a second with a stabilised clathrin coat. Coat stabilisation is mediated by three molecular mechanisms: reduced recruitment of Hsc70 and synaptojanin1 and enhanced μ2/AP-2 phosphorylation and activation. Stabilised AP-2 vesicles are enriched in the structural active zone proteins Git1 and stonin2 and synapses contain more Git1. Endocytosis of the synaptic vesicle exocytosis regulating Munc13 isoforms are differentially effected. Regulation of synaptic protein endocytosis by the differential stability of AP-2/clathrin coats is a novel molecular mechanism of synaptic plasticity.
Wang, Haibin; Kohno, Tatsuro; Amaya, Fumimasa; Brenner, Gary J; Ito, Nobuko; Allchorne, Andrew; Ji, Ru-Rong; Woolf, Clifford J
2005-08-31
Bradykinin, an inflammatory mediator, sensitizes nociceptor peripheral terminals reducing pain threshold. We now show that the B2 kinin receptor is expressed in rat dorsal horn neurons and that bradykinin, a B2-specific agonist, augments AMPA- and NMDA-induced, and primary afferent-evoked EPSCs, and increases the frequency and amplitude of miniature EPSCs in superficial dorsal horn neurons in vitro. Administration of bradykinin to the spinal cord in vivo produces, moreover, an NMDA-dependent hyperalgesia. We also demonstrate that nociceptive inputs result in the production of bradykinin in the spinal cord and that an intrathecal B2-selective antagonist suppresses behavioral manifestations of central sensitization, an activity-dependent increase in glutamatergic synaptic efficacy. Primary afferent-evoked central sensitization is, in addition, reduced in B2 receptor knock-out mice. We conclude that bradykinin is released in the spinal cord in response to nociceptor inputs and acts as a synaptic neuromodulator, potentiating glutamatergic synaptic transmission to produce pain hypersensitivity.
Stochastic and non-stochastic effects - a conceptual analysis
International Nuclear Information System (INIS)
Karhausen, L.R.
1980-01-01
The attempt to divide radiation effects into stochastic and non-stochastic effects is discussed. It is argued that radiation or toxicological effects are contingently related to radiation or chemical exposure. Biological effects in general can be described by general laws but these laws never represent a necessary connection. Actually stochastic effects express contingent, or empirical, connections while non-stochastic effects represent semantic and non-factual connections. These two expressions stem from two different levels of discourse. The consequence of this analysis for radiation biology and radiation protection is discussed. (author)
Slow GABAA mediated synaptic transmission in rat visual cortex
Directory of Open Access Journals (Sweden)
Sceniak Michael P
2008-01-01
Full Text Available Abstract Background Previous reports of inhibition in the neocortex suggest that inhibition is mediated predominantly through GABAA receptors exhibiting fast kinetics. Within the hippocampus, it has been shown that GABAA responses can take the form of either fast or slow response kinetics. Our findings indicate, for the first time, that the neocortex displays synaptic responses with slow GABAA receptor mediated inhibitory postsynaptic currents (IPSCs. These IPSCs are kinetically and pharmacologically similar to responses found in the hippocampus, although the anatomical specificity of evoked responses is unique from hippocampus. Spontaneous slow GABAA IPSCs were recorded from both pyramidal and inhibitory neurons in rat visual cortex. Results GABAA slow IPSCs were significantly different from fast responses with respect to rise times and decay time constants, but not amplitudes. Spontaneously occurring GABAA slow IPSCs were nearly 100 times less frequent than fast sIPSCs and both were completely abolished by the chloride channel blocker, picrotoxin. The GABAA subunit-specific antagonist, furosemide, depressed spontaneous and evoked GABAA fast IPSCs, but not slow GABAA-mediated IPSCs. Anatomical specificity was evident using minimal stimulation: IPSCs with slow kinetics were evoked predominantly through stimulation of layer 1/2 apical dendritic zones of layer 4 pyramidal neurons and across their basal dendrites, while GABAA fast IPSCs were evoked through stimulation throughout the dendritic arborization. Many evoked IPSCs were also composed of a combination of fast and slow IPSC components. Conclusion GABAA slow IPSCs displayed durations that were approximately 4 fold longer than typical GABAA fast IPSCs, but shorter than GABAB-mediated inhibition. The anatomical and pharmacological specificity of evoked slow IPSCs suggests a unique origin of synaptic input. Incorporating GABAA slow IPSCs into computational models of cortical function will help
Lefort, Sandrine; Petersen, Carl C H
2017-07-01
Neurons process information through spatiotemporal integration of synaptic input. Synaptic transmission between any given pair of neurons is typically a dynamic process with presynaptic action potentials (APs) evoking depressing or facilitating postsynaptic potentials when presynaptic APs occur within hundreds of milliseconds of each other. In order to understand neocortical function, it is therefore important to investigate such short-term synaptic plasticity at synapses between different types of neocortical neurons. Here, we examine short-term synaptic dynamics between excitatory neurons in different layers of the mouse C2 barrel column through in vitro whole-cell recordings. We find layer-dependent short-term plasticity, with depression being dominant at many synaptic connections. Interestingly, however, presynaptic layer 2 neurons predominantly give rise to facilitating excitatory synaptic output at short interspike intervals of 10 and 30 ms. Previous studies have found prominent burst firing of excitatory neurons in supragranular layers of awake mice. The facilitation we observed in the synaptic output of layer 2 may, therefore, be functionally relevant, possibly serving to enhance the postsynaptic impact of burst firing. © The Author 2017. Published by Oxford University Press.
A retrodictive stochastic simulation algorithm
International Nuclear Information System (INIS)
Vaughan, T.G.; Drummond, P.D.; Drummond, A.J.
2010-01-01
In this paper we describe a simple method for inferring the initial states of systems evolving stochastically according to master equations, given knowledge of the final states. This is achieved through the use of a retrodictive stochastic simulation algorithm which complements the usual predictive stochastic simulation approach. We demonstrate the utility of this new algorithm by applying it to example problems, including the derivation of likely ancestral states of a gene sequence given a Markovian model of genetic mutation.
Aircraft Evaluation Using Stochastic Duels
2017-09-01
LEFT BLANK xix EXECUTIVE SUMMARY In this thesis, we present a modeling paradigm for evaluating fighter aircraft using stochastic duels... EVALUATION USING STOCHASTIC DUELS by Jason W.C. Gay September 2017 Thesis Advisor: Moshe Kress Co-Advisor: Michael Atkinson Second Reader...DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE AIRCRAFT EVALUATION USING STOCHASTIC DUELS 6. AUTHOR(S) Jason W.C. Gay 7. PERFORMING
Assessment of Stochastic Capacity Consumption in Railway Networks
DEFF Research Database (Denmark)
Jensen, Lars Wittrup; Landex, Alex; Nielsen, Otto Anker
2015-01-01
The railway industry continuously strive to reduce cost and utilise resources optimally. Thus, there is a demand for tools that are able to fast and efficiently provide decision-makers with solutions that can help them achieve their goals. In strategic planning of capacity, this translates...... in networks where a timetable is not needed as input. We account for robustness using a stochastic simulation of delays to obtain the stochastic capacity consumption in a network. The model is used on a case network where four different infrastructure scenarios are considered and both deterministic...... and stochastic capacity consumption results are obtained efficiently. The case study show that the results of capacity analysis depends on the size of the network considered. Furthermore, we find that the capacity gain in the case scenarios are greater when delays are considered compared to a deterministic...
Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.
2017-08-01
The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.
Plasticity of GABA transporters: an unconventional route to shape inhibitory synaptic transmission
Directory of Open Access Journals (Sweden)
Annalisa eScimemi
2014-05-01
Full Text Available The brain relies on GABAergic neurons to control the ongoing activity of neuronal networks. GABAergic neurons control the firing pattern of excitatory cells, the temporal structure of membrane potential oscillations and the time window for integration of synaptic inputs. These actions require a fine control of the timing of GABA receptor activation which, in turn, depends on the precise timing of GABA release from pre-synaptic terminals and GABA clearance from the extracellular space. Extracellular GABA is not subject to enzymatic breakdown, and its clearance relies entirely on diffusion and uptake by specific transporters. In contrast to glutamate transporters, GABA transporters are abundantly expressed in neuronal pre-synaptic terminals. GABA transporters move laterally within the plasma membrane and are continuously trafficked to/from intracellular compartments. It is hypothesized that due to their proximity to GABA release sites, changes in the concentration and lateral mobility of GABA transporters may have a significant effect on the time course of the GABA concentration profile in and out of the synaptic cleft. To date, this hypothesis remains to be tested. Here we use 3D Monte Carlo reaction-diffusion simulations to analyze how changes in the density of expression and lateral mobility of GABA transporters in the cell membrane affect the extracellular GABA concentration profile and the activation of GABA receptors. Our results indicate that these manipulations mainly alter the GABA concentration profile away from the synaptic cleft. These findings provide novel insights into how the ability of GABA transporters to undergo plastic changes may alter the strength of GABAergic signals and the activity of neuronal networks in the brain.
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.
Directory of Open Access Journals (Sweden)
John R W Menzies
Full Text Available BACKGROUND: Vestibulo-ocular reflex (VOR gain adaptation, a longstanding experimental model of cerebellar learning, utilizes sites of plasticity in both cerebellar cortex and brainstem. However, the mechanisms by which the activity of cortical Purkinje cells may guide synaptic plasticity in brainstem vestibular neurons are unclear. Theoretical analyses indicate that vestibular plasticity should depend upon the correlation between Purkinje cell and vestibular afferent inputs, so that, in gain-down learning for example, increased cortical activity should induce long-term depression (LTD at vestibular synapses. METHODOLOGY/PRINCIPAL FINDINGS: Here we expressed this correlational learning rule in its simplest form, as an anti-Hebbian, heterosynaptic spike-timing dependent plasticity interaction between excitatory (vestibular and inhibitory (floccular inputs converging on medial vestibular nucleus (MVN neurons (input-spike-timing dependent plasticity, iSTDP. To test this rule, we stimulated vestibular afferents to evoke EPSCs in rat MVN neurons in vitro. Control EPSC recordings were followed by an induction protocol where membrane hyperpolarizing pulses, mimicking IPSPs evoked by flocculus inputs, were paired with single vestibular nerve stimuli. A robust LTD developed at vestibular synapses when the afferent EPSPs coincided with membrane hyperpolarization, while EPSPs occurring before or after the simulated IPSPs induced no lasting change. Furthermore, the iSTDP rule also successfully predicted the effects of a complex protocol using EPSP trains designed to mimic classical conditioning. CONCLUSIONS: These results, in strong support of theoretical predictions, suggest that the cerebellum alters the strength of vestibular synapses on MVN neurons through hetero-synaptic, anti-Hebbian iSTDP. Since the iSTDP rule does not depend on post-synaptic firing, it suggests a possible mechanism for VOR adaptation without compromising gaze-holding and VOR
Glutamate receptors in glia: new cells, new inputs and new functions.
Gallo, V; Ghiani, C A
2000-07-01
Functional glutamate receptors are expressed on the majority of glial cell types in the developing and mature brain. Although glutamate receptors on glia are activated by glutamate released from neurons, their physiological role remains largely unknown. Potential roles for these receptors in glia include regulation of proliferation and differentiation, and modulation of synaptic efficacy. Recent anatomical and functional evidence indicates that glutamate receptors on immature glia are activated through direct synaptic inputs. Therefore, glutamate and its receptors appear to be involved in a continuous crosstalk between neurons and glia during development and also in the mature brain.
Carbon Nanotube Synaptic Transistor Network for Pattern Recognition.
Kim, Sungho; Yoon, Jinsu; Kim, Hee-Dong; Choi, Sung-Jin
2015-11-18
Inspired by the human brain, a neuromorphic system combining complementary metal-oxide semiconductor (CMOS) and adjustable synaptic devices may offer new computing paradigms by enabling massive neural-network parallelism. In particular, synaptic devices, which are capable of emulating the functions of biological synapses, are used as the essential building blocks for an information storage and processing system. However, previous synaptic devices based on two-terminal resistive devices remain challenging because of their variability and specific physical mechanisms of resistance change, which lead to a bottleneck in the implementation of a high-density synaptic device network. Here we report that a three-terminal synaptic transistor based on carbon nanotubes can provide reliable synaptic functions that encode relative timing and regulate weight change. In addition, using system-level simulations, the developed synaptic transistor network associated with CMOS circuits can perform unsupervised learning for pattern recognition using a simplified spike-timing-dependent plasticity scheme.
Tripartite synapses: astrocytes process and control synaptic information.
Perea, Gertrudis; Navarrete, Marta; Araque, Alfonso
2009-08-01
The term 'tripartite synapse' refers to a concept in synaptic physiology based on the demonstration of the existence of bidirectional communication between astrocytes and neurons. Consistent with this concept, in addition to the classic 'bipartite' information flow between the pre- and postsynaptic neurons, astrocytes exchange information with the synaptic neuronal elements, responding to synaptic activity and, in turn, regulating synaptic transmission. Because recent evidence has demonstrated that astrocytes integrate and process synaptic information and control synaptic transmission and plasticity, astrocytes, being active partners in synaptic function, are cellular elements involved in the processing, transfer and storage of information by the nervous system. Consequently, in contrast to the classically accepted paradigm that brain function results exclusively from neuronal activity, there is an emerging view, which we review herein, in which brain function actually arises from the coordinated activity of a network comprising both neurons and glia.
Directory of Open Access Journals (Sweden)
Jong-Cheol eRah
2013-11-01
Full Text Available The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT, a high-resolution optical microscopy method, to examine thalamocortical (TC input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale AT to measure TC synapse distribution on L5 pyramdial neurons in a 1.00 x 0.83 x 0.21 mm^3 volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased towards certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5-15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.
Distinct roles of synaptic and extrasynaptic GABAA receptors in striatal inhibition dynamics
Directory of Open Access Journals (Sweden)
Ruixi eLuo
2013-11-01
Full Text Available Striatonigral and striatopallidal projecting medium spiny neurons (MSNs express dopamine D1 (D1+ and D2 receptors (D2+, respectively. Both classes receive extensive GABAergic input via expression of synaptic, perisynaptic and extrasynaptic GABAA receptors. The activation patterns of different presynaptic GABAergic neurons produce transient and sustained GABAA receptor-mediated conductance that fulfill distinct physiological roles. We performed single and dual whole cell recordings from striatal neurons in mice expressing fluorescent proteins in interneurons and MSNs. We report specific inhibitory dynamics produced by distinct activation patterns of presynaptic GABAergic neurons as source of synaptic, perisynaptic and extrasynaptic inhibition. Synaptic GABAA receptors in MSNs contain the α2, γ2 and a β subunit. In addition, there is evidence for the developmental increase of the α1 subunit that contributes to faster inhibitory postsynaptic current (IPSC. Tonic GABAergic currents in MSNs from adult mice are carried by extrasynaptic receptors containing the α4 and δ subunit, while in younger mice this current is mediated by receptors that contain the α5 subunit. Both forms of tonic currents are differentially expressed in D1+ and D2+ MSNs. This study extends these findings by relating presynaptic activation with pharmacological analysis of inhibitory conductance in mice where the β3 subunit is conditionally removed in fluorescently labeled D2+ MSNs and in mice with global deletion of the δ subunit. Our results show that responses to low doses of gaboxadol (2μM, a GABAA receptor agonist with preference to δ subunit, are abolished in the δ but not the β3 subunit knock out mice. This suggests that the β3 subunit is not a component of the adult extrasynaptic receptor pool, in contrast to what has been shown for tonic current in young mice. Deletion of the β3 subunit from D2+ MSNs however, removed slow spontaneous IPSCs, implicating its
Lo, Liching; Anderson, David J.
2012-01-01
SUMMARY Neurotropic viruses that conditionally infect or replicate in molecularly defined neuronal subpopulations, and then spread trans-synaptically, are powerful tools for mapping neural pathways. Genetically targetable retrograde trans-synaptic tracer viruses are available to map the inputs to specific neuronal subpopulations, but an analogous tool for mapping synaptic outputs is not yet available. Here we describe a Cre recombinase-dependent, anterograde trans-neuronal tracer, based on the H129 strain of herpes simplex virus (HSV). Application of this virus to transgenic or knock-in mice expressing Cre in peripheral neurons of the olfactory epithelium or the retina reveals widespread, polysynaptic labeling of higher-order neurons in the olfactory and visual systems, respectively. Polysynaptic pathways were also labeled from cerebellar Purkinje cells. In each system, the pattern of labeling was consistent with classical circuit-tracing studies, restricted to neurons and anterograde-specific. These data provide proof-of-principle for a conditional, non-diluting anterograde trans-synaptic tracer for mapping synaptic outputs from genetically marked neuronal subpopulations. PMID:22196330
2016-01-01
Abstract The fluorescent dyes, Alexa Fluor 488 and 594 are commonly used to visualize dendritic structures and the localization of synapses, both of which are critical for the spatial and temporal integration of synaptic inputs. However, the effect of the dyes on synaptic transmission is not known. Here we investigated whether Alexa Fluor dyes alter the properties of synaptic currents mediated by two subtypes of AMPA receptors (AMPARs) at cerebellar stellate cell synapses. In naive mice, GluA2-lacking AMPAR-mediated synaptic currents displayed an inwardly rectifying current–voltage (I–V) relationship due to blockade by cytoplasmic spermine at depolarized potentials. We found that the inclusion of 100 µm Alexa Fluor dye, but not 10 µm, in the pipette solution led to a gradual increase in the amplitude of EPSCs at +40 mV and a change in the I–V relationship from inwardly rectifying to more linear. In mice exposed to an acute stress, AMPARs switched to GluA2-containing receptors, and 100 µm Alexa Fluor 594 did not alter the I–V relationship of synaptic currents. Therefore, a high concentration of Alexa Fluor dye changed the I–V relationship of EPSCs at GluA2-lacking AMPAR synapses. PMID:27280156
Maroteaux, Matthieu; Liu, Siqiong June
2016-01-01
The fluorescent dyes, Alexa Fluor 488 and 594 are commonly used to visualize dendritic structures and the localization of synapses, both of which are critical for the spatial and temporal integration of synaptic inputs. However, the effect of the dyes on synaptic transmission is not known. Here we investigated whether Alexa Fluor dyes alter the properties of synaptic currents mediated by two subtypes of AMPA receptors (AMPARs) at cerebellar stellate cell synapses. In naive mice, GluA2-lacking AMPAR-mediated synaptic currents displayed an inwardly rectifying current-voltage (I-V) relationship due to blockade by cytoplasmic spermine at depolarized potentials. We found that the inclusion of 100 µm Alexa Fluor dye, but not 10 µm, in the pipette solution led to a gradual increase in the amplitude of EPSCs at +40 mV and a change in the I-V relationship from inwardly rectifying to more linear. In mice exposed to an acute stress, AMPARs switched to GluA2-containing receptors, and 100 µm Alexa Fluor 594 did not alter the I-V relationship of synaptic currents. Therefore, a high concentration of Alexa Fluor dye changed the I-V relationship of EPSCs at GluA2-lacking AMPAR synapses.
Directory of Open Access Journals (Sweden)
Sarah X. Luo
2016-12-01
Full Text Available Neural circuits involving midbrain dopaminergic (DA neurons regulate reward and goal-directed behaviors. Although local GABAergic input is known to modulate DA circuits, the mechanism that controls excitatory/inhibitory synaptic balance in DA neurons remains unclear. Here, we show that DA neurons use autocrine transforming growth factor β (TGF-β signaling to promote the growth of axons and dendrites. Surprisingly, removing TGF-β type II receptor in DA neurons also disrupts the balance in TGF-β1 expression in DA neurons and neighboring GABAergic neurons, which increases inhibitory input, reduces excitatory synaptic input, and alters phasic firing patterns in DA neurons. Mice lacking TGF-β signaling in DA neurons are hyperactive and exhibit inflexibility in relinquishing learned behaviors and re-establishing new stimulus-reward associations. These results support a role for TGF-β in regulating the delicate balance of excitatory/inhibitory synaptic input in local microcircuits involving DA and GABAergic neurons and its potential contributions to neuropsychiatric disorders.
Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile
2014-09-01
The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.
Energy Technology Data Exchange (ETDEWEB)
Yu, Haitao; Guo, Xinmeng; Wang, Jiang, E-mail: jiangwang@tju.edu.cn; Deng, Bin; Wei, Xile [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)
2014-09-01
The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.
Energy Technology Data Exchange (ETDEWEB)
Hardwick, Robert J.; Vennin, Vincent; Wands, David [Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX (United Kingdom); Byrnes, Christian T.; Torrado, Jesús, E-mail: robert.hardwick@port.ac.uk, E-mail: vincent.vennin@port.ac.uk, E-mail: c.byrnes@sussex.ac.uk, E-mail: jesus.torrado@sussex.ac.uk, E-mail: david.wands@port.ac.uk [Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH (United Kingdom)
2017-10-01
We study the stochastic distribution of spectator fields predicted in different slow-roll inflation backgrounds. Spectator fields have a negligible energy density during inflation but may play an important dynamical role later, even giving rise to primordial density perturbations within our observational horizon today. During de-Sitter expansion there is an equilibrium solution for the spectator field which is often used to estimate the stochastic distribution during slow-roll inflation. However slow roll only requires that the Hubble rate varies slowly compared to the Hubble time, while the time taken for the stochastic distribution to evolve to the de-Sitter equilibrium solution can be much longer than a Hubble time. We study both chaotic (monomial) and plateau inflaton potentials, with quadratic, quartic and axionic spectator fields. We give an adiabaticity condition for the spectator field distribution to relax to the de-Sitter equilibrium, and find that the de-Sitter approximation is never a reliable estimate for the typical distribution at the end of inflation for a quadratic spectator during monomial inflation. The existence of an adiabatic regime at early times can erase the dependence on initial conditions of the final distribution of field values. In these cases, spectator fields acquire sub-Planckian expectation values. Otherwise spectator fields may acquire much larger field displacements than suggested by the de-Sitter equilibrium solution. We quantify the information about initial conditions that can be obtained from the final field distribution. Our results may have important consequences for the viability of spectator models for the origin of structure, such as the simplest curvaton models.
Portfolio Optimization with Stochastic Dividends and Stochastic Volatility
Varga, Katherine Yvonne
2015-01-01
We consider an optimal investment-consumption portfolio optimization model in which an investor receives stochastic dividends. As a first problem, we allow the drift of stock price to be a bounded function. Next, we consider a stochastic volatility model. In each problem, we use the dynamic programming method to derive the Hamilton-Jacobi-Bellman…
Fang, Lian; Fu, YaoYao; Zhang, Tian-Yu
2016-06-01
Lesion-induced cochlear damage can result in synaptic outgrowth in the ventral cochlear nucleus (VCN). Tinnitus may be associated with the synaptic outgrowth and hyperactivity in the VCN. However, it remains unclear how hearing loss triggers structural synaptic modifications in the VCN of rats subjected to salicylate-induced tinnitus. To address this issue, we evaluated tinnitus-like behavior in rats after salicylate treatment and compared the amplitude of the distortion product evoked otoacoustic emission (DPOAE) and auditory brainstem response (ABR) between control and treated rats. Moreover, we observed the changes in the synaptic ultrastructure and in the expression levels of growth-associated protein (GAP-43), brain-derived neurotrophic factor (BDNF), the microglial marker Iba-1 and glial fibrillary acidic protein (GFAP) in the VCN. After salicylate treatment (300 mg/kg/day for 4 and 8 days), analysis of the gap prepulse inhibition of the acoustic startle showed that the rats were experiencing tinnitus. The changes in the DPOAE and ABR amplitude indicated an improvement in cochlear sensitivity and a reduction in auditory input following salicylate treatment. The treated rats displayed more synaptic vesicles and longer postsynaptic density in the VCN than the control rats. We observed that the GAP-43 expression, predominantly from medial olivocochlear (MOC) neurons, was significantly up-regulated, and that BDNF- and Iba-1-immunoreactive cells were persistently decreased after salicylate administration. Furthermore, GFAP-immunoreactive astrocytes, which is associated with synaptic regrowth, was significantly increased in the treated groups. Our study revealed that reduced auditory nerve activity triggers synaptic outgrowth and hyperactivity in the VCN via a MOC neural feedback circuit. Structural synaptic modifications may be a reflexive process that compensates for the reduced auditory input after salicylate administration. However, massive increases in
Ultrafast Synaptic Events in a Chalcogenide Memristor
Li, Yi; Zhong, Yingpeng; Xu, Lei; Zhang, Jinjian; Xu, Xiaohua; Sun, Huajun; Miao, Xiangshui
2013-04-01
Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 105 times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.
Molecular Signatures Underlying Synaptic Vesicle Cargo Retrieval
Mori, Yasunori; Takamori, Shigeo
2018-01-01
Efficient retrieval of the synaptic vesicle (SV) membrane from the presynaptic plasma membrane, a process called endocytosis, is crucial for the fidelity of neurotransmission, particularly during sustained neural activity. Although multiple modes of endocytosis have been identified, it is clear that the efficient retrieval of the major SV cargos into newly formed SVs during any of these modes is fundamental for synaptic transmission. It is currently believed that SVs are eventually reformed via a clathrin-dependent pathway. Various adaptor proteins recognize SV cargos and link them to clathrin, ensuring the efficient retrieval of the cargos into newly formed SVs. Here, we summarize our current knowledge of the molecular signatures within individual SV cargos that underlie efficient retrieval into SV membranes, as well as discuss possible contributions of the mechanisms under physiological conditions. PMID:29379416
Synaptic devices based on purely electronic memristors
Energy Technology Data Exchange (ETDEWEB)
Pan, Ruobing [Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201 (China); Institute of Materials Science, School of Materials Science and Engineering, Shanghai University, Shanghai 200072 (China); Li, Jun; Zhuge, Fei, E-mail: zhugefei@nimte.ac.cn, E-mail: h-cao@nimte.ac.cn; Zhu, Liqiang; Liang, Lingyan; Zhang, Hongliang; Gao, Junhua; Cao, Hongtao, E-mail: zhugefei@nimte.ac.cn, E-mail: h-cao@nimte.ac.cn; Fu, Bing; Li, Kang [Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201 (China)
2016-01-04
Memristive devices have been widely employed to emulate biological synaptic behavior. In these cases, the memristive switching generally originates from electrical field induced ion migration or Joule heating induced phase change. In this letter, the Ti/ZnO/Pt structure was found to show memristive switching ascribed to a carrier trapping/detrapping of the trap sites (e.g., oxygen vacancies or zinc interstitials) in ZnO. The carrier trapping/detrapping level can be controllably adjusted by regulating the current compliance level or voltage amplitude. Multi-level conductance states can, therefore, be realized in such memristive device. The spike-timing-dependent plasticity, an important Hebbian learning rule, has been implemented in this type of synaptic device. Compared with filamentary-type memristive devices, purely electronic memristors have potential to reduce their energy consumption and work more stably and reliably, since no structural distortion occurs.
Research in Stochastic Processes.
1982-10-31
Medicine 70, (1981), 960-970. G. Kallianpur, Same ramifications of Wiener’s ideas on nonlinear prediction, in Norbert Wiener , Collected Works Volume III... Norbert Wiener " at the Meetings of the American Mathematical Society at the University of Maryland, College Park, Maryland (October 1982). (8) The Layman...joint error M5E(SP) is computed when the input is Gaussian. For a Wiener input, the joint distribution of the sanple point and predictive errors is
Approximations of Stochastic Partial Differential Equations
Di Nunno, Giulia; Zhang, Tusheng
2014-01-01
In this paper we show that solutions of stochastic partial differ- ential equations driven by Brownian motion can be approximated by stochastic partial differential equations forced by pure jump noise/random kicks. Applications to stochastic Burgers equations are discussed.
Stochastic cooling for beginners
International Nuclear Information System (INIS)
Moehl, D.
1984-01-01
These two lectures have been prepared to give a simple introduction to the principles. In Part I we try to explain stochastic cooling using the time-domain picture which starts from the pulse response of the system. In Part II the discussion is repeated, looking more closely at the frequency-domain response. An attempt is made to familiarize the beginners with some of the elementary cooling equations, from the 'single particle case' up to equations which describe the evolution of the particle distribution. (orig.)
Stochastic ontogenetic growth model
West, B. J.; West, D.
2012-02-01
An ontogenetic growth model (OGM) for a thermodynamically closed system is generalized to satisfy both the first and second law of thermodynamics. The hypothesized stochastic ontogenetic growth model (SOGM) is shown to entail the interspecies allometry relation by explicitly averaging the basal metabolic rate and the total body mass over the steady-state probability density for the total body mass (TBM). This is the first derivation of the interspecies metabolic allometric relation from a dynamical model and the asymptotic steady-state distribution of the TBM is fit to data and shown to be inverse power law.
Stochastic calculus in physics
International Nuclear Information System (INIS)
Fox, R.F.
1987-01-01
The relationship of Ito-Stratonovich stochastic calculus to studies of weakly colored noise is explained. A functional calculus approach is used to obtain an effective Fokker-Planck equation for the weakly colored noise regime. In a smooth limit, this representation produces the Stratonovich version of the Ito-Stratonovich calculus for white noise. It also provides an approach to steady state behavior for strongly colored noise. Numerical simulation algorithms are explored, and a novel suggestion is made for efficient and accurate simulation of white noise equations
Hippocampal synaptic plasticity, spatial memory and anxiety
Bannerman, David M.; Sprengel, Rolf; Sanderson, David J.; McHugh, Stephen B.; Rawlins, J. Nicholas P.; Monyer, Hannah; Seeburg, Peter H.
2014-01-01
Recent studies using transgenic mice lacking NMDA receptors in the hippocampus challenge the long-standing hypothesis that hippocampal long-term potentiation-like mechanisms underlie the encoding and storage of associative long-term spatial memories. However, it may not be the synaptic plasticity-dependent memory hypothesis that is wrong; instead, it may be the role of the hippocampus that needs to be re-examined. We present an account of hippocampal function that explains its role in both me...
Carpentier, Pierre; Cohen, Guy; De Lara, Michel
2015-01-01
The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics.
Local synaptic signaling enhances the stochastic transport of motor-driven cargo in neurons
Newby, Jay
2010-08-23
The tug-of-war model of motor-driven cargo transport is formulated as an intermittent trapping process. An immobile trap, representing the cellular machinery that sequesters a motor-driven cargo for eventual use, is located somewhere within a microtubule track. A particle representing a motor-driven cargo that moves randomly with a forward bias is introduced at the beginning of the track. The particle switches randomly between a fast moving phase and a slow moving phase. When in the slow moving phase, the particle can be captured by the trap. To account for the possibility that the particle avoids the trap, an absorbing boundary is placed at the end of the track. Two local signaling mechanisms-intended to improve the chances of capturing the target-are considered by allowing the trap to affect the tug-of-war parameters within a small region around itself. The first is based on a localized adenosine triphosphate (ATP) concentration gradient surrounding a synapse, and the second is based on a concentration of tau-a microtubule-associated protein involved in Alzheimer\\'s disease-coating the microtubule near the synapse. It is shown that both mechanisms can lead to dramatic improvements in the capture probability, with a minimal increase in the mean capture time. The analysis also shows that tau can cause a cargo to undergo random oscillations, which could explain some experimental observations. © 2010 IOP Publishing Ltd.
Barrett, Shawnna G; Chapman, C Andrew
2013-10-25
The entorhinal cortex is thought to play roles in sensory and mnemonic function, and the cholinergic suppression of the strength of synaptic inputs is likely to have important impacts on these processes. Field excitatory postsynaptic potentials (fEPSPs) in the medial entorhinal cortex evoked by stimulation of the piriform cortex are suppressed during theta EEG activity in behaving animals, and cholinergic receptor activation suppresses synaptic responses both in vivo, and in layer II entorhinal neurons in vitro. Here, we have used in vitro field potential recordings to investigate the transmitter receptors that mediate the cholinergic suppression of synaptic responses in layer I inputs to layer II of the medial entorhinal cortex. Bath-application of the cholinergic agonist carbachol suppressed the amplitude of fEPSPs with an EC50 of 5.3μM, and enhanced paired-pulse ratio. The M2/M4 preferring receptor blocker methoctramine, or the M4 receptor blocker PD102807, did not prevent the cholinergic suppression. However, the M1/M4 receptor blocker pirenzepine and the M1 receptor blocker VU0255035 reduced the suppression, suggesting that the cholinergic suppression of synaptic responses in the entorhinal cortex is dependent in large part on activation of M1 receptors. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Synaptic theory of Replicator-like melioration
Directory of Open Access Journals (Sweden)
Yonatan Loewenstein
2010-06-01
Full Text Available According to the theory of Melioration, organisms in repeated choice settings shift their choice preference in favor of the alternative that provides the highest return. The goal of this paper is to explain how this learning behavior can emerge from microscopic changes in the efficacies of synapses, in the context of two-alternative repeated-choice experiment. I consider a large family of synaptic plasticity rules in which changes in synaptic efficacies are driven by the covariance between reward and neural activity. I construct a general framework that predicts the learning dynamics of any decision-making neural network that implements this synaptic plasticity rule and show that melioration naturally emerges in such networks. Moreover, the resultant learning dynamics follows the Replicator equation which is commonly used to phenomenologically describe changes in behavior in operant conditioning experiments. Several examples demonstrate how the learning rate of the network is affected by its properties and by the specifics of the plasticity rule. These results help bridge the gap between cellular physiology and learning behavior.
Energy Technology Data Exchange (ETDEWEB)
Zdunek, A.; Soederberg, M. (Aeronautical Research Inst. of Sweden, Bromma (Sweden))
1989-01-01
The input card deck for the finite element program GARFEM version 3.2 is described in this manual. The program includes, but is not limited to, capabilities to handle the following problems: * Linear bar and beam element structures, * Geometrically non-linear problems (bar and beam), both static and transient dynamic analysis, * Transient response dynamics from a catalog of time varying external forcing function types or input function tables, * Eigenvalue solution (modes and frequencies), * Multi point constraints (MPC) for the modelling of mechanisms and e.g. rigid links. The MPC definition is used only in the geometrically linearized sense, * Beams with disjunct shear axis and neutral axis, * Beams with rigid offset. An interface exist that connects GARFEM with the program GAROS. GAROS is a program for aeroelastic analysis of rotating structures. Since this interface was developed GARFEM now serves as a preprocessor program in place of NASTRAN which was formerly used. Documentation of the methods applied in GARFEM exists but is so far limited to the capacities in existence before the GAROS interface was developed.
Directory of Open Access Journals (Sweden)
Judit Navracsics
2014-01-01
Full Text Available According to the critical period hypothesis, the earlier the acquisition of a second language starts, the better. Owing to the plasticity of the brain, up until a certain age a second language can be acquired successfully according to this view. Early second language learners are commonly said to have an advantage over later ones especially in phonetic/phonological acquisition. Native-like pronunciation is said to be most likely to be achieved by young learners. However, there is evidence of accentfree speech in second languages learnt after puberty as well. Occasionally, on the other hand, a nonnative accent may appear even in early second (or third language acquisition. Cross-linguistic influences are natural in multilingual development, and we would expect the dominant language to have an impact on the weaker one(s. The dominant language is usually the one that provides the largest amount of input for the child. But is it always the amount that counts? Perhaps sometimes other factors, such as emotions, ome into play? In this paper, data obtained from an EnglishPersian-Hungarian trilingual pair of siblings (under age 4 and 3 respectively is analyzed, with a special focus on cross-linguistic influences at the phonetic/phonological levels. It will be shown that beyond the amount of input there are more important factors that trigger interference in multilingual development.
International Nuclear Information System (INIS)
Borgwaldt, H.; Baumann, W.; Willerding, G.
1991-05-01
FLUTAN is a highly vectorized computer code for 3-D fluiddynamic and thermal-hydraulic analyses in cartesian and cylinder coordinates. It is related to the family of COMMIX codes originally developed at Argonne National Laboratory, USA. To a large extent, FLUTAN relies on basic concepts and structures imported from COMMIX-1B and COMMIX-2 which were made available to KfK in the frame of cooperation contracts in the fast reactor safety field. While on the one hand not all features of the original COMMIX versions have been implemented in FLUTAN, the code on the other hand includes some essential innovative options like CRESOR solution algorithm, general 3-dimensional rebalacing scheme for solving the pressure equation, and LECUSSO-QUICK-FRAM techniques suitable for reducing 'numerical diffusion' in both the enthalphy and momentum equations. This report provides users with detailed input instructions, presents formulations of the various model options, and explains by means of comprehensive sample input, how to use the code. (orig.) [de
Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis
Morales, Juan; Rodríguez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Merchán-Pérez, Angel
2013-01-01
Geometrical features of chemical synapses are relevant to their function. Two critical components of the synaptic junction are the active zone (AZ) and the postsynaptic density (PSD), as they are related to the probability of synaptic release and the number of postsynaptic receptors, respectively. Morphological studies of these structures are greatly facilitated by the use of recent electron microscopy techniques, such as combined focused ion beam milling and scanning electron microscopy (FIB/SEM), and software tools that permit reconstruction of large numbers of synapses in three dimensions. Since the AZ and the PSD are in close apposition and have a similar surface area, they can be represented by a single surface—the synaptic apposition surface (SAS). We have developed an efficient computational technique to automatically extract this surface from synaptic junctions that have previously been three-dimensionally reconstructed from actual tissue samples imaged by automated FIB/SEM. Given its relationship with the release probability and the number of postsynaptic receptors, the surface area of the SAS is a functionally relevant measure of the size of a synapse that can complement other geometrical features like the volume of the reconstructed synaptic junction, the equivalent ellipsoid size and the Feret's diameter. PMID:23847474
Dynamic Control of Synaptic Adhesion and Organizing Molecules in Synaptic Plasticity
Energy Technology Data Exchange (ETDEWEB)
Rudenko, Gabby (Texas-MED)
2017-01-01
Synapses play a critical role in establishing and maintaining neural circuits, permitting targeted information transfer throughout the brain. A large portfolio of synaptic adhesion/organizing molecules (SAMs) exists in the mammalian brain involved in synapse development and maintenance. SAMs bind protein partners, forming
Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis.
Directory of Open Access Journals (Sweden)
Juan eMorales
2013-07-01
Full Text Available Geometrical features of chemical synapses are relevant to their function. Two critical components of the synaptic junction are the active zone and the postsynaptic density, as they are related to the probability of synaptic release and the number of postsynaptic receptors, respectively. Morphological studies of these structures are greatly facilitated by the use of recent electron microscopy techniques, such as combined focused ion beam milling and scanning electron microscopy (FIB/SEM, and software tools that permit reconstruction of large numbers of synapses in three dimensions. Since the active zone and the postsynaptic density are in close apposition and have a similar surface area, they can be represented by a single surface — the synaptic apposition surface (SAS. We have developed an efficient computational technique to automatically extract this surface from synaptic junctions that have previously been three-dimensionally reconstructed from actual tissue samples imaged by automated FIB/SEM. Given its relationship with the release probability and the number of postsynaptic receptors, the surface area of the SAS is a functionally relevant measure of the size of a synapse that can complement other geometrical features like the volume of the reconstructed synaptic junction, the equivalent ellipsoid size and the Feret’s diameter.
Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity
Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro
2017-01-01
We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons fI and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on fI, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.
Hoffmann, Jochen H.O.; Meyer, H. S.; Schmitt, Arno C.; Straehle, Jakob; Weitbrecht, Trinh; Sakmann, Bert; Helmstaedter, Moritz
2015-01-01
Stimulation of a principal whisker yields sparse action potential (AP) spiking in layer 2/3 (L2/3) pyramidal neurons in a cortical column of rat barrel cortex. The low AP rates in pyramidal neurons could be explained by activation of interneurons in L2/3 providing inhibition onto L2/3 pyramidal neurons. L2/3 interneurons classified as local inhibitors based on their axonal projection in the same column were reported to receive strong excitatory input from spiny neurons in L4, which are also the main source of the excitatory input to L2/3 pyramidal neurons. Here, we investigated the remaining synaptic connection in this intracolumnar microcircuit. We found strong and reliable inhibitory synaptic transmission between intracolumnar L2/3 local-inhibitor-to-L2/3 pyramidal neuron pairs [inhibitory postsynaptic potential (IPSP) amplitude −0.88 ± 0.67 mV]. On average, 6.2 ± 2 synaptic contacts were made by L2/3 local inhibitors onto L2/3 pyramidal neurons at 107 ± 64 µm path distance from the pyramidal neuron soma, thus overlapping with the distribution of synaptic contacts from L4 spiny neurons onto L2/3 pyramidal neurons (67 ± 34 µm). Finally, using compartmental simulations, we determined the synaptic conductance per synaptic contact to be 0.77 ± 0.4 nS. We conclude that the synaptic circuit from L4 to L2/3 can provide efficient shunting inhibition that is temporally and spatially aligned with the excitatory input from L4 to L2/3. PMID:25761638
Stochastic reduced order models for inverse problems under uncertainty.
Warner, James E; Aquino, Wilkins; Grigoriu, Mircea D
2015-03-01
This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well.
Influence of Signal Stationarity on Digital Stochastic Measurement Implementation
Directory of Open Access Journals (Sweden)
Ivan Župunski
2013-06-01
Full Text Available The paper presents the influence of signal stationarity on digital stochastic measurement method implementation. The implementation method is based on stochastic voltage generators, analog adders, low resolution A/D converter, and multipliers and accumulators implemented by Field-Programmable Gate Array (FPGA. The characteristic of first implementations of digital stochastic measurement was the measurement of stationary signal harmonics over the constant measurement period. Later, digital stochastic measurement was extended and used also when it was necessary to measure timeseries of non-stationary signal over the variable measurement time. The result of measurement is the set of harmonics, which is, in the case of non-stationary signals, the input for calculating digital values of signal in time domain. A theoretical approach to determine measurement uncertainty is presented and the accuracy trends with varying signal-to-noise ratio (SNR are analyzed. Noisy brain potentials (spontaneous and nonspontaneous are selected as an example of real non-stationary signal and its digital stochastic measurement is tested by simulations and experiments. Tests were performed without noise and with adding noise with SNR values of 10dB, 0dB and - 10dB. The results of simulations and experiments are compared versus theory calculations, and comparasion confirms the theory.
Assessment of Stochastic Capacity Consumption in Railway Networks
DEFF Research Database (Denmark)
Jensen, Lars Wittrup; Landex, Alex; Nielsen, Otto Anker
2015-01-01
in networks where a timetable is not needed as input. We account for robustness using a stochastic simulation of delays to obtain the stochastic capacity consumption in a network. The model is used on a case network where four different infrastructure scenarios are considered and both deterministic......The railway industry continuously strive to reduce cost and utilise resources optimally. Thus, there is a demand for tools that are able to fast and efficiently provide decision-makers with solutions that can help them achieve their goals. In strategic planning of capacity, this translates...... into being able to evaluate capacity considering robustness of the operation effi- ciently. To achieve this efficiency a timetable should not be needed as input, as producing timetables is very time consuming. In this paper we therefore propose a model to calculate the capacity consumption distribution...
Stochastic Blind Motion Deblurring
Xiao, Lei
2015-05-13
Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can therefore only be obtained with the help of prior information in the form of (often non-convex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with PSNR values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms.
AA, stochastic precooling pickup
CERN PhotoLab
1980-01-01
The freshly injected antiprotons were subjected to fast stochastic "precooling". In this picture of a precooling pickup, the injection orbit is to the left, the stack orbit to the far right. After several seconds of precooling with the system's kickers (in momentum and in the vertical plane), the precooled antiprotons were transferred, by means of RF, to the stack tail, where they were subjected to further stochastic cooling in momentum and in both transverse planes, until they ended up, deeply cooled, in the stack core. During precooling, a shutter near the central orbit shielded the pickups from the signals emanating from the stack-core, whilst the stack-core was shielded from the violent action of the precooling kickers by a shutter on these. All shutters were opened briefly during transfer of the precooled antiprotons to the stack tail. Here, the shutter is not yet mounted. Precooling pickups and kickers had the same design, except that the kickers had cooling circuits and the pickups had none. Peering th...
Stochastic Models of Polymer Systems
2016-01-01
published in non-peer-reviewed journals (N/A for none) The dynamics of stochastic gradient algorithms (submitted); Noisy Hegselmann- Krause Systems...algorithms for big data applications. (2) We studied stochastic dynamics of polymer systems in the mean field limit. (3) We studied noisy Hegselmann- Krause
Stochastic programming with integer recourse
van der Vlerk, Maarten Hendrikus
1995-01-01
In this thesis we consider two-stage stochastic linear programming models with integer recourse. Such models are at the intersection of two different branches of mathematical programming. On the one hand some of the model parameters are random, which places the problem in the field of stochastic
Stochastic ferromagnetism analysis and numerics
Brzezniak, Zdzislaw; Neklyudov, Mikhail; Prohl, Andreas
2013-01-01
This monograph examines magnetization dynamics at elevated temperatures which can be described by the stochastic Landau-Lifshitz-Gilbert equation (SLLG). Comparative computational studies with the stochastic model are included. Constructive tools such as e.g. finite element methods are used to derive the theoretical results, which are then used for computational studies.
Stochastic Pi-calculus Revisited
DEFF Research Database (Denmark)
Cardelli, Luca; Mardare, Radu Iulian
2013-01-01
We develop a version of stochastic Pi-calculus with a semantics based on measure theory. We dene the behaviour of a process in a rate environment using measures over the measurable space of processes induced by structural congruence. We extend the stochastic bisimulation to include the concept...
Anticipated backward stochastic differential equations
Peng, Shige; Yang, Zhe
2007-01-01
In this paper we discuss new types of differential equations which we call anticipated backward stochastic differential equations (anticipated BSDEs). In these equations the generator includes not only the values of solutions of the present but also the future. We show that these anticipated BSDEs have unique solutions, a comparison theorem for their solutions, and a duality between them and stochastic differential delay equations.
Mixed effects in stochastic differential equation models
DEFF Research Database (Denmark)
Ditlevsen, Susanne; De Gaetano, Andrea
2005-01-01
maximum likelihood; pharmacokinetics; population estimates; random effects; repeated measurements; stochastic processes......maximum likelihood; pharmacokinetics; population estimates; random effects; repeated measurements; stochastic processes...
Energy Technology Data Exchange (ETDEWEB)
Liu, Yunlong; Wang, Aiping; Guo, Lei; Wang, Hong
2017-07-09
This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.
Synaptic Homeostasis and Its Immunological Disturbance in Neuromuscular Junction Disorders
Directory of Open Access Journals (Sweden)
Masaharu Takamori
2017-04-01
Full Text Available In the neuromuscular junction, postsynaptic nicotinic acetylcholine receptor (nAChR clustering, trans-synaptic communication and synaptic stabilization are modulated by the molecular mechanisms underlying synaptic plasticity. The synaptic functions are based presynaptically on the active zone architecture, synaptic vesicle proteins, Ca2+ channels and synaptic vesicle recycling. Postsynaptically, they are based on rapsyn-anchored nAChR clusters, localized sensitivity to ACh, and synaptic stabilization via linkage to the extracellular matrix so as to be precisely opposed to the nerve terminal. Focusing on neural agrin, Wnts, muscle-specific tyrosine kinase (a mediator of agrin and Wnts signalings and regulator of trans-synaptic communication, low-density lipoprotein receptor-related protein 4 (the receptor of agrin and Wnts and participant in retrograde signaling, laminin-network (including muscle-derived agrin, extracellular matrix proteins (participating in the synaptic stabilization and presynaptic receptors (including muscarinic and adenosine receptors, we review the functional structures of the synapse by making reference to immunological pathogenecities in postsynaptic disease, myasthenia gravis. The synapse-related proteins including cortactin, coronin-6, caveolin-3, doublecortin, R-spondin 2, amyloid precursor family proteins, glia cell-derived neurotrophic factor and neurexins are also discussed in terms of their possible contribution to efficient synaptic transmission at the neuromuscular junction.
Synaptic transmission and plasticity in an active cortical network.
Directory of Open Access Journals (Sweden)
Ramon Reig
Full Text Available BACKGROUND: The cerebral cortex is permanently active during both awake and sleep states. This ongoing cortical activity has an impact on synaptic transmission and short-term plasticity. An activity pattern generated by the cortical network is a slow rhythmic activity that alternates up (active and down (silent states, a pattern occurring during slow wave sleep, anesthesia and even in vitro. Here we have studied 1 how network activity affects short term synaptic plasticity and, 2 how synaptic transmission varies in up versus down states. METHODOLOGY/PRINCIPAL FINDINGS: Intracellular recordings obtained from cortex in vitro and in vivo were used to record synaptic potentials, while presynaptic activation was achieved either with electrical or natural stimulation. Repetitive activation of layer 4 to layer 2/3 synaptic connections from ferret visual cortex slices displayed synaptic augmentation that was larger and longer lasting in active than in silent slices. Paired-pulse facilitation was also significantly larger in an active network and it persisted for longer intervals (up to 200 ms than in silent slices. Intracortical synaptic potentials occurring during up states in vitro increased their amplitude while paired-pulse facilitation disappeared. Both intracortical and thalamocortical synaptic potentials were also significantly larger in up than in down states in the cat visual cortex in vivo. These enhanced synaptic potentials did not further facilitate when pairs of stimuli were given, thus paired-pulse facilitation during up states in vivo was virtually absent. Visually induced synaptic responses displayed larger amplitudes when occurring during up versus down states. This was further tested in rat barrel cortex, where a sensory activated synaptic potential was also larger in up states. CONCLUSIONS/SIGNIFICANCE: These results imply that synaptic transmission in an active cortical network is more secure and efficient due to larger amplitude of
Calcineurin mediates homeostatic synaptic plasticity by regulating retinoic acid synthesis.
Arendt, Kristin L; Zhang, Zhenjie; Ganesan, Subhashree; Hintze, Maik; Shin, Maggie M; Tang, Yitai; Cho, Ahryon; Graef, Isabella A; Chen, Lu
2015-10-20
Homeostatic synaptic plasticity is a form of non-Hebbian plasticity that maintains stability of the network and fidelity for information processing in response to prolonged perturbation of network and synaptic activity. Prolonged blockade of synaptic activity decreases resting Ca(2+) levels in neurons, thereby inducing retinoic acid (RA) synthesis and RA-dependent homeostatic synaptic plasticity; however, the signal transduction pathway that links reduced Ca(2+)-levels to RA synthesis remains unknown. Here we identify the Ca(2+)-dependent protein phosphatase calcineurin (CaN) as a key regulator for RA synthesis and homeostatic synaptic plasticity. Prolonged inhibition of CaN activity promotes RA synthesis in neurons, and leads to increased excitatory and decreased inhibitory synaptic transmission. These effects of CaN inhibitors on synaptic transmission are blocked by pharmacological inhibitors of RA synthesis or acute genetic deletion of the RA receptor RARα. Thus, CaN, acting upstream of RA, plays a critical role in gating RA signaling pathway in response to synaptic activity. Moreover, activity blockade-induced homeostatic synaptic plasticity is absent in CaN knockout neurons, demonstrating the essential role of CaN in RA-dependent homeostatic synaptic plasticity. Interestingly, in GluA1 S831A and S845A knockin mice, CaN inhibitor- and RA-induced regulation of synaptic transmission is intact, suggesting that phosphorylation of GluA1 C-terminal serine residues S831 and S845 is not required for CaN inhibitor- or RA-induced homeostatic synaptic plasticity. Thus, our study uncovers an unforeseen role of CaN in postsynaptic signaling, and defines CaN as the Ca(2+)-sensing signaling molecule that mediates RA-dependent homeostatic synaptic plasticity.
Variance decomposition in stochastic simulators
Le Maître, O. P.
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Energy Technology Data Exchange (ETDEWEB)
Vollan, A.; Soederberg, M. (Aeronautical Research Inst. of Sweden, Bromma (Sweden))
1989-01-01
This report describes the input for the programs GAROS1 and GAROS2, version 5.8 and later, February 1988. The GAROS system, developed by Arne Vollan, Omega GmbH, is used for the analysis of the mechanical and aeroelastic properties for general rotating systems. It has been specially designed to meet the requirements of aeroelastic stability and dynamic response of horizontal axis wind energy converters. Some of the special characteristics are: * The rotor may have one or more blades. * The blades may be rigidly attached to the hub, or they may be fully articulated. * The full elastic properties of the blades, the hub, the machine house and the tower are taken into account. * With the same basic model, a number of different analyses can be performed: Snap-shot analysis, Floquet method, transient response analysis, frequency response analysis etc.
DEFF Research Database (Denmark)
Czarnitzki, Dirk; Grimpe, Christoph; Pellens, Maikel
The viability of modern open science norms and practices depend on public disclosure of new knowledge, methods, and materials. However, increasing industry funding of research can restrict the dissemination of results and materials. We show, through a survey sample of 837 German scientists in life...... sciences, natural sciences, engineering, and social sciences, that scientists who receive industry funding are twice as likely to deny requests for research inputs as those who do not. Receiving external funding in general does not affect denying others access. Scientists who receive external funding...... of any kind are, however, 50% more likely to be denied access to research materials by others, but this is not affected by being funded specifically by industry....
DEFF Research Database (Denmark)
Czarnitzki, Dirk; Grimpe, Christoph; Pellens, Maikel
2015-01-01
The viability of modern open science norms and practices depends on public disclosure of new knowledge, methods, and materials. However, increasing industry funding of research can restrict the dissemination of results and materials. We show, through a survey sample of 837 German scientists in life...... sciences, natural sciences, engineering, and social sciences, that scientists who receive industry funding are twice as likely to deny requests for research inputs as those who do not. Receiving external funding in general does not affect denying others access. Scientists who receive external funding...... of any kind are, however, 50 % more likely to be denied access to research materials by others, but this is not affected by being funded specifically by industry...
xSPDE: Extensible software for stochastic equations
Directory of Open Access Journals (Sweden)
Simon Kiesewetter
2016-01-01
Full Text Available We introduce an extensible software toolbox, xSPDE, for solving ordinary and partial stochastic differential equations. The toolbox makes extensive use of vector and parallel methods. Inputs are exceptionally simple, to reduce the learning curve, with default options for all of the many input parameters. The code calculates functional means, correlations and spectra, checks for errors in both time-step and sampling, and provides several choices of algorithm. Most aspects of the code, including the numerical algorithm, have a modular functional design to allow user modifications.
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.
Harsing, Laszlo G; Matyus, Peter
2013-04-01
Glycine is an amino acid neurotransmitter that is involved in both inhibitory and excitatory neurochemical transmission in the central nervous system. The role of glycine in excitatory neurotransmission is related to its coagonist action at glutamatergic N-methyl-D-aspartate receptors. The glycine levels in the synaptic cleft rise many times higher during synaptic activation assuring that glycine spills over into the extrasynaptic space. Another possible origin of extrasynaptic glycine is the efflux of glycine occurring from astrocytes associated with glutamatergic synapses. The release of glycine from neuronal or glial origins exhibits several differences compared to that of biogenic amines or other amino acid neurotransmitters. These differences appear in an external Ca(2+)- and temperature-dependent manner, conferring unique characteristics on glycine as a neurotransmitter. Glycine transporter type-1 at synapses may exhibit neural and glial forms and plays a role in controlling synaptic glycine levels and the spill over rate of glycine from the synaptic cleft into the extrasynaptic biophase. Non-synaptic glycine transporter type-1 regulates extrasynaptic glycine concentrations, either increasing or decreasing them depending on the reverse or normal mode operation of the carrier molecule. While we can, at best, only estimate synaptic glycine levels at rest and during synaptic activation, glycine concentrations are readily measurable via brain microdialysis technique applied in the extrasynaptic space. The non-synaptic N-methyl-D-aspartate receptor may obtain glycine for activation following its spill over from highly active synapses or from its release mediated by the reverse operation of non-synaptic glycine transporter-1. The sensitivity of non-synaptic N-methyl-D-aspartate receptors to glutamate and glycine is many times higher than that of synaptic N-methyl-D-aspartate receptors making the former type of receptor the primary target for drug action. Synaptic
Zolnik, Timothy A; Sha, Fern; Johenning, Friedrich W; Schreiter, Eric R; Looger, Loren L; Larkum, Matthew E; Sachdev, Robert N S
2017-03-01
The genetically encoded fluorescent calcium integrator calcium-modulated photoactivatable ratiobetric integrator (CaMPARI) reports calcium influx induced by synaptic and neural activity. Its fluorescence is converted from green to red in the presence of violet light and calcium. The rate of conversion - the sensitivity to activity - is tunable and depends on the intensity of violet light. Synaptic activity and action potentials can independently initiate significant CaMPARI conversion. The level of conversion by subthreshold synaptic inputs is correlated to the strength of input, enabling optical readout of relative synaptic strength. When combined with optogenetic activation of defined presynaptic neurons, CaMPARI provides an all-optical method to map synaptic connectivity. The calcium-modulated photoactivatable ratiometric integrator (CaMPARI) is a genetically encoded calcium integrator that facilitates the study of neural circuits by permanently marking cells active during user-specified temporal windows. Permanent marking enables measurement of signals from large swathes of tissue and easy correlation of activity with other structural or functional labels. One potential application of CaMPARI is labelling neurons postsynaptic to specific populations targeted for optogenetic stimulation, giving rise to all-optical functional connectivity mapping. Here, we characterized the response of CaMPARI to several common types of neuronal calcium signals in mouse acute cortical brain slices. Our experiments show that CaMPARI is effectively converted by both action potentials and subthreshold synaptic inputs, and that conversion level is correlated to synaptic strength. Importantly, we found that conversion rate can be tuned: it is linearly related to light intensity. At low photoconversion light levels CaMPARI offers a wide dynamic range due to slower conversion rate; at high light levels conversion is more rapid and more sensitive to activity. Finally, we employed Ca
LTD-like molecular pathways in developmental synaptic pruning
Piochon, Claire; Kano, Masanobu; Hansel, Christian
2016-01-01
In long-term depression (LTD) at synapses in the adult brain, synaptic strength is reduced in an experience-dependent manner. LTD thus provides a cellular mechanism for information storage in some forms of learning. A similar activity-dependent reduction in synaptic strength also occurs in the developing brain and there provides an essential step in synaptic pruning and the postnatal development of neural circuits. Here we review evidence suggesting that LTD and synaptic pruning share components of their underlying molecular machinery and may thus represent two developmental stages of the same type of synaptic modulation that serve different, but related, functions in neural circuit plasticity. We also assess the relationship between LTD and synaptic pruning in the context of recent findings of LTD dysregulation in several mouse models of autism spectrum disorder (ASD) and discuss whether LTD deficits can indicate impaired pruning processes that are required for proper brain development. PMID:27669991
Potentiation of electrical and chemical synaptic transmission mediated by endocannabinoids
Cachope, Roger; Mackie, Ken; Triller, Antoine; O’Brien, John; Pereda, Alberto E.
2009-01-01
SUMMARY Endocannabinoids are well established as inhibitors of chemical synaptic transmission via presynaptic activation of the cannabinoid type 1 receptor (CB1R). Contrasting this notion, we show that dendritic release of endocannabinoids mediates potentiation of synaptic transmission at mixed (electrical and chemical) synaptic contacts on the goldfish Mauthner cell. Remarkably, the observed enhancement was not restricted to the glutamatergic component of the synaptic response but also included a parallel increase in electrical transmission. This novel effect involved the activation of CB1 receptors and was indirectly mediated via the release of dopamine from nearby varicosities, which in turn led to potentiation of the synaptic response via a cAMP-dependent protein kinase-mediated postsynaptic mechanism. Thus, endocannabinoid release can potentiate synaptic transmission and its functional roles include the regulation of gap junction-mediated electrical synapses. Similar interactions between endocannabinoid and dopaminergic systems may be widespread and potentially relevant for the motor and rewarding effects of cannabis derivatives. PMID:18093525
Electrophysiological Techniques for Studying Synaptic Activity In Vivo.
Jeggo, Ross; Zhao, Fei-Yue; Spanswick, David
2014-03-03
Understanding the physiology, pharmacology, and plasticity associated with synaptic function is a key goal of neuroscience research and is fundamental to identifying the processes involved in the development and manifestation of neurological disease. A diverse range of electrophysiological methodologies are used to study synaptic function. Described in this unit is a technique for recording electrical activity from a single component of the central nervous system that is used to investigate pre- and post-synaptic elements of synaptic function. A strength of this technique is that it can be used on live animals, although the effect of anesthesia must be taken into consideration when interpreting the results. This methodology can be employed not only in naïve animals for studying normal physiological synaptic function, but also in a variety of disease models, including transgenic animals, to examine dysfunctional synaptic plasticity associated with neurological pathologies. Copyright © 2013 John Wiley & Sons, Inc. All rights reserved.
[Astrocytes and microglia: active players in synaptic plasticity].
Ronzano, Rémi
2017-12-01
Synaptic plasticity consists in a change in structure and composition of presynaptic and postsynaptic compartments. For a long time, synaptic plasticity had been thought as a neuronal mechanism only under the control of neural network activity. However, recently, with the growing knowledge about glial physiology, plasticity has been reviewed as a mechanism influenced by the synaptic environment. Thus, it appears that astrocytes and microglia modulate these mechanisms modifying neural environment by clearance of neurotransmitters, releasing essential factors and modulating inflammation. Moreover, glia can change its own activity and the expression pattern of many factors that modulate synaptic plasticity according to the environment. Hence, these populations of "non-neuronal" cells in the central nervous system seem to be active players in synaptic plasticity. This review discusses how glia modulates synaptic plasticity focusing on long-term potentiation and depression, and questions the role of the signaling processes between astrocytes and microglia in these mechanisms. © 2017 médecine/sciences – Inserm.
Astrocytes and synaptic plasticity in health and disease.
Singh, A; Abraham, Wickliffe C
2017-06-01
Activity-dependent synaptic plasticity phenomena such as long-term potentiation and long-term depression are candidate mechanisms for storing information in the brain. Regulation of synaptic plasticity is critical for healthy cognition and learning and this is provided in part by metaplasticity, which can act to maintain synaptic transmission within a dynamic range and potentially prevent excitotoxicity. Metaplasticity mechanisms also allow neurons to integrate plasticity-associated signals over time. Interestingly, astrocytes appear to be critical for certain forms of synaptic plasticity and metaplasticity mechanisms. Synaptic dysfunction is increasingly viewed as an early feature of AD that is correlated with the severity of cognitive decline, and the development of these pathologies is correlated with a rise in reactive astrocytes. This review focuses on the contributions of astrocytes to synaptic plasticity and metaplasticity in normal tissue, and addresses whether astroglial pathology may lead to aberrant engagement of these mechanisms in neurological diseases such as Alzheimer's disease.
Wan, Chang Jin; Zhu, Li Qiang; Zhou, Ju Mei; Shi, Yi; Wan, Qing
2014-05-07
Ionic/electronic hybrid devices with synaptic functions are considered to be the essential building blocks for neuromorphic systems and brain-inspired computing. Here, artificial synapses based on indium-zinc-oxide (IZO) transistors gated by nanogranular SiO2 proton-conducting electrolyte films are fabricated on glass substrates. Spike-timing dependent plasticity and paired-pulse facilitation are successfully mimicked in an individual bottom-gate transistor. Most importantly, dynamic logic and dendritic integration established by spatiotemporally correlated spikes are also mimicked in dendritic transistors with two in-plane gates as the presynaptic input terminals.
Rich spectrum of neural field dynamics in the presence of short-term synaptic depression
Wang, He; Lam, Kin; Fung, C. C. Alan; Wong, K. Y. Michael; Wu, Si
2015-09-01
In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.
Stochastic population theories
Ludwig, Donald
1974-01-01
These notes serve as an introduction to stochastic theories which are useful in population biology; they are based on a course given at the Courant Institute, New York, in the Spring of 1974. In order to make the material. accessible to a wide audience, it is assumed that the reader has only a slight acquaintance with probability theory and differential equations. The more sophisticated topics, such as the qualitative behavior of nonlinear models, are approached through a succession of simpler problems. Emphasis is placed upon intuitive interpretations, rather than upon formal proofs. In most cases, the reader is referred elsewhere for a rigorous development. On the other hand, an attempt has been made to treat simple, useful models in some detail. Thus these notes complement the existing mathematical literature, and there appears to be little duplication of existing works. The authors are indebted to Miss Jeanette Figueroa for her beautiful and speedy typing of this work. The research was supported by the Na...
Morgan, Byron JT; Tanner, Martin Abba; Carlin, Bradley P
2008-01-01
Introduction and Examples Introduction Examples of data sets Basic Model Fitting Introduction Maximum-likelihood estimation for a geometric model Maximum-likelihood for the beta-geometric model Modelling polyspermy Which model? What is a model for? Mechanistic models Function Optimisation Introduction MATLAB: graphs and finite differences Deterministic search methods Stochastic search methods Accuracy and a hybrid approach Basic Likelihood ToolsIntroduction Estimating standard errors and correlations Looking at surfaces: profile log-likelihoods Confidence regions from profiles Hypothesis testing in model selectionScore and Wald tests Classical goodness of fit Model selection biasGeneral Principles Introduction Parameterisation Parameter redundancy Boundary estimates Regression and influence The EM algorithm Alternative methods of model fitting Non-regular problemsSimulation Techniques Introduction Simulating random variables Integral estimation Verification Monte Carlo inference Estimating sampling distributi...
Stochastic separation theorems.
Gorban, A N; Tyukin, I Y
2017-10-01
The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples where the system works properly. We demonstrate that in (moderately) high dimension this separation could be achieved with probability close to one by linear discriminants. Based on fundamental properties of measure concentration, we show that for M1-ϑ, where 1>ϑ>0 is a given small constant. Exact values of a,b>0 depend on the probability distribution that determines how the random M-element sets are drawn, and on the constant ϑ. These stochastic separation theorems provide a new instrument for the development, analysis, and assessment of machine learning methods and algorithms in high dimension. Theoretical statements are illustrated with numerical examples. Copyright © 2017 Elsevier Ltd. All rights reserved.
Defective Glycinergic Synaptic Transmission in Zebrafish Motility Mutants
Hirata, Hiromi; Carta, Eloisa; Yamanaka, Iori; Harvey, Robert J.; Kuwada, John Y.
2010-01-01
Glycine is a major inhibitory neurotransmitter in the spinal cord and brainstem. Recently, in vivo analysis of glycinergic synaptic transmission has been pursued in zebrafish using molecular genetics. An ENU mutagenesis screen identified two behavioral mutants that are defective in glycinergic synaptic transmission. Zebrafish bandoneon (beo) mutants have a defect in glrbb, one of the duplicated glycine receptor (GlyR) β subunit genes. These mutants exhibit a loss of glycinergic synaptic ...
Decoding suprathreshold stochastic resonance with optimal weights
International Nuclear Information System (INIS)
Xu, Liyan; Vladusich, Tony; Duan, Fabing; Gunn, Lachlan J.; Abbott, Derek; McDonnell, Mark D.
2015-01-01
We investigate an array of stochastic quantizers for converting an analog input signal into a discrete output in the context of suprathreshold stochastic resonance. A new optimal weighted decoding is considered for different threshold level distributions. We show that for particular noise levels and choices of the threshold levels optimally weighting the quantizer responses provides a reduced mean square error in comparison with the original unweighted array. However, there are also many parameter regions where the original array provides near optimal performance, and when this occurs, it offers a much simpler approach than optimally weighting each quantizer's response. - Highlights: • A weighted summing array of independently noisy binary comparators is investigated. • We present an optimal linearly weighted decoding scheme for combining the comparator responses. • We solve for the optimal weights by applying least squares regression to simulated data. • We find that the MSE distortion of weighting before summation is superior to unweighted summation of comparator responses. • For some parameter regions, the decrease in MSE distortion due to weighting is negligible
Decoding suprathreshold stochastic resonance with optimal weights
Energy Technology Data Exchange (ETDEWEB)
Xu, Liyan, E-mail: xuliyan@qdu.edu.cn [Institute of Complexity Science, Qingdao University, Qingdao 266071 (China); Vladusich, Tony [Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, SA 5095 (Australia); Duan, Fabing [Institute of Complexity Science, Qingdao University, Qingdao 266071 (China); Gunn, Lachlan J.; Abbott, Derek [Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA 5005 (Australia); McDonnell, Mark D. [Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, SA 5095 (Australia); Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA 5005 (Australia)
2015-10-09
We investigate an array of stochastic quantizers for converting an analog input signal into a discrete output in the context of suprathreshold stochastic resonance. A new optimal weighted decoding is considered for different threshold level distributions. We show that for particular noise levels and choices of the threshold levels optimally weighting the quantizer responses provides a reduced mean square error in comparison with the original unweighted array. However, there are also many parameter regions where the original array provides near optimal performance, and when this occurs, it offers a much simpler approach than optimally weighting each quantizer's response. - Highlights: • A weighted summing array of independently noisy binary comparators is investigated. • We present an optimal linearly weighted decoding scheme for combining the comparator responses. • We solve for the optimal weights by applying least squares regression to simulated data. • We find that the MSE distortion of weighting before summation is superior to unweighted summation of comparator responses. • For some parameter regions, the decrease in MSE distortion due to weighting is negligible.
A pivotal role of GSK-3 in synaptic plasticity
Directory of Open Access Journals (Sweden)
Clarrisa A Bradley
2012-02-01
Full Text Available Glycogen synthase kinase-3 (GSK-3 has many cellular functions. Recent evidence suggests that it plays a key role in certain types of synaptic plasticity, in particular a form of long-term depression (LTD that is induced by the synaptic activation of N-methyl-D-aspartate (NMDA receptors. In the present article we summarise what is currently known concerning the roles of GSK-3 in synaptic plasticity at both glutamatergic and GABAergic synapses. We summarise its role in cognition and speculate on how alterations in the synaptic functioning of GSK-3 may be a major factor in certain neurodegenerative disorders.
Mild hypoxia affects synaptic connectivity in cultured neuronal networks.
Hofmeijer, Jeannette; Mulder, Alex T B; Farinha, Ana C; van Putten, Michel J A M; le Feber, Joost
2014-04-04
Eighty percent of patients with chronic mild cerebral ischemia/hypoxia resulting from chronic heart failure or pulmonary disease have cognitive impairment. Overt structural neuronal damage is lacking and the precise cause of neuronal damage is unclear. As almost half of the cerebral energy consumption is used for synaptic transmission, and synaptic failure is the first abrupt consequence of acute complete anoxia, synaptic dysfunction is a candidate mechanism for the cognitive deterioration in chronic mild ischemia/hypoxia. Because measurement of synaptic functioning in patients is problematic, we use cultured networks of cortical neurons from new born rats, grown over a multi-electrode array, as a model system. These were exposed to partial hypoxia (partial oxygen pressure of 150Torr lowered to 40-50Torr) during 3 (n=14) or 6 (n=8) hours. Synaptic functioning was assessed before, during, and after hypoxia by assessment of spontaneous network activity, functional connectivity, and synaptically driven network responses to electrical stimulation. Action potential heights and shapes and non-synaptic stimulus responses were used as measures of individual neuronal integrity. During hypoxia of 3 and 6h, there was a statistically significant decrease of spontaneous network activity, functional connectivity, and synaptically driven network responses, whereas direct responses and action potentials remained unchanged. These changes were largely reversible. Our results indicate that in cultured neuronal networks, partial hypoxia during 3 or 6h causes isolated disturbances of synaptic connectivity. Copyright © 2014 Elsevier B.V. All rights reserved.
A pivotal role of GSK-3 in synaptic plasticity
Bradley, Clarrisa A.; Peineau, Stéphane; Taghibiglou, Changiz; Nicolas, Celine S.; Whitcomb, Daniel J.; Bortolotto, Zuner A.; Kaang, Bong-Kiun; Cho, Kwangwook; Wang, Yu Tian; Collingridge, Graham L.
2012-01-01
Glycogen synthase kinase-3 (GSK-3) has many cellular functions. Recent evidence suggests that it plays a key role in certain types of synaptic plasticity, in particular a form of long-term depression (LTD) that is induced by the synaptic activation of N-methyl-D-aspartate receptors (NMDARs). In the present article we summarize what is currently known concerning the roles of GSK-3 in synaptic plasticity at both glutamatergic and GABAergic synapses. We summarize its role in cognition and speculate on how alterations in the synaptic functioning of GSK-3 may be a major factor in certain neurodegenerative disorders. PMID:22363262
Reprocessing input data validation
International Nuclear Information System (INIS)
Persiani, P.J.; Bucher, R.G.; Pond, R.B.; Cornella, R.J.
1990-01-01
The Isotope Correlation Technique (ICT), in conjunction with the gravimetric (Pu/U ratio) method for mass determination, provides an independent verification of the input accountancy at the dissolver or accountancy stage of the reprocessing plant. The Isotope Correlation Technique has been applied to many classes of domestic and international reactor systems (light-water, heavy-water, graphite, and liquid-metal) operating in a variety of modes (power, research, production, and breeder), and for a variety of reprocessing fuel cycle management strategies. Analysis of reprocessing operations data based on isotopic correlations derived for assemblies in a PWR environment and fuel management scheme, yielded differences between the measurement-derived and ICT-derived plutonium mass determinations of (-0.02 ± 0.23)% for the measured U-235 and (+0.50 ± 0.31)% for the measured Pu-239, for a core campaign. The ICT analyses has been implemented for the plutonium isotopics in a depleted uranium assembly in a heavy-water, enriched uranium system and for the uranium isotopes in the fuel assemblies in light-water, highly-enriched systems. 7 refs., 5 figs., 4 tabs
Directory of Open Access Journals (Sweden)
Narayan eSrinivasa
2013-02-01
Full Text Available This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1. This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through +/- 180 along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.
Srinivasa, Narayan; Jiang, Qin
2013-01-01
This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN) to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through ±180° along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.
Directory of Open Access Journals (Sweden)
Asami Tanimura
2016-12-01
Full Text Available Huntington’s disease (HD is a neurodegenerative disorder characterized by deficits in movement control that are widely viewed as stemming from pathophysiological changes in the striatum. Giant, aspiny cholinergic interneurons (ChIs are key elements in the striatal circuitry controlling movement, but whether their physiological properties are intact in the HD brain is unclear. To address this issue, the synaptic properties of ChIs were examined using optogenetic approaches in the Q175 mouse model of HD. In ex vivo brain slices, synaptic facilitation at thalamostriatal synapses onto ChIs was reduced in Q175 mice. The alteration in thalamostriatal transmission was paralleled by an increased response to optogenetic stimulation of cortical axons, enabling these inputs to more readily induce burst-pause patterns of activity in ChIs. This adaptation was dependent upon amplification of cortically evoked responses by a post-synaptic upregulation of voltage-dependent Na+ channels. This upregulation also led to an increased ability of somatic spikes to invade ChI dendrites. However, there was not an alteration in the basal pacemaking rate of ChIs, possibly due to increased availability of Kv4 channels. Thus, there is a functional ‘re-wiring’ of the striatal networks in Q175 mice, which results in greater cortical control of phasic ChI activity, which is widely thought to shape the impact of salient stimuli on striatal action selection.
Hao, Yanhui; Li, Wenchao; Wang, Hui; Zhang, Jing; Yu, Chao; Tan, Shengzhi; Wang, Haoyu; Xu, Xinping; Dong, Ji; Yao, Binwei; Zhou, Hongmei; Zhao, Li; Peng, Ruiyun
2018-02-03
To explore how autophagy changes and whether autophagy is involved in the pathophysiological process of synaptic plasticity injury caused by microwave radiation, we established a 30 mW/cm 2 microwave-exposure in vivo model, which caused reversible injuries in rat neurons. Microwave radiation induced cognitive impairment in rats and synaptic plasticity injury in rat hippocampal neurons. Autophagy in rat hippocampal neurons was activated following microwave exposure. Additionally, we observed that synaptic vesicles were encapsulated by autophagosomes, a phenomenon more evident in the microwave-exposed group. Colocation of autophagosomes and synaptic vesicles in rat hippocampal neurons increased following microwave exposure. microwave exposure led to the activation of autophagy in rat hippocampal neurons, and excessive activation of autophagy might damage synaptic plasticity by mediating synaptic vesicle degradation. Copyright © 2018 Elsevier Inc. All rights reserved.
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...
Sparks, Daniel W; Chapman, C Andrew
2016-08-01
The superficial layers of the entorhinal cortex receive sensory and associational cortical inputs and provide the hippocampus with the majority of its cortical sensory input. The parasubiculum, which receives input from multiple hippocampal subfields, sends its single major output projection to layer II of the entorhinal cortex, suggesting that it may modulate processing of synaptic inputs to the entorhinal cortex. Indeed, stimulation of the parasubiculum can enhance entorhinal responses to synaptic input from the piriform cortex in vivo. Theta EEG activity contributes to spatial and mnemonic processes in this region, and the current study assessed how stimulation of the parasubiculum with either single pulses or short, five-pulse, theta-frequency trains may modulate synaptic responses in layer II entorhinal stellate neurons evoked by stimulation of layer I afferents in vitro. Parasubicular stimulation pulses or trains suppressed responses to layer I stimulation at intervals of 5 ms, and parasubicular stimulation trains facilitated layer I responses at a train-pulse interval of 25 ms. This suggests that firing of parasubicular neurons during theta activity may heterosynaptically enhance incoming sensory inputs to the entorhinal cortex. Bath application of the hyperpolarization-activated cation current (Ih) blocker ZD7288 enhanced the facilitation effect, suggesting that cholinergic inhibition of Ih may contribute. In addition, repetitive pairing of parasubicular trains and layer I stimulation induced a lasting depression of entorhinal responses to layer I stimulation. These findings provide evidence that theta activity in the parasubiculum may promote heterosynaptic modulation effects that may alter sensory processing in the entorhinal cortex. Copyright © 2016 the American Physiological Society.
Stochastic estimation of electricity consumption
International Nuclear Information System (INIS)
Electricity consumption forecasting represents a part of the stable functioning of the power system. It is very important because of rationality and increase of control process efficiency and development planning of all aspects of society. On a scientific basis, forecasting is a possible way to solve problems. Among different models that have been used in the area of forecasting, the stochastic aspect of forecasting as a part of quantitative models takes a very important place in applications. ARIMA models and Kalman filter as stochastic estimators have been treated together for electricity consumption forecasting. Therefore, the main aim of this paper is to present the stochastic forecasting aspect using short time series. (author)
Introduction to stochastic dynamic programming
Ross, Sheldon M; Lukacs, E
1983-01-01
Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist-providing counterexamples where appropriate-and the
Functional Abstraction of Stochastic Hybrid Systems
Bujorianu, L.M.; Blom, Henk A.P.; Hermanns, H.
2006-01-01
The verification problem for stochastic hybrid systems is quite difficult. One method to verify these systems is stochastic reachability analysis. Concepts of abstractions for stochastic hybrid systems are needed to ease the stochastic reachability analysis. In this paper, we set up different ways
An introduction to probability and stochastic processes
Melsa, James L
2013-01-01
Geared toward college seniors and first-year graduate students, this text is designed for a one-semester course in probability and stochastic processes. Topics covered in detail include probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.
Directory of Open Access Journals (Sweden)
Denise eManahan-Vaughan
2011-03-01
Full Text Available Hippocampal synaptic plasticity is believed to comprise the cellular basis for spatial learning. Strain-dependent differences in synaptic plasticity in the CA1 region have been reported. However, it is not known whether these differences extend to other synapses within the trisynaptic circuit, although there is evidence for morphological variations within that path. We investigated whether Wistar and Hooded Lister (HL rat strains express differences in synaptic plasticity in the dentate gyrus in vivo. We also explored whether they exhibit differences in the ability to engage in spatial learning in an 8-arm radial maze. Basal synaptic transmission was stable over a 24h period in both rat strains, and the input-output relationship of both strains was not significantly different. Paired-pulse analysis revealed significantly less paired-pulse facilitation in the Hooded Lister strain when pulses were given 40-100 msec apart. Low frequency stimulation at 1Hz evoked long-term depression (>24h in Wistar and short-term depression (<2h in HL rats; 200Hz stimulation induced long-term potentiation (>24h in Wistar, and a transient, significantly smaller potentiation (<1h in HL rats, suggesting that HL rats have higher thresholds for expression of persistent synaptic plasticity. Training for 10d in an 8-arm radial maze revealed that HL rats master the working memory task faster than Wistar rats, although both strains show an equivalent performance by the end of the trial period. HL rats also perform more efficiently in a double working and reference memory task. On the other hand, Wistar rats show better reference memory performance on the final (8-10 days of training. Wistar rats were less active and more anxious than HL rats.These data suggest that strain-dependent variations in hippocampal synaptic plasticity occur in different hippocampal synapses. A clear correlation with differences in spatial learning is not evident however.
Bhuiyan, Mohammad Maqueshudul Haque; Haque, Md Nazmul; Mohibbullah, Md; Kim, Yung Kyu; Moon, Il Soo
2017-09-14
Neurologic disorders are frequently characterized by synaptic pathology, including abnormal density and morphology of dendritic spines, synapse loss, and aberrant synaptic signaling and plasticity. Therefore, to promote and/or protect synapses by the use of natural molecules capable of modulating neurodevelopmental events, such as, spinogenesis and synaptic plasticity, could offer a preventive and curative strategy for nervous disorders associated with synaptic pathology. Radix Puerariae, the root of Pueraria monatana var. lobata (Willd.) Sanjappa&Pradeep, is a Chinese ethnomedicine, traditionally used for the treatment of memory-related nervous disorders including Alzheimer's disease. In the previous study, we showed that the ethanolic extracts of Radix Puerariae (RPE) and its prime constituent, puerarin induced neuritogenesis and synapse formation in cultured hippocampal neurons, and thus could improve memory functions. In the present study, we specifically investigated the abilities of RPE and puerarin to improve memory-related brain disorders through modulating synaptic maturation and functional potentiation. Rat embryonic (E19) brain neurons were cultured in the absence or presence of RPE or puerarin. At predetermined times, cells were live-stained with DiO or fixed and immunostained to visualize neuronal morphologies, or lysed for protein harvesting. Morphometric analyses of dendritic spines and synaptogenesis were performed using Image J software. Functional pre- and postsynaptic plasticity was measured by FM1-43 staining and whole-cell patch clamping, respectively. RPE or puerarin-mediated changes in actin-related protein 2 were assessed by Western blotting. Neuronal survivals were measured using propidium iodide exclusion assay. RPE and puerarin both: (1) promoted a significant increase in the numbers, and maturation, of dendritic spines; (2) modulated the formation of glutamatergic synapses; (3) potentiated synaptic transmission by increasing the sizes of
Directory of Open Access Journals (Sweden)
Paula Patricia Perissinotti
2015-01-01
Full Text Available Kelch-like 1 (KLHL1 is a neuronal actin-binding protein that modulates voltage-gated CaV2.1 (P/Q-type and CaV3.2 (α1H T-type calcium channels; KLHL1 knockdown experiments (KD cause down-regulation of both channel types and altered synaptic properties in cultured rat hippocampal neurons (Perissinotti et al., 2014. Here, we studied the effect of ablation of KLHL1 on calcium channel function and synaptic properties in cultured hippocampal neurons from KLHL1 knockout (KO mice. Western blot data showed the P/Q-type channel α1A subunit was less abundant in KO hippocampus compared to wildtype (WT; and PQ-type calcium currents were smaller in KO neurons than WT during early days in vitro, although this decrease was compensated for at late stages by increases in L-type calcium current. In contrast, T-type currents did not change in culture. However, biophysical properties and western blot analysis revealed a differential contribution of T-type channel isoforms in the KO, with CaV3.2 α1H subunit being down-regulated and CaV3.1 α1G up-regulated. Synapsin I levels were reduced in the KO hippocampus; cultured neurons displayed a concomitant reduction in synapsin I puncta and decreased miniature excitatory postsynaptic current (mEPSC frequency. In summary, genetic ablation of the calcium channel modulator resulted in compensatory mechanisms to maintain calcium current homeostasis in hippocampal KO neurons; however, synaptic alterations resulted in a reduction of excitatory synapse number, causing an imbalance of the excitatory-inhibitory synaptic input ratio favoring inhibition.
Wang, Yanqing; Burrell, Brian D
2016-08-01
Endocannabinoids can elicit persistent depression of excitatory and inhibitory synapses, reducing or enhancing (disinhibiting) neural circuit output, respectively. In this study, we examined whether differences in Cl(-) gradients can regulate which synapses undergo endocannabinoid-mediated synaptic depression vs. disinhibition using the well-characterized central nervous system (CNS) of the medicinal leech, Hirudo verbana Exogenous application of endocannabinoids or capsaicin elicits potentiation of pressure (P) cell synapses and depression of both polymodal (Npoly) and mechanical (Nmech) nociceptive synapses. In P synapses, blocking Cl(-) export prevented endocannabinoid-mediated potentiation, consistent with a disinhibition process that has been indicated by previous experiments. In Nmech neurons, which are depolarized by GABA due to an elevated Cl(-) equilibrium potentials (ECl), endocannabinoid-mediated depression was prevented by blocking Cl(-) import, indicating that this decrease in synaptic signaling was due to depression of excitatory GABAergic input (disexcitation). Npoly neurons are also depolarized by GABA, but endocannabinoids elicit depression in these synapses directly and were only weakly affected by disruption of Cl(-) import. Consequently, the primary role of elevated ECl may be to protect Npoly synapses from disinhibition. All forms of endocannabinoid-mediated plasticity required activation of transient potential receptor vanilloid (TRPV) channels. Endocannabinoid/TRPV-dependent synaptic plasticity could also be elicited by distinct patterns of afferent stimulation with low-frequency stimulation (LFS) eliciting endocannabinoid-mediated depression of Npoly synapses and high-frequency stimulus (HFS) eliciting endocannabinoid-mediated potentiation of P synapses and depression of Nmech synapses. These findings demonstrate a critical role of differences in Cl(-) gradients between neurons in determining the sign, potentiation vs. depression, of
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.
Directory of Open Access Journals (Sweden)
Christian Albers
Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.
Albers, Christian; Westkott, Maren; Pawelzik, Klaus
2016-01-01
Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity
Albers, Christian; Westkott, Maren; Pawelzik, Klaus
2016-01-01
Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845
Computational methods in stochastic dynamics
Papadrakakis, Manolis; Papadopoulos, Vissarion
2011-01-01
Covering what is an emerging frontier in research, this book focuses on advanced computational methods and software tools. These can be of huge assistance in tackling complex problems in stochastic dynamic and seismic analysis as well as structure design.
Statistical validation of stochastic models
Energy Technology Data Exchange (ETDEWEB)
Hunter, N.F. [Los Alamos National Lab., NM (United States). Engineering Science and Analysis Div.; Barney, P.; Paez, T.L. [Sandia National Labs., Albuquerque, NM (United States). Experimental Structural Dynamics Dept.; Ferregut, C.; Perez, L. [Univ. of Texas, El Paso, TX (United States). Dept. of Civil Engineering
1996-12-31
It is common practice in structural dynamics to develop mathematical models for system behavior, and the authors are now capable of developing stochastic models, i.e., models whose parameters are random variables. Such models have random characteristics that are meant to simulate the randomness in characteristics of experimentally observed systems. This paper suggests a formal statistical procedure for the validation of mathematical models of stochastic systems when data taken during operation of the stochastic system are available. The statistical characteristics of the experimental system are obtained using the bootstrap, a technique for the statistical analysis of non-Gaussian data. The authors propose a procedure to determine whether or not a mathematical model is an acceptable model of a stochastic system with regard to user-specified measures of system behavior. A numerical example is presented to demonstrate the application of the technique.
International Nuclear Information System (INIS)
Faris, W.G.
1981-01-01
Dankel has shown how to incorporate spin into stochastic mechanics. The resulting non-local hidden variable theory gives an appealing picture of spin correlation experiments in which Bell's inequality is violated. (orig.)
Stochastic Integration in Abstract Spaces
Directory of Open Access Journals (Sweden)
J. K. Brooks
2010-01-01
-valued process (∫ called the stochastic integral. The Lebesgue space of these integrable processes is studied and convergence theorems are given. Extensions to general locally convex spaces are presented.
Statistical inference for stochastic processes
National Research Council Canada - National Science Library
Basawa, Ishwar V; Prakasa Rao, B. L. S
1980-01-01
The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...
Stochastic massless fields I: Integer spin
International Nuclear Information System (INIS)
Lim, S.C.
1981-04-01
Nelson's stochastic quantization scheme is applied to classical massless tensor potential in ''Coulomb'' gauge. The relationship between stochastic potential field in various gauges is discussed using the case of vector potential as an illustration. It is possible to identify the Euclidean tensor potential with the corresponding stochastic field in physical Minkowski space-time. Stochastic quantization of massless fields can also be carried out in terms of field strength tensors. An example of linearized stochastic gravitational field in vacuum is given. (author)
Synaptic Democracy and Vesicular Transport in Axons
Bressloff, Paul C.; Levien, Ethan
2015-04-01
Synaptic democracy concerns the general problem of how regions of an axon or dendrite far from the cell body (soma) of a neuron can play an effective role in neuronal function. For example, stimulated synapses far from the soma are unlikely to influence the firing of a neuron unless some sort of active dendritic processing occurs. Analogously, the motor-driven transport of newly synthesized proteins from the soma to presynaptic targets along the axon tends to favor the delivery of resources to proximal synapses. Both of these phenomena reflect fundamental limitations of transport processes based on a localized source. In this Letter, we show that a more democratic distribution of proteins along an axon can be achieved by making the transport process less efficient. This involves two components: bidirectional or "stop-and-go" motor transport (which can be modeled in terms of advection-diffusion), and reversible interactions between motor-cargo complexes and synaptic targets. Both of these features have recently been observed experimentally. Our model suggests that, just as in human societies, there needs to be a balance between "efficiency" and "equality".
Stochastic quantization and gauge theories
International Nuclear Information System (INIS)
Kolck, U. van.
1987-01-01
Stochastic quantization is presented taking the Flutuation-Dissipation Theorem as a guide. It is shown that the original approach of Parisi and Wu to gauge theories fails to give the right results to gauge invariant quantities when dimensional regularization is used. Although there is a simple solution in an abelian theory, in the non-abelian case it is probably necessary to start from a BRST invariant action instead of a gauge invariant one. Stochastic regularizations are also discussed. (author) [pt
Stochasticity induced by coherent wavepackets
International Nuclear Information System (INIS)
Fuchs, V.; Krapchev, V.; Ram, A.; Bers, A.
1983-02-01
We consider the momentum transfer and diffusion of electrons periodically interacting with a coherent longitudinal wavepacket. Such a problem arises, for example, in lower-hybrid current drive. We establish the stochastic threshold, the stochastic region δv/sub stoch/ in velocity space, the associated momentum transfer j, and the diffusion coefficient D. We concentrate principally on the weak-field regime, tau/sub autocorrelation/ < tau/sub bounce/
Stochastic Estimation via Polynomial Chaos
2015-10-01
processes. As originally developed by Norbert Weiner, a polynomial chaos represents key properties of a stochastic process through the application of...polynomial chaos method for representing the properties of second order stochastic processes. As originally developed by Norbert Weiner, a...any real or complex functional ][xF in 2L converges to ][xF in the 2L sense with Wiener measure.[3] In the same reference, the orthogonality of
Stochastic optimization: beyond mathematical programming
CERN. Geneva
2015-01-01
Stochastic optimization, among which bio-inspired algorithms, is gaining momentum in areas where more classical optimization algorithms fail to deliver satisfactory results, or simply cannot be directly applied. This presentation will introduce baseline stochastic optimization algorithms, and illustrate their efficiency in different domains, from continuous non-convex problems to combinatorial optimization problem, to problems for which a non-parametric formulation can help exploring unforeseen possible solution spaces.
Stochastic efficiency: five case studies
International Nuclear Information System (INIS)
Proesmans, Karel; Broeck, Christian Van den
2015-01-01
Stochastic efficiency is evaluated in five case studies: driven Brownian motion, effusion with a thermo-chemical and thermo-velocity gradient, a quantum dot and a model for information to work conversion. The salient features of stochastic efficiency, including the maximum of the large deviation function at the reversible efficiency, are reproduced. The approach to and extrapolation into the asymptotic time regime are documented. (paper)
Stochastic Analysis and Related Topics
Ustunel, Ali
1988-01-01
The Silvri Workshop was divided into a short summer school and a working conference, producing lectures and research papers on recent developments in stochastic analysis on Wiener space. The topics treated in the lectures relate to the Malliavin calculus, the Skorohod integral and nonlinear functionals of white noise. Most of the research papers are applications of these subjects. This volume addresses researchers and graduate students in stochastic processes and theoretical physics.
Stochastic quantization and gauge invariance
International Nuclear Information System (INIS)
Viana, R.L.
1987-01-01
A survey of the fundamental ideas about Parisi-Wu's Stochastic Quantization Method, with applications to Scalar, Gauge and Fermionic theories, is done. In particular, the Analytic Stochastic Regularization Scheme is used to calculate the polarization tensor for Quantum Electrodynamics with Dirac bosons or Fermions. The regularization influence is studied for both theories and an extension of this method for some supersymmetrical models is suggested. (author)
Stochastic Gradient Monomial Gamma Sampler
Zhang, Yizhe; Chen, Changyou; Gan, Zhe; Henao, Ricardo; Carin, Lawrence
2017-01-01
Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is often poor. This results in inadequate exploration of the posterior distribution. A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A generalized kinetic function is leveraged, delivering ...
Energy Technology Data Exchange (ETDEWEB)
Waite, Anthony; /SLAC
2011-09-07
Serial Input/Output (SIO) is designed to be a long term storage format of a sophistication somewhere between simple ASCII files and the techniques provided by inter alia Objectivity and Root. The former tend to be low density, information lossy (floating point numbers lose precision) and inflexible. The latter require abstract descriptions of the data with all that that implies in terms of extra complexity. The basic building blocks of SIO are streams, records and blocks. Streams provide the connections between the program and files. The user can define an arbitrary list of streams as required. A given stream must be opened for either reading or writing. SIO does not support read/write streams. If a stream is closed during the execution of a program, it can be reopened in either read or write mode to the same or a different file. Records represent a coherent grouping of data. Records consist of a collection of blocks (see next paragraph). The user can define a variety of records (headers, events, error logs, etc.) and request that any of them be written to any stream. When SIO reads a file, it first decodes the record name and if that record has been defined and unpacking has been requested for it, SIO proceeds to unpack the blocks. Blocks are user provided objects which do the real work of reading/writing the data. The user is responsible for writing the code for these blocks and for identifying these blocks to SIO at run time. To write a collection of blocks, the user must first connect them to a record. The record can then be written to a stream as described above. Note that the same block can be connected to many different records. When SIO reads a record, it scans through the blocks written and calls the corresponding block object (if it has been defined) to decode it. Undefined blocks are skipped. Each of these categories (streams, records and blocks) have some characteristics in common. Every stream, record and block has a name with the condition that each
A one-model approach based on relaxed combinations of inputs for evaluating input congestion in DEA
Khodabakhshi, Mohammad
2009-08-01
This paper provides a one-model approach of input congestion based on input relaxation model developed in data envelopment analysis (e.g. [G.R. Jahanshahloo, M. Khodabakhshi, Suitable combination of inputs for improving outputs in DEA with determining input congestion -- Considering textile industry of China, Applied Mathematics and Computation (1) (2004) 263-273; G.R. Jahanshahloo, M. Khodabakhshi, Determining assurance interval for non-Archimedean ele improving outputs model in DEA, Applied Mathematics and Computation 151 (2) (2004) 501-506; M. Khodabakhshi, A super-efficiency model based on improved outputs in data envelopment analysis, Applied Mathematics and Computation 184 (2) (2007) 695-703; M. Khodabakhshi, M. Asgharian, An input relaxation measure of efficiency in stochastic data analysis, Applied Mathematical Modelling 33 (2009) 2010-2023]. This approach reduces solving three problems with the two-model approach introduced in the first of the above-mentioned reference to two problems which is certainly important from computational point of view. The model is applied to a set of data extracted from ISI database to estimate input congestion of 12 Canadian business schools.
SDR Input Power Estimation Algorithms
Nappier, Jennifer M.; Briones, Janette C.
2013-01-01
The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) provides experimenters an opportunity to develop and demonstrate experimental waveforms in space. The SDR has an analog and a digital automatic gain control (AGC) and the response of the AGCs to changes in SDR input power and temperature was characterized prior to the launch and installation of the SCAN Testbed on the ISS. The AGCs were used to estimate the SDR input power and SNR of the received signal and the characterization results showed a nonlinear response to SDR input power and temperature. In order to estimate the SDR input from the AGCs, three algorithms were developed and implemented on the ground software of the SCAN Testbed. The algorithms include a linear straight line estimator, which used the digital AGC and the temperature to estimate the SDR input power over a narrower section of the SDR input power range. There is a linear adaptive filter algorithm that uses both AGCs and the temperature to estimate the SDR input power over a wide input power range. Finally, an algorithm that uses neural networks was designed to estimate the input power over a wide range. This paper describes the algorithms in detail and their associated performance in estimating the SDR input power.
Intermediate inputs and economic productivity.
Baptist, Simon; Hepburn, Cameron
2013-03-13
Many models of economic growth exclude materials, energy and other intermediate inputs from the production function. Growing environmental pressures and resource prices suggest that this may be increasingly inappropriate. This paper explores the relationship between intermediate input intensity, productivity and national accounts using a panel dataset of manufacturing subsectors in the USA over 47 years. The first contribution is to identify sectoral production functions that incorporate intermediate inputs, while allowing for heterogeneity in both technology and productivity. The second contribution is that the paper finds a negative correlation between intermediate input intensity and total factor productivity (TFP)--sectors that are less intensive in their use of intermediate inputs have higher productivity. This finding is replicated at the firm level. We propose tentative hypotheses to explain this association, but testing and further disaggregation of intermediate inputs is left for further work. Further work could also explore more directly the relationship between material inputs and economic growth--given the high proportion of materials in intermediate inputs, the results in this paper are suggestive of further work on material efficiency. Depending upon the nature of the mechanism linking a reduction in intermediate input intensity to an increase in TFP, the implications could be significant. A third contribution is to suggest that an empirical bias in productivity, as measured in national accounts, may arise due to the exclusion of intermediate inputs. Current conventions of measuring productivity in national accounts may overstate the productivity of resource-intensive sectors relative to other sectors.
Phenomenology of stochastic exponential growth
Pirjol, Dan; Jafarpour, Farshid; Iyer-Biswas, Srividya
2017-06-01
Stochastic exponential growth is observed in a variety of contexts, including molecular autocatalysis, nuclear fission, population growth, inflation of the universe, viral social media posts, and financial markets. Yet literature on modeling the phenomenology of these stochastic dynamics has predominantly focused on one model, geometric Brownian motion (GBM), which can be described as the solution of a Langevin equation with linear drift and linear multiplicative noise. Using recent experimental results on stochastic exponential growth of individual bacterial cell sizes, we motivate the need for a more general class of phenomenological models of stochastic exponential growth, which are consistent with the observation that the mean-rescaled distributions are approximately stationary at long times. We show that this behavior is not consistent with GBM, instead it is consistent with power-law multiplicative noise with positive fractional powers. Therefore, we consider this general class of phenomenological models for stochastic exponential growth, provide analytical solutions, and identify the important dimensionless combination of model parameters, which determines the shape of the mean-rescaled distribution. We also provide a prescription for robustly inferring model parameters from experimentally observed stochastic growth trajectories.
Ubiquitination-dependent mechanisms regulate synaptic growth and function.
DiAntonio, A; Haghighi, A P; Portman, S L; Lee, J D; Amaranto, A M; Goodman, C S
2001-07-26
The covalent attachment of ubiquitin to cellular proteins is a powerful mechanism for controlling protein activity and localization. Ubiquitination is a reversible modification promoted by ubiquitin ligases and antagonized by deubiquitinating proteases. Ubiquitin-dependent mechanisms regulate many important processes including cell-cycle progression, apoptosis and transcriptional regulation. Here we show that ubiquitin-dependent mechanisms regulate synaptic development at the Drosophila neuromuscular junction (NMJ). Neuronal overexpression of the deubiquitinating protease fat facets leads to a profound disruption of synaptic growth control; there is a large increase in the number of synaptic boutons, an elaboration of the synaptic branching pattern, and a disruption of synaptic function. Antagonizing the ubiquitination pathway in neurons by expression of the yeast deubiquitinating protease UBP2 (ref. 5) also produces synaptic overgrowth and dysfunction. Genetic interactions between fat facets and highwire, a negative regulator of synaptic growth that has structural homology to a family of ubiquitin ligases, suggest that synaptic development may be controlled by the balance between positive and negative regulators of ubiquitination.
Synaptic Tagging, Evaluation of Memories, and the Distal Reward Problem
Papper, Marc; Kempter, Richard; Leibold, Christian
2011-01-01
Long-term synaptic plasticity exhibits distinct phases. The synaptic tagging hypothesis suggests an early phase in which synapses are prepared, or "tagged," for protein capture, and a late phase in which those proteins are integrated into the synapses to achieve memory consolidation. The synapse specificity of the tags is consistent with…
A presynaptic role for PKA in synaptic tagging and memory.
Park, Alan Jung; Havekes, Robbert; Choi, Jennifer Hk; Luczak, Vince; Nie, Ting; Huang, Ted; Abel, Ted
2014-10-01
Protein kinase A (PKA) and other signaling molecules are spatially restricted within neurons by A-kinase anchoring proteins (AKAPs). Although studies on compartmentalized PKA signaling have focused on postsynaptic mechanisms, presynaptically anchored PKA may contribute to synaptic plasticity and memory because PKA also regulates presynaptic transmitter release. Here, we examine this issue using genetic and pharmacological application of Ht31, a PKA anchoring disrupting peptide. At the hippocampal Schaffer collateral CA3-CA1 synapse, Ht31 treatment elicits a rapid decay of synaptic responses to repetitive stimuli, indicating a fast depletion of the readily releasable pool of synaptic vesicles. The interaction between PKA and proteins involved in producing this pool of synaptic vesicles is supported by biochemical assays showing that synaptic vesicle protein 2 (SV2), Rim1, and SNAP25 are components of a complex that interacts with cAMP. Moreover, acute treatment with Ht31 reduces the levels of SV2. Finally, experiments with transgenic mouse lines, which express Ht31 in excitatory neurons at the Schaffer collateral CA3-CA1 synapse, highlight a requirement for presynaptically anchored PKA in pathway-specific synaptic tagging and long-term contextual fear memory. These results suggest that a presynaptically compartmentalized PKA is critical for synaptic plasticity and memory by regulating the readily releasable pool of synaptic vesicles. Copyright © 2014 Elsevier Inc. All rights reserved.
Impaired synaptic plasticity in RASopathies: a mini-review.
Mainberger, Florian; Langer, Susanne; Mall, Volker; Jung, Nikolai H
2016-10-01
Synaptic plasticity in the form of long-term potentiation (LTP) and long-term depression (LTD) is considered to be the neurophysiological correlate of learning and memory. Impairments are discussed to be one of the underlying pathophysiological mechanisms of developmental disorders. In so-called RASopathies [e.g., neurofibromatosis 1 (NF1)], neurocognitive impairments are frequent and are affected by components of the RAS pathway which lead to impairments in synaptic plasticity. Transcranial magnetic stimulation (TMS) provides a non-invasive method to investigate synaptic plasticity in humans. Here, we review studies using TMS to evaluate synaptic plasticity in patients with RASopathies. Patients with NF1 and Noonan syndrome (NS) showed reduced cortical LTP-like synaptic plasticity. In contrast, increased LTP-like synaptic plasticity has been shown in Costello syndrome. Notably, lovastatin normalized impaired LTP-like plasticity and increased intracortical inhibition in patients with NF1. TMS has been shown to be a safe and efficient method to investigate synaptic plasticity and intracortical inhibition in patients with RASopathies. Deeper insights in impairments of synaptic plasticity in RASopathies could help to develop new options for the therapy of learning deficits in these patients.
The discovery of GluA3-dependent synaptic plasticity
Renner, M.C.
2016-01-01
AMPA receptors (AMPARs) are responsible for fast excitatory synaptic transmission. GluA1-containing AMPARs have been extensively studied and play a key role in several forms of synaptic plasticity and memory. In contrast, GluA3-containing AMPARs have historically been ignored because they have
Role of MicroRNA in Governing Synaptic Plasticity.
Ye, Yuqin; Xu, Hongyu; Su, Xinhong; He, Xiaosheng
2016-01-01
Although synaptic plasticity in neural circuits is orchestrated by an ocean of genes, molecules, and proteins, the underlying mechanisms remain poorly understood. Recently, it is well acknowledged that miRNA exerts widespread regulation over the translation and degradation of target gene in nervous system. Increasing evidence suggests that quite a few specific miRNAs play important roles in various respects of synaptic plasticity including synaptogenesis, synaptic morphology alteration, and synaptic function modification. More importantly, the miRNA-mediated regulation of synaptic plasticity is not only responsible for synapse development and function but also involved in the pathophysiology of plasticity-related diseases. A review is made here on the function of miRNAs in governing synaptic plasticity, emphasizing the emerging regulatory role of individual miRNAs in synaptic morphological and functional plasticity, as well as their implications in neurological disorders. Understanding of the way in which miRNAs contribute to synaptic plasticity provides rational clues in establishing the novel therapeutic strategy for plasticity-related diseases.
Glutamatergic synaptic plasticity in the mesocorticolimbic system in addiction
van Huijstee, Aile N.; Mansvelder, Huibert D.
2015-01-01
Addictive drugs remodel the brain’s reward circuitry, the mesocorticolimbic dopamine (DA) system, by inducing widespread adaptations of glutamatergic synapses. This drug-induced synaptic plasticity is thought to contribute to both the development and the persistence of addiction. This review highlights the synaptic modifications that are induced by in vivo exposure to addictive drugs and describes how these drug-induced synaptic changes may contribute to the different components of addictive behavior, such as compulsive drug use despite negative consequences and relapse. Initially, exposure to an addictive drug induces synaptic changes in the ventral tegmental area (VTA). This drug-induced synaptic potentiation in the VTA subsequently triggers synaptic changes in downstream areas of the mesocorticolimbic system, such as the nucleus accumbens (NAc) and the prefrontal cortex (PFC), with further drug exposure. These glutamatergic synaptic alterations are then thought to mediate many of the behavioral symptoms that characterize addiction. The later stages of glutamatergic synaptic plasticity in the NAc and in particular in the PFC play a role in maintaining addiction and drive relapse to drug-taking induced by drug-associated cues. Remodeling of PFC glutamatergic circuits can persist into adulthood, causing a lasting vulnerability to relapse. We will discuss how these neurobiological changes produced by drugs of abuse may provide novel targets for potential treatment strategies for addiction. PMID:25653591
The Ubiquitin-Proteasome Pathway and Synaptic Plasticity
Hegde, Ashok N.
2010-01-01
Proteolysis by the ubiquitin-proteasome pathway (UPP) has emerged as a new molecular mechanism that controls wide-ranging functions in the nervous system, including fine-tuning of synaptic connections during development and synaptic plasticity in the adult organism. In the UPP, attachment of a small protein, ubiquitin, tags the substrates for…
Directory of Open Access Journals (Sweden)
Yiu Chung Tse
Full Text Available Stress exerts a profound impact on learning and memory, in part, through the actions of adrenal corticosterone (CORT on synaptic plasticity, a cellular model of learning and memory. Increasing findings suggest that CORT exerts its impact on synaptic plasticity by altering the functional properties of glutamate receptors, which include changes in the motility and function of α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid subtype of glutamate receptor (AMPAR that are responsible for the expression of synaptic plasticity. Here we provide evidence that CORT could also regulate synaptic plasticity by modulating the function of synaptic N-methyl-D-aspartate receptors (NMDARs, which mediate the induction of synaptic plasticity. We found that stress level CORT applied to adult rat hippocampal slices potentiated evoked NMDAR-mediated synaptic responses within 30 min. Surprisingly, following this fast-onset change, we observed a slow-onset (>1 hour after termination of CORT exposure increase in synaptic expression of GluN2A-containing NMDARs. To investigate the consequences of the distinct fast- and slow-onset modulation of NMDARs for synaptic plasticity, we examined the formation of long-term potentiation (LTP and long-term depression (LTD within relevant time windows. Paralleling the increased NMDAR function, both LTP and LTD were facilitated during CORT treatment. However, 1-2 hours after CORT treatment when synaptic expression of GluN2A-containing NMDARs is increased, bidirectional plasticity was no longer facilitated. Our findings reveal the remarkable plasticity of NMDARs in the adult hippocampus in response to CORT. CORT-mediated slow-onset increase in GluN2A in hippocampal synapses could be a homeostatic mechanism to normalize synaptic plasticity following fast-onset stress-induced facilitation.
Adaptation in stochastic environments
Clark, Colib
1993-01-01
The classical theory of natural selection, as developed by Fisher, Haldane, and 'Wright, and their followers, is in a sense a statistical theory. By and large the classical theory assumes that the underlying environment in which evolution transpires is both constant and stable - the theory is in this sense deterministic. In reality, on the other hand, nature is almost always changing and unstable. We do not yet possess a complete theory of natural selection in stochastic environ ments. Perhaps it has been thought that such a theory is unimportant, or that it would be too difficult. Our own view is that the time is now ripe for the development of a probabilistic theory of natural selection. The present volume is an attempt to provide an elementary introduction to this probabilistic theory. Each author was asked to con tribute a simple, basic introduction to his or her specialty, including lively discussions and speculation. We hope that the book contributes further to the understanding of the roles of "Cha...
Kallianpur, Gopinath; Hida, Takeyuki
1987-01-01
The use of probabilistic methods in the biological sciences has been so well established by now that mathematical biology is regarded by many as a distinct dis cipline with its own repertoire of techniques. The purpose of the Workshop on sto chastic methods in biology held at Nagoya University during the week of July 8-12, 1985, was to enable biologists and probabilists from Japan and the U. S. to discuss the latest developments in their respective fields and to exchange ideas on the ap plicability of the more recent developments in stochastic process theory to problems in biology. Eighteen papers were presented at the Workshop and have been grouped under the following headings: I. Population genetics (five papers) II. Measure valued diffusion processes related to population genetics (three papers) III. Neurophysiology (two papers) IV. Fluctuation in living cells (two papers) V. Mathematical methods related to other problems in biology, epidemiology, population dynamics, etc. (six papers) An important f...
AA, stochastic precooling kicker
CERN PhotoLab
1980-01-01
The freshly injected antiprotons were subjected to fast stochastic "precooling", while a shutter shielded the deeply cooled antiproton stack from the violent action of the precooling kicker. In this picture, the injection orbit is to the left, the stack orbit to the far right, the separating shutter is in open position. After several seconds of precooling (in momentum and in the vertical plane), the shutter was opened briefly, so that by means of RF the precooled antiprotons could be transferred to the stack tail, where they were subjected to further cooling in momentum and both transverse planes, until they ended up, deeply cooled, in the stack core. The fast shutter, which had to open and close in a fraction of a second was an essential item of the cooling scheme and a mechanical masterpiece. Here the shutter is in the open position. The precooling pickups were of the same design, with the difference that the kickers had cooling circuits and the pickups not. 8401150 shows a precooling pickup with the shutte...
Stochastic partial differential equations
Lototsky, Sergey V
2017-01-01
Taking readers with a basic knowledge of probability and real analysis to the frontiers of a very active research discipline, this textbook provides all the necessary background from functional analysis and the theory of PDEs. It covers the main types of equations (elliptic, hyperbolic and parabolic) and discusses different types of random forcing. The objective is to give the reader the necessary tools to understand the proofs of existing theorems about SPDEs (from other sources) and perhaps even to formulate and prove a few new ones. Most of the material could be covered in about 40 hours of lectures, as long as not too much time is spent on the general discussion of stochastic analysis in infinite dimensions. As the subject of SPDEs is currently making the transition from the research level to that of a graduate or even undergraduate course, the book attempts to present enough exercise material to fill potential exams and homework assignments. Exercises appear throughout and are usually directly connected ...
Stochastic Neural Field Theory and the System-Size Expansion
Bressloff, Paul C.
2010-01-01
We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.
International Nuclear Information System (INIS)
Zhu Guoqi; Chen Ying; Huang Yuying; Li Qinglin; Behnisch, Thomas
2011-01-01
Parkinson's disease (PD)-like symptoms including learning deficits are inducible by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Therefore, it is possible that MPTP may disturb hippocampal memory processing by modulation of dopamine (DA)- and activity-dependent synaptic plasticity. We demonstrate here that intraperitoneal (i.p.) MPTP injection reduces the number of tyrosine hydroxylase (TH)-positive neurons in the substantia nigra (SN) within 7 days. Subsequently, the TH expression level in SN and hippocampus and the amount of DA and its metabolite DOPAC in striatum and hippocampus decrease. DA depletion does not alter basal synaptic transmission and changes pair-pulse facilitation (PPF) of field excitatory postsynaptic potentials (fEPSPs) only at the 30 ms inter-pulse interval. In addition, the induction of long-term potentiation (LTP) is impaired whereas the duration of long-term depression (LTD) becomes prolonged. Since both LTP and LTD depend critically on activation of NMDA and DA receptors, we also tested the effect of DA depletion on NMDA receptor-mediated synaptic transmission. Seven days after MPTP injection, the NMDA receptor-mediated fEPSPs are decreased by about 23%. Blocking the NMDA receptor-mediated fEPSP does not mimic the MPTP-LTP. Only co-application of D1/D5 and NMDA receptor antagonists during tetanization resembled the time course of fEPSP potentiation as observed 7 days after i.p. MPTP injection. Together, our data demonstrate that MPTP-induced degeneration of DA neurons and the subsequent hippocampal DA depletion alter NMDA receptor-mediated synaptic transmission and activity-dependent synaptic plasticity. - Highlights: → I.p. MPTP-injection mediates death of dopaminergic neurons. → I.p. MPTP-injection depletes DA and DOPAC in striatum and hippocampus. → I.p. MPTP-injection does not alter basal synaptic transmission. → Reduction of LTP and enhancement of LTD after i.p. MPTP-injection. → Attenuation of NMDA-receptors mediated
Directory of Open Access Journals (Sweden)
P. Lorenzo Bozzelli
2018-01-01
Full Text Available The perineuronal net (PNN represents a lattice-like structure that is prominently expressed along the soma and proximal dendrites of parvalbumin- (PV- positive interneurons in varied brain regions including the cortex and hippocampus. It is thus apposed to sites at which PV neurons receive synaptic input. Emerging evidence suggests that changes in PNN integrity may affect glutamatergic input to PV interneurons, a population that is critical for the expression of synchronous neuronal population discharges that occur with gamma oscillations and sharp-wave ripples. The present review is focused on the composition of PNNs, posttranslation modulation of PNN components by sulfation and proteolysis, PNN alterations in disease, and potential effects of PNN remodeling on neuronal plasticity at the single-cell and population level.
Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages
Directory of Open Access Journals (Sweden)
Adam Sol Shai
2014-08-01
Full Text Available Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (~ hundreds of milliseconds and strong (~50 mV from rest depolarizations. Such plateau events rely on clustered glutamatergic input, can be mediated by calcium and NMDA currents, and often generate somatic depolarizations that last for the time course of the dendritic plateau event. We address the computational significance of such single-neuron processing via reduced but biophysically realistic modeling. We introduce a model based on two discrete integration zones, a somatic and a dendritic one, that communicate via a long plateau-conductance. We show principled differences in the way dendritic versus somatic inhibition controls spike timing, and demonstrate how this could implement a mechanism of spike time control in the face of barrages of synaptic inputs.
Hyperactivity of Newborn Pten Knock-out Neurons Results from Increased Excitatory Synaptic Drive
Williams, Michael R.; DeSpenza, Tyrone; Li, Meijie; Gulledge, Allan T.
2015-01-01
Developing neurons must regulate morphology, intrinsic excitability, and synaptogenesis to form neural circuits. When these processes go awry, disorders, including autism spectrum disorder (ASD) or epilepsy, may result. The phosphatase Pten is mutated in some patients having ASD and seizures, suggesting that its mutation disrupts neurological function in part through increasing neuronal activity. Supporting this idea, neuronal knock-out of Pten in mice can cause macrocephaly, behavioral changes similar to ASD, and seizures. However, the mechanisms through which excitability is enhanced following Pten depletion are unclear. Previous studies have separately shown that Pten-depleted neurons can drive seizures, receive elevated excitatory synaptic input, and have abnormal dendrites. We therefore tested the hypothesis that developing Pten-depleted neurons are hyperactive due to increased excitatory synaptogenesis using electrophysiology, calcium imaging, morphological analyses, and modeling. This was accomplished by coinjecting retroviruses to either “birthdate” or birthdate and knock-out Pten in granule neurons of the murine neonatal dentate gyrus. We found that Pten knock-out neurons, despite a rapid onset of hypertrophy, were more active in vivo. Pten knock-out neurons fired at more hyperpolarized membrane potentials, displayed greater peak spike rates, and were more sensitive to depolarizing synaptic input. The increased sensitivity of Pten knock-out neurons was due, in part, to a higher density of synapses located more proximal to the soma. We determined that increased synaptic drive was sufficient to drive hypertrophic Pten knock-out neurons beyond their altered action potential threshold. Thus, our work contributes a developmental mechanism for the increased activity of Pten-depleted neurons. PMID:25609613
Stochastic Optimization for Network-Constrained Power System Scheduling Problem
Directory of Open Access Journals (Sweden)
D. F. Teshome
2015-01-01
Full Text Available The stochastic nature of demand and wind generation has a considerable effect on solving the scheduling problem of a modern power system. Network constraints such as power flow equations and transmission capacities also need to be considered for a comprehensive approach to model renewable energy integration and analyze generation system flexibility. Firstly, this paper accounts for the stochastic inputs in such a way that the uncertainties are modeled as normally distributed forecast errors. The forecast errors are then superimposed on the outputs of load and wind forecasting tools. Secondly, it efficiently models the network constraints and tests an iterative algorithm and a piecewise linear approximation for representing transmission losses in mixed integer linear programming (MILP. It also integrates load shedding according to priority factors set by the system operator. Moreover, the different interactions among stochastic programming, network constraints, and prioritized load shedding are thoroughly investigated in the paper. The stochastic model is tested on a power system adopted from Jeju Island, South Korea. Results demonstrate the impact of wind speed variability and network constraints on the flexibility of the generation system. Further analysis shows the effect of loss modeling approaches on total cost, accuracy, computational time, and memory requirement.
Multi-scenario modelling of uncertainty in stochastic chemical systems
International Nuclear Information System (INIS)
Evans, R. David; Ricardez-Sandoval, Luis A.
2014-01-01
Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo
Stochastic multi-symplectic Runge-Kutta methods for stochastic Hamiltonian PDEs
Zhang, Liying; Ji, Lihai
2018-01-01
In this paper, we consider stochastic Runge-Kutta methods for stochastic Hamiltonian partial differential equations and present some sufficient conditions for multisymplecticity of stochastic Runge-Kutta methods of stochastic Hamiltonian partial differential equations. Particularly, we apply these ideas to stochastic Maxwell equations with multiplicative noise, possessing the stochastic multi-symplectic conservation law and energy conservation law. Theoretical analysis shows that the methods ...
Nicotinic mechanisms influencing synaptic plasticity in the hippocampus
Placzek, Andon Nicholas; Zhang, Tao A; Dani, John Anthony
2009-01-01
Nicotinic acetylcholine receptors (nAChRs) are expressed throughout the hippocampus, and nicotinic signaling plays an important role in neuronal function. In the context of learning and memory related behaviors associated with hippocampal function, a potentially significant feature of nAChR activity is the impact it has on synaptic plasticity. Synaptic plasticity in hippocampal neurons has long been considered a contributing cellular mechanism of learning and memory. These same kinds of cellular mechanisms are a factor in the development of nicotine addiction. Nicotinic signaling has been demonstrated by in vitro studies to affect synaptic plasticity in hippocampal neurons via multiple steps, and the signaling has also been shown to evoke synaptic plasticity in vivo. This review focuses on the nAChRs subtypes that contribute to hippocampal synaptic plasticity at the cellular and circuit level. It also considers nicotinic influences over long-term changes in the hippocampus that may contribute to addiction. PMID:19434057
Soft-bound synaptic plasticity increases storage capacity.
Directory of Open Access Journals (Sweden)
Mark C W van Rossum
Full Text Available Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses.
[Involvement of aquaporin-4 in synaptic plasticity, learning and memory].
Wu, Xin; Gao, Jian-Feng
2017-06-25
Aquaporin-4 (AQP-4) is the predominant water channel in the central nervous system (CNS) and primarily expressed in astrocytes. Astrocytes have been generally believed to play important roles in regulating synaptic plasticity and information processing. However, the role of AQP-4 in regulating synaptic plasticity, learning and memory, cognitive function is only beginning to be investigated. It is well known that synaptic plasticity is the prime candidate for mediating of learning and memory. Long term potentiation (LTP) and long term depression (LTD) are two forms of synaptic plasticity, and they share some but not all the properties and mechanisms. Hippocampus is a part of limbic system that is particularly important in regulation of learning and memory. This article is to review some research progresses of the function of AQP-4 in synaptic plasticity, learning and memory, and propose the possible role of AQP-4 as a new target in the treatment of cognitive dysfunction.
Synaptic Plasticity onto Dopamine Neurons Shapes Fear Learning.
Pignatelli, Marco; Umanah, George Kwabena Essien; Ribeiro, Sissi Palma; Chen, Rong; Karuppagounder, Senthilkumar Senthil; Yau, Hau-Jie; Eacker, Stephen; Dawson, Valina Lynn; Dawson, Ted Murray; Bonci, Antonello
2017-01-18
Fear learning is a fundamental behavioral process that requires dopamine (DA) release. Experience-dependent synaptic plasticity occurs on DA neurons while an organism is engaged in aversive experiences. However, whether synaptic plasticity onto DA neurons is causally involved in aversion learning is unknown. Here, we show that a stress priming procedure enhances fear learning by engaging VTA synaptic plasticity. Moreover, we took advantage of the ability of the ATPase Thorase to regulate the internalization of AMPA receptors (AMPARs) in order to selectively manipulate glutamatergic synaptic plasticity on DA neurons. Genetic ablation of Thorase in DAT + neurons produced increased AMPAR surface expression and function that lead to impaired induction of both long-term depression (LTD) and long-term potentiation (LTP). Strikingly, animals lacking Thorase in DAT + neurons expressed greater associative learning in a fear conditioning paradigm. In conclusion, our data provide a novel, causal link between synaptic plasticity onto DA neurons and fear learning. Published by Elsevier Inc.
International Nuclear Information System (INIS)
Gao Xiaoyan; Tang Mingliang; Li Zhifeng; Zha Yingying; Cheng Guosheng; Yin Shuting; Chen Jutao; Ruan Diyun; Chen Lin; Wang Ming
2013-01-01
Studies reported that quantum dots (QDs), as a novel probe, demonstrated a promising future for in vivo imaging, but also showed potential toxicity. This study is mainly to investigate in vivo response in the central nervous system (CNS) after exposure to QDs in a rat model of synaptic plasticity and spatial memory. Adult rats were exposed to streptavidin-conjugated CdSe/ZnS QDs (Qdots 525, purchased from Molecular Probes Inc.) by intraperitoneal injection for 7 days, followed by behavioral, electrophysiological, and biochemical examinations. The electrophysiological results show that input/output (I/O) functions were increased, while the peak of paired-pulse reaction and long-term potentiation were decreased after QDs insult, indicating synaptic transmission was enhanced and synaptic plasticity in the hippocampus was impaired. Meanwhile, behavioral experiments provide the evidence that QDs could impair rats’ spatial memory process. All the results present evidences of interference of synaptic transmission and plasticity in rat hippocampal dentate gyrus area by QDs insult and suggest potential adverse issues which should be considered in QDs applications.
Energy Technology Data Exchange (ETDEWEB)
Gao Xiaoyan [Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (Germany); Tang Mingliang [Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences (China); Li Zhifeng; Zha Yingying [University of Science and Technology of China, CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences (China); Cheng Guosheng [Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences (China); Yin Shuting [Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (Germany); Chen Jutao; Ruan Diyun; Chen Lin; Wang Ming, E-mail: wming@ustc.edu.cn [University of Science and Technology of China, CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences (China)
2013-04-15
Studies reported that quantum dots (QDs), as a novel probe, demonstrated a promising future for in vivo imaging, but also showed potential toxicity. This study is mainly to investigate in vivo response in the central nervous system (CNS) after exposure to QDs in a rat model of synaptic plasticity and spatial memory. Adult rats were exposed to streptavidin-conjugated CdSe/ZnS QDs (Qdots 525, purchased from Molecular Probes Inc.) by intraperitoneal injection for 7 days, followed by behavioral, electrophysiological, and biochemical examinations. The electrophysiological results show that input/output (I/O) functions were increased, while the peak of paired-pulse reaction and long-term potentiation were decreased after QDs insult, indicating synaptic transmission was enhanced and synaptic plasticity in the hippocampus was impaired. Meanwhile, behavioral experiments provide the evidence that QDs could impair rats' spatial memory process. All the results present evidences of interference of synaptic transmission and plasticity in rat hippocampal dentate gyrus area by QDs insult and suggest potential adverse issues which should be considered in QDs applications.
Patten, Anna R; Sickmann, Helle M; Dyer, Roger A; Innis, Sheila M; Christie, Brian R
2013-09-13
Fetal alcohol spectrum disorders result in long-lasting neurological deficits including decreases in synaptic plasticity and deficits in learning and memory. In this study we examined the effects of prenatal ethanol exposure on hippocampal synaptic plasticity in male and female Sprague-Dawley rats. Furthermore, we looked at the capacity for postnatal dietary intervention to rescue deficits in synaptic plasticity. Animals were fed an omega-3 enriched diet from birth until adulthood (PND55-70) and in vivo electrophysiology was performed by stimulating the medial perforant path input to the dentate gyrus and recording field excitatory post-synaptic potentials. LTP was induced by administering bursts of five 400 Hz pulses as a theta-patterned train of stimuli (200 ms inter-burst interval). Ethanol-exposed adult males, but not females, exhibited a significant reduction in LTP. This deficit in male animals was completely reversed with an omega-3 enriched diet. These results demonstrate that omega-3 fatty acids can have benefits following prenatal neuropathological insults and may be a viable option for alleviating some of the neurological deficits associated with FASD. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle.
Directory of Open Access Journals (Sweden)
Wilfredo Blanco
2015-05-01
Full Text Available Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS followed by a rebound during rapid-eye-movement sleep (REM. The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes
Chaudhury, Sraboni; Nag, Tapas Chandra; Wadhwa, Shashi
2009-02-01
Previous studies on prenatal auditory stimulation by species-specific sound or sitar music showed enhanced morphological and biochemical changes in chick hippocampus, which plays an important role in learning and memory. Changes in the efficiency of synapses, synaptic morphology and de novo synapse formation affects learning and memory. Therefore, in the present study, we set out to investigate the mean synaptic density and mean synaptic height at posthatch Day 1 in dorsal and ventral part of chick hippocampus following prenatal auditory stimulation. Fertilized 0 day eggs of domestic chick incubated under normal conditions were exposed to patterned sounds of species-specific and sitar music at 65 dB levels for 15 min/h round the clock (frequency range: 100-6300 Hz) from embryonic Day 10 till hatching. The synapses identified under transmission electron microscope were estimated for their numerical density by physical disector method and also the mean synaptic height calculated. Our results demonstrate a significant increase in mean synaptic density with no alterations in the mean synaptic height following both types of auditory stimulation in the dorsal as well as ventral part of the hippocampus. The observed increase in mean synaptic density suggests enhanced synaptic substrate to strengthen hippocampal function. 2008 Wiley-Liss, Inc.
Schroeder, Anna; de Wit, Joris
2018-04-09
The brain harbors billions of neurons that form distinct neural circuits with exquisite specificity. Specific patterns of connectivity between distinct neuronal cell types permit the transfer and computation of information. The molecular correlates that give rise to synaptic specificity are incompletely understood. Recent studies indicate that cell-surface molecules are important determinants of cell type identity and suggest that these are essential players in the specification of synaptic connectivity. Leucine-rich repeat (LRR)-containing adhesion molecules in particular have emerged as key organizers of excitatory and inhibitory synapses. Here, we discuss emerging evidence that LRR proteins regulate the assembly of specific connectivity patterns across neural circuits, and contribute to the diverse structural and functional properties of synapses, two key features that are critical for the proper formation and function of neural circuits.
Epigenetic mechanisms in memory and synaptic function
Sultan, Faraz A; Day, Jeremy J
2011-01-01
Although the term ‘epigenetics’ was coined nearly seventy years ago, its critical function in memory processing by the adult CNS has only recently been appreciated. The hypothesis that epigenetic mechanisms regulate memory and behavior was motivated by the need for stable molecular processes that evade turnover of the neuronal proteome. In this article, we discuss evidence that supports a role for neural epigenetic modifications in the formation, consolidation and storage of memory. In addition, we will review the evidence that epigenetic mechanisms regulate synaptic plasticity, a cellular correlate of memory. We will also examine how the concerted action of multiple epigenetic mechanisms with varying spatiotemporal profiles influence selective gene expression in response to behavioral experience. Finally, we will suggest key areas for future research that will help elucidate the complex, vital and still mysterious, role of epigenetic mechanisms in neural function and behavior. PMID:22122279
Alzheimer's disease: synaptic dysfunction and Abeta
LENUS (Irish Health Repository)
Shankar, Ganesh M
2009-11-23
Abstract Synapse loss is an early and invariant feature of Alzheimer\\'s disease (AD) and there is a strong correlation between the extent of synapse loss and the severity of dementia. Accordingly, it has been proposed that synapse loss underlies the memory impairment evident in the early phase of AD and that since plasticity is important for neuronal viability, persistent disruption of plasticity may account for the frank cell loss typical of later phases of the disease. Extensive multi-disciplinary research has implicated the amyloid β-protein (Aβ) in the aetiology of AD and here we review the evidence that non-fibrillar soluble forms of Aβ are mediators of synaptic compromise. We also discuss the possible mechanisms of Aβ synaptotoxicity and potential targets for therapeutic intervention.
Directory of Open Access Journals (Sweden)
Attila I Gulyas
2016-11-01
Full Text Available In vivo and in vitro multichannel field and somatic intracellular recordings are frequently used to study mechanisms of network pattern generation. When interpreting these data, neurons are often implicitly considered as electrotonically compact cylinders with a homogeneous distribution of excitatory and inhibitory inputs. However, the actual distributions of dendritic length, diameter, and the densities of excitatory and inhibitory input are non-uniform and cell type-specific. We first review quantitative data on the dendritic structure and synaptic input and output distribution of pyramidal cells and interneurons in the hippocampal CA1 area. Second, using multicompartmental passive models of four different types of neurons, we quantitatively explore the effect of differences in dendritic structure and synaptic distribution on the errors and biases of voltage clamp measurements of inhibitory and excitatory postsynaptic currents. Finally, using the 3-dimensional distribution of dendrites and synaptic inputs we calculate how different inhibitory and excitatory inputs contribute to the generation of local field potential in the hippocampus. We analyze these effects at different realistic background activity levels as synaptic bombardment influences neuronal conductance and thus the propagation of signals in the dendritic tree.We conclude that, since dendrites are electrotonically long and entangled in 3D, somatic intracellular and field potential recordings miss the majority of dendritic events in some cell types, and thus overemphasize the importance of perisomatic inhibitory inputs and belittle the importance of complex dendritic processing. Modeling results also suggest that pyramidal cells and inhibitory neurons probably use different input integration strategies. In pyramidal cells, second- and higher-order thin dendrites are relatively well-isolated from each other, which may support branch-specific local processing as suggested by studies
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals....
Sala, Carlo; Vicidomini, Cinzia; Bigi, Ilaria; Mossa, Adele; Verpelli, Chiara
2015-12-01
Shank/ProSAP proteins are essential to synaptic formation, development, and function. Mutations in the family of SHANK genes are strongly associated with autism spectrum disorders (ASD) and other neurodevelopmental and neuropsychiatric disorders, such as intellectual disability (ID), and schizophrenia. Thus, the term 'Shankopathies' identifies a number of neuronal diseases caused by alteration of Shank protein expression leading to abnormal synaptic development. With this review we want to summarize the major genetic, molecular, behavior and electrophysiological studies that provide new clues into the function of Shanks and pave the way for the discovery of new therapeutic drugs targeted to treat patients with SHANK mutations and also patients affected by other neurodevelopmental and neuropsychiatric disorders. Shank/ProSAP proteins are essential to synaptic formation, development, and function. Mutations in the family of SHANK genes are strongly associated with autism spectrum disorders (ASD) and other neurodevelopmental and neuropsychiatric disorders, such as intellectual disability (ID), and schizophrenia (SCZ). With this review we want to summarize the major genetic, molecular, behavior and electrophysiological studies that provide new clues into the function of Shanks and pave the way for the discovery of new therapeutic drugs targeted to treat patients with SHANK mutations. © 2015 International Society for Neurochemistry.
Input-output maps are strongly biased towards simple outputs.
Dingle, Kamaludin; Camargo, Chico Q; Louis, Ard A
2018-02-22
Many systems in nature can be described using discrete input-output maps. Without knowing details about a map, there may seem to be no a priori reason to expect that a randomly chosen input would be more likely to generate one output over another. Here, by extending fundamental results from algorithmic information theory, we show instead that for many real-world maps, the a priori probability P(x) that randomly sampled inputs generate a particular output x decays exponentially with the approximate Kolmogorov complexity [Formula: see text] of that output. These input-output maps are biased towards simplicity. We derive an upper bound P(x) ≲ [Formula: see text], which is tight for most inputs. The constants a and b, as well as many properties of P(x), can be predicted with minimal knowledge of the map. We explore this strong bias towards simple outputs in systems ranging from the folding of RNA secondary structures to systems of coupled ordinary differential equations to a stochastic financial trading model.
Global sensitivity analysis of computer models with functional inputs
International Nuclear Information System (INIS)
Iooss, Bertrand; Ribatet, Mathieu
2009-01-01
Global sensitivity analysis is used to quantify the influence of uncertain model inputs on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar model inputs. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol's indices, when some model inputs are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked generalized linear models (GLMs) or generalized additive models (GAMs). The 'mean model' allows to estimate the sensitivity indices of each scalar model inputs, while the 'dispersion model' allows to derive the total sensitivity index of the functional model inputs. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function. Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.
Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.
2010-01-01
The successful development of neural prostheses requires an understanding of the neurobiological bases of cognitive processes, i.e., how the collective activity of populations of neurons results in a higher level process not predictable based on knowledge of the individual neurons and/or synapses alone. We have been studying and applying novel methods for representing nonlinear transformations of multiple spike train inputs (multiple time series of pulse train inputs) produced by synaptic and field interactions among multiple subclasses of neurons arrayed in multiple layers of incompletely connected units. We have been applying our methods to study of the hippocampus, a cortical brain structure that has been demonstrated, in humans and in animals, to perform the cognitive function of encoding new long-term (declarative) memories. Without their hippocampi, animals and humans retain a short-term memory (memory lasting approximately 1 min), and long-term memory for information learned prior to loss of hippocampal function. Results of more than 20 years of studies have demonstrated that both individual hippocampal neurons, and populations of hippocampal cells, e.g., the neurons comprising one of the three principal subsystems of the hippocampus, induce strong, higher order, nonlinear transformations of hippocampal inputs into hippocampal outputs. For one synaptic input or for a population of synchronously active synaptic inputs, such a transformation is represented by a sequence of action potential inputs being changed into a different sequence of action potential outputs. In other words, an incoming temporal pattern is transformed into a different, outgoing temporal pattern. For multiple, asynchronous synaptic inputs, such a transformation is represented by a spatiotemporal pattern of action potential inputs being changed into a different spatiotemporal pattern of action potential outputs. Our primary thesis is that the encoding of short-term memories into new, long
Relative numbers of cortical and brainstem inputs to the lateral geniculate nucleus
Erişir, Alev; Van Horn, Susan C.; Sherman, S. Murray
1997-01-01
Terminals of a morphological type known as RD (for round vesicles and dense mitochondria, which we define here as the aggregate of types formerly known as RSD and RLD, where “S” is small and “L” is large) constitute at least half of the synaptic inputs to the feline lateral geniculate nucleus, which represents the thalamic relay of retinal input to cortex. It had been thought that the vast majority of these RD terminals were of cortical origin, making the corticogeniculate pathway by far the ...
Feasibility of energy harvesting from a rotating tire based on the theory of stochastic resonance
International Nuclear Information System (INIS)
Zhang, Y; Zheng, R; Nakano, K
2014-01-01
Recently the use of nonlinear bi-stable micro-electro mechanical systems (MEMS) to achieve automobile tire vibration power generation has made some progress. However, the theory of stochastic resonance has not been successfully applied to automobile tires, which can produce a larger vibrational response than for a typical resonance while inputting a weak periodic force and noise excitation into a nonlinear bi-stable system. Hence, in this paper, in view of the principle of stochastic resonance, a new model is derived by positioning a magnetic end mass attached to a cantilever beam and another permanent magnet with the same polarity on the frame. Due to the road noise excitation along with the periodic force inputted to the mechanism, whether the phenomenon of stochastic resonance can happen will be discussed. Meanwhile, on the basis of Kramers rate and duffing equations the preliminary experimental device is also designed
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Stochastic procedures for extreme wave induced responses in flexible ships
DEFF Research Database (Denmark)
Jensen, Jørgen Juncher; Andersen, Ingrid Marie Vincent; Seng, Sopheak
2014-01-01
Different procedures for estimation of the extreme global wave hydroelastic responses in ships are discussed. Firstly, stochastic procedures for application in detailed numerical studies (CFD) are outlined. The use of the First Order Reliability Method (FORM) to generate critical wave episodes...... of short duration, less than I minute, with prescribed probability content is discussed for use in extreme response predictions including hydroelastic behaviour and slamming load events. The possibility of combining FORM results with Monte Carlo simulations is discussed for faster but still very accurate...... estimation of extreme responses. Secondly, stochastic procedures using measured time series of responses as input are considered. The Peak-over-Threshold procedure and the Weibull fitting are applied and discussed for the extreme value predictions including possible corrections for clustering effects....
Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs
Energy Technology Data Exchange (ETDEWEB)
Liao, Qifeng, E-mail: liaoqf@shanghaitech.edu.cn [School of Information Science and Technology, ShanghaiTech University, Shanghai 200031 (China); Lin, Guang, E-mail: guanglin@purdue.edu [Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States)
2016-07-15
In this paper we present a reduced basis ANOVA approach for partial deferential equations (PDEs) with random inputs. The ANOVA method combined with stochastic collocation methods provides model reduction in high-dimensional parameter space through decomposing high-dimensional inputs into unions of low-dimensional inputs. In this work, to further reduce the computational cost, we investigate spatial low-rank structures in the ANOVA-collocation method, and develop efficient spatial model reduction techniques using hierarchically generated reduced bases. We present a general mathematical framework of the methodology, validate its accuracy and demonstrate its efficiency with numerical experiments.
Pannexin 1 Regulates Bidirectional Hippocampal Synaptic Plasticity in Adult Mice
Directory of Open Access Journals (Sweden)
Alvaro O. Ardiles
2014-10-01
Full Text Available The threshold for bidirectional modification of synaptic plasticity is known to be controlled by several factors, including the balance between protein phosphorylation and dephosphorylation, postsynaptic free Ca2+ concentration and NMDA receptor (NMDAR composition of GluN2 subunits. Pannexin 1 (Panx1, a member of the integral membrane protein family, has been shown to form non-selective channels and to regulate the induction of synaptic plasticity as well as hippocampal-dependent learning. Although Panx1 channels have been suggested to play a role in excitatory long-term potentiation (LTP, it remains unknown whether these channels also modulate long-term depression (LTD or the balance between both types of synaptic plasticity. To study how Panx1 contributes to excitatory synaptic efficacy, we examined the age-dependent effects of eliminating or blocking Panx1 channels on excitatory synaptic plasticity within the CA1 region of the mouse hippocampus. By using different protocols to induce bidirectional synaptic plasticity, Panx1 channel blockade or lack of Panx1 were found to enhance LTP, whereas both conditions precluded the induction of LTD in adults, but not in young animals. These findings suggest that Panx1 channels restrain the sliding threshold for the induction of synaptic plasticity and underlying brain mechanisms of learning and memory.
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.
Arc protein: a flexible hub for synaptic plasticity and cognition.
Nikolaienko, Oleksii; Patil, Sudarshan; Eriksen, Maria Steene; Bramham, Clive R
2017-09-07
Mammalian excitatory synapses express diverse types of synaptic plasticity. A major challenge in neuroscience is to understand how a neuron utilizes different types of plasticity to sculpt brain development, function, and behavior. Neuronal activity-induced expression of the immediate early protein, Arc, is critical for long-term potentiation and depression of synaptic transmission, homeostatic synaptic scaling, and adaptive functions such as long-term memory formation. However, the molecular basis of Arc protein function as a regulator of synaptic plasticity and cognition remains a puzzle. Recent work on the biophysical and structural properties of Arc, its protein-protein interactions and post-translational modifications have shed light on the issue. Here, we present Arc protein as a flexible, multifunctional and interactive hub. Arc interacts with specific effector proteins in neuronal compartments (dendritic spines, nuclear domains) to bidirectionally regulate synaptic strength by distinct molecular mechanisms. Arc stability, subcellular localization, and interactions are dictated by synaptic activity and post-translational modification of Arc. This functional versatility and context-dependent signaling supports a view of Arc as a highly specialized master organizer of long-term synaptic plasticity, critical for information storage and cognition. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Pannexin 1 regulates bidirectional hippocampal synaptic plasticity in adult mice.
Ardiles, Alvaro O; Flores-Muñoz, Carolina; Toro-Ayala, Gabriela; Cárdenas, Ana M; Palacios, Adrian G; Muñoz, Pablo; Fuenzalida, Marco; Sáez, Juan C; Martínez, Agustín D
2014-01-01
The threshold for bidirectional modification of synaptic plasticity is known to be controlled by several factors, including the balance between protein phosphorylation and dephosphorylation, postsynaptic free Ca(2+) concentration and NMDA receptor (NMDAR) composition of GluN2 subunits. Pannexin 1 (Panx1), a member of the integral membrane protein family, has been shown to form non-selective channels and to regulate the induction of synaptic plasticity as well as hippocampal-dependent learning. Although Panx1 channels have been suggested to play a role in excitatory long-term potentiation (LTP), it remains unknown whether these channels also modulate long-term depression (LTD) or the balance between both types of synaptic plasticity. To study how Panx1 contributes to excitatory synaptic efficacy, we examined the age-dependent effects of eliminating or blocking Panx1 channels on excitatory synaptic plasticity within the CA1 region of the mouse hippocampus. By using different protocols to induce bidirectional synaptic plasticity, Panx1 channel blockade or lack of Panx1 were found to enhance LTP, whereas both conditions precluded the induction of LTD in adults, but not in young animals. These findings suggest that Panx1 channels restrain the sliding threshold for the induction of synaptic plasticity and underlying brain mechanisms of learning and memory.
Stochasticity in Ca2+ increase in spines enables robust and sensitive information coding.
Directory of Open Access Journals (Sweden)
Takuya Koumura
Full Text Available A dendritic spine is a very small structure (∼0.1 µm3 of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca2+ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca2+ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca2+ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca2+ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca2+ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.
International Nuclear Information System (INIS)
El-Tawil, M A; Al-Jihany, A S
2008-01-01
In this paper, nonlinear oscillators under quadratic nonlinearity with stochastic inputs are considered. Different methods are used to obtain first order approximations, namely, the WHEP technique, the perturbation method, the Pickard approximations, the Adomian decompositions and the homotopy perturbation method (HPM). Some statistical moments are computed for the different methods using mathematica 5. Comparisons are illustrated through figures for different case-studies
Pinchas, Monika; Baranes, Danny
2013-01-01
Synaptic clustering on dendritic branches enhances plasticity, input integration and neuronal firing. However, the mechanisms guiding axons to cluster synapses at appropriate sites along dendritic branches are poorly understood. We searched for such a mechanism by investigating the structural overlap between dendritic branches and axons in a simplified model of neuronal networks - the hippocampal cell culture. Using newly developed software, we converted images of meshes of overlapping axonal and dendrites into topological maps of intersections, enabling quantitative study of overlapping neuritic geometry at the resolution of single dendritic branch-to-branch and axon-to-branch crossings. Among dendro-dendritic crossing configurations, it was revealed that the orientations through which dendritic branches cross is a regulated attribute. While crossing angle distribution among branches thinner than 1 µm appeared to be random, dendritic branches 1 µm or wider showed a preference for crossing each other at angle ranges of either 50°–70° or 80°–90°. It was then found that the dendro-dendritic crossings themselves, as well as their selective angles, both affected the path of axonal growth. Axons displayed 4 fold stronger tendency to traverse within 2 µm of dendro-dendritic intersections than at farther distances, probably to minimize wiring length. Moreover, almost 70% of the 50°–70° dendro-denritic crossings were traversed by axons from the obtuse angle’s zone, whereas only 15% traversed through the acute angle’s zone. By contrast, axons showed no orientation restriction when traversing 80°–90° crossings. When such traverse behavior was repeated by many axons, they converged in the vicinity of dendro-dendritic intersections, thereby clustering their synaptic connections. Thus, the vicinity of dendritic branch-to-branch crossings appears to be a regulated structure used by axons as a target for efficient wiring and as a preferred site for synaptic
Directory of Open Access Journals (Sweden)
Katsuyuki Kaneda
Full Text Available The superficial layer of the superior colliculus (sSC receives visual inputs via two different pathways: from the retina and the primary visual cortex. However, the functional significance of each input for the operation of the sSC circuit remains to be identified. As a first step toward understanding the functional role of each of these inputs, we developed an optogenetic method to specifically suppress the synaptic transmission in the retino-tectal pathway. We introduced enhanced halorhodopsin (eNpHR, a yellow light-sensitive, membrane-targeting chloride pump, into mouse retinal ganglion cells (RGCs by intravitreously injecting an adeno-associated virus serotype-2 vector carrying the CMV-eNpHR-EYFP construct. Several weeks after the injection, whole-cell recordings made from sSC neurons in slice preparations revealed that yellow laser illumination of the eNpHR-expressing retino-tectal axons, putatively synapsing onto the recorded cells, effectively inhibited EPSCs evoked by electrical stimulation of the optic nerve layer. We also showed that sSC spike activities elicited by visual stimulation were significantly reduced by laser illumination of the sSC in anesthetized mice. These results indicate that photo-activation of eNpHR expressed in RGC axons enables selective blockade of retino-tectal synaptic transmission. The method established here can most likely be applied to a variety of brain regions for studying the function of individual inputs to these regions.
Regulation of Wnt signaling by nociceptive input in animal models
Directory of Open Access Journals (Sweden)
Shi Yuqiang
2012-06-01
Full Text Available Abstract Background Central sensitization-associated synaptic plasticity in the spinal cord dorsal horn (SCDH critically contributes to the development of chronic pain, but understanding of the underlying molecular pathways is still incomplete. Emerging evidence suggests that Wnt signaling plays a crucial role in regulation of synaptic plasticity. Little is known about the potential function of the Wnt signaling cascades in chronic pain development. Results Fluorescent immunostaining results indicate that β-catenin, an essential protein in the canonical Wnt signaling pathway, is expressed in the superficial layers of the mouse SCDH with enrichment at synapses in lamina II. In addition, Wnt3a, a prototypic Wnt ligand that activates the canonical pathway, is also enriched in the superficial layers. Immunoblotting analysis indicates that both Wnt3a a β-catenin are up-regulated in the SCDH of various mouse pain models created by hind-paw injection of capsaicin, intrathecal (i.t. injection of HIV-gp120 protein or spinal nerve ligation (SNL. Furthermore, Wnt5a, a prototypic Wnt ligand for non-canonical pathways, and its receptor Ror2 are also up-regulated in the SCDH of these models. Conclusion Our results suggest that Wnt signaling pathways are regulated by nociceptive input. The activation of Wnt signaling may regulate the expression of spinal central sensitization during the development of acute and chronic pain.
Limits for Stochastic Reaction Networks
DEFF Research Database (Denmark)
Cappelletti, Daniele
at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...... network tends to that of the original one. In particular, we prove a uniform punctual convergence in distribution and weak convergence of the integrals of continuous functions along the paths of the two models. Under some extra conditions, we also prove weak convergence of the two processes. The result....... Such species, in the deterministic modelling regime, assume always the same value at any positive steady state. In the stochastic setting, we prove that, if the initial condition is a point in the basin of attraction of a positive steady state of the corresponding deterministic model and tends to innity...
Fundamentals of stochastic nature sciences
Klyatskin, Valery I
2017-01-01
This book addresses the processes of stochastic structure formation in two-dimensional geophysical fluid dynamics based on statistical analysis of Gaussian random fields, as well as stochastic structure formation in dynamic systems with parametric excitation of positive random fields f(r,t) described by partial differential equations. Further, the book considers two examples of stochastic structure formation in dynamic systems with parametric excitation in the presence of Gaussian pumping. In dynamic systems with parametric excitation in space and time, this type of structure formation either happens – or doesn’t! However, if it occurs in space, then this almost always happens (exponentially quickly) in individual realizations with a unit probability. In the case considered, clustering of the field f(r,t) of any nature is a general feature of dynamic fields, and one may claim that structure formation is the Law of Nature for arbitrary random fields of such type. The study clarifies the conditions under wh...
Stochastic models of cell motility
DEFF Research Database (Denmark)
Gradinaru, Cristian
2012-01-01
Cell motility and migration are central to the development and maintenance of multicellular organisms, and errors during this process can lead to major diseases. Consequently, the mechanisms and phenomenology of cell motility are currently under intense study. In recent years, a new...... interdisciplinary field focusing on the study of biological processes at the nanoscale level, with a range of technological applications in medicine and biological research, has emerged. The work presented in this thesis is at the interface of cell biology, image processing, and stochastic modeling. The stochastic...... models introduced here are based on persistent random motion, which I apply to real-life studies of cell motility on flat and nanostructured surfaces. These models aim to predict the time-dependent position of cell centroids in a stochastic manner, and conversely determine directly from experimental...
Verification of Stochastic Process Calculi
DEFF Research Database (Denmark)
Skrypnyuk, Nataliya
process calculi. The description of a system in the syntax of a particular stochastic process calculus can be analysed in a compositional way, without expanding the state space by explicitly resolving all the interdependencies between the subsystems which may lead to the state space explosion problem....... In support of this claim we have developed analysis methods that belong to a particular type of Static Analysis { Data Flow / Pathway Analysis. These methods have previously been applied to a number of non-stochastic process calculi. In this thesis we are lifting them to the stochastic calculus...... description of a system. The presented methods have a clear application in the areas of embedded systems, (randomised) protocols run between a fixed number of parties etc....
Stochastic quantization of general relativity
International Nuclear Information System (INIS)
Rumpf, H.
1986-01-01
Following an elementary exposition of the basic mathematical concepts used in the theory of stochastic relaxation processes the stochastic quantization method of Parisi and Wu is briefly reviewed. The method is applied to Einstein's theory of gravitation using a formalism that is manifestly covariant with respect to field redefinitions. This requires the adoption of Ito's calculus and the introduction of a metric in field configuration space, for which there is a unique candidate. Due to the indefiniteness of the Euclidean Einstein-Hilbert action stochastic quantization is generalized to the pseudo-Riemannian case. It is formally shown to imply the DeWitt path integral measure. Finally a new type of perturbation theory is developed. (Author)
Stochastic models for atmospheric dispersion
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
2003-01-01
Simple stochastic differential equation models have been applied by several researchers to describe the dispersion of tracer particles in the planetary atmospheric boundary layer and to form the basis for computer simulations of particle paths. To obtain the drift coefficient, empirical vertical...... positions close to the boundaries. Different rules have been suggested in the literature with justifications based on simulation studies. Herein the relevant stochastic differential equation model is formulated in a particular way. The formulation is based on the marginal transformation of the position...... dependent particle velocity into a position independent Gaussian velocity. Boundary conditions are obtained from Itos rule of stochastic differentiation. The model directly point at a canonical rule of reflection for the approximating random walk with finite time step. This reflection rule is different from...
Applied probability and stochastic processes
Sumita, Ushio
1999-01-01
Applied Probability and Stochastic Processes is an edited work written in honor of Julien Keilson. This volume has attracted a host of scholars in applied probability, who have made major contributions to the field, and have written survey and state-of-the-art papers on a variety of applied probability topics, including, but not limited to: perturbation method, time reversible Markov chains, Poisson processes, Brownian techniques, Bayesian probability, optimal quality control, Markov decision processes, random matrices, queueing theory and a variety of applications of stochastic processes. The book has a mixture of theoretical, algorithmic, and application chapters providing examples of the cutting-edge work that Professor Keilson has done or influenced over the course of his highly-productive and energetic career in applied probability and stochastic processes. The book will be of interest to academic researchers, students, and industrial practitioners who seek to use the mathematics of applied probability i...
A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators
Directory of Open Access Journals (Sweden)
Leon Chua
2012-03-01
Full Text Available A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.
Directory of Open Access Journals (Sweden)
Zanon Renata G
2010-05-01
Full Text Available Abstract Background Astrocytes play a major role in preserving and restoring structural and physiological integrity following injury to the nervous system. After peripheral axotomy, reactive gliosis propagates within adjacent spinal segments, influenced by the local synthesis of nitric oxide (NO. The present work investigated the importance of inducible nitric oxide synthase (iNOS activity in acute and late glial responses after injury and in major histocompatibility complex class I (MHC I expression and synaptic plasticity of inputs to lesioned alpha motoneurons. Methods In vivo analyses were carried out using C57BL/6J-iNOS knockout (iNOS-/- and C57BL/6J mice. Glial response after axotomy, glial MHC I expression, and the effects of axotomy on synaptic contacts were measured using immunohistochemistry and transmission electron microscopy. For this purpose, 2-month-old animals were sacrificed and fixed one or two weeks after unilateral sciatic nerve transection, and spinal cord sections were incubated with antibodies against classical MHC I, GFAP (glial fibrillary acidic protein - an astroglial marker, Iba-1 (an ionized calcium binding adaptor protein and a microglial marker or synaptophysin (a presynaptic terminal marker. Western blotting analysis of MHC I and nNOS expression one week after lesion were also performed. The data were analyzed using a two-tailed Student's t test for parametric data or a two-tailed Mann-Whitney U test for nonparametric data. Results A statistical difference was shown with respect to astrogliosis between strains at the different time points studied. Also, MHC I expression by iNOS-/- microglial cells did not increase at one or two weeks after unilateral axotomy. There was a difference in synaptophysin expression reflecting synaptic elimination, in which iNOS-/- mice displayed a decreased number of the inputs to alpha motoneurons, in comparison to that of C57BL/6J. Conclusion The findings herein indicate that i
B.C. Okoye; A. Abass; B. Bachwenkizi; G. Asumugha; B. Alenkhe; R. Ranaivoson; R. Randrianarivelo; N. Rabemanantsoa; I. Ralimanana
2016-01-01
This study employed the Cobb-Douglas stochastic frontier production function to measure the level of technical efficiency among smallholder cassava farmers in Central Madagascar. A multi-stage random sampling technique was used to select 180 cassava farmers in the region and from this sample, input-output data were obtained using the cost route approach. The parameters of the stochastic frontier production function were estimated using the maximum likelihood method. The results of the analysi...
A stochastic approach to chemical evolution
International Nuclear Information System (INIS)
Copi, C.J.
1997-01-01
Observations of elemental abundances in the Galaxy have repeatedly shown an intrinsic scatter as a function of time and metallicity. The standard approach to chemical evolution does not attempt to address this scatter in abundances since only the mean evolution is followed. In this work, the scatter is addressed via a stochastic approach to solving chemical evolution models. Three simple chemical evolution scenarios are studied using this stochastic approach: a closed box model, an infall model, and an outflow model. These models are solved for the solar neighborhood in a Monte Carlo fashion. The evolutionary history of one particular region is determined randomly based on the star formation rate and the initial mass function. Following the evolution in an ensemble of such regions leads to the predicted spread in abundances expected, based solely on different evolutionary histories of otherwise identical regions. In this work, 13 isotopes are followed, including the light elements, the CNO elements, a few α-elements, and iron. It is found that the predicted spread in abundances for a 10 5 M circle-dot region is in good agreement with observations for the α-elements. For CN, the agreement is not as good, perhaps indicating the need for more physics input for low-mass stellar evolution. Similarly for the light elements, the predicted scatter is quite small, which is in contradiction to the observations of 3 He in HII regions. The models are tuned for the solar neighborhood so that good agreement with HII regions is not expected. This has important implications for low-mass stellar evolution and on using chemical evolution to determine the primordial light-element abundances in order to test big bang nucleosynthesis. copyright 1997 The American Astronomical Society
Logic analysis and verification of n-input genetic logic circuits
DEFF Research Database (Denmark)
Baig, Hasan; Madsen, Jan
2017-01-01
accordingly. As compared to electronic circuits, genetic circuits exhibit stochastic behavior and do not always behave as intended. Therefore, there is a growing interest in being able to analyze and verify the logical behavior of a genetic circuit model, prior to its physical implementation in a laboratory....... In this paper, we present an approach to analyze and verify the Boolean logic of a genetic circuit from the data obtained through stochastic analog circuit simulations. The usefulness of this analysis is demonstrated through different case studies illustrating how our approach can be used to verify the expected...... behavior of an n-input genetic logic circuit....