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.
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.
Memristor-based neural networks: Synaptic versus neuronal stochasticity
Naous, Rawan; Alshedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.
2016-01-01
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
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.
Inverse stochastic resonance induced by synaptic background activity with unreliable synapses
Energy Technology Data Exchange (ETDEWEB)
Uzuntarla, Muhammet, E-mail: muzuntarla@yahoo.com
2013-11-15
Inverse stochastic resonance (ISR) is a recently pronounced phenomenon that is the minimum occurrence in mean firing rate of a rhythmically firing neuron as noise level varies. Here, by using a realistic modeling approach for the noise, we investigate the ISR with concrete biophysical mechanisms. It is shown that mean firing rate of a single neuron subjected to synaptic bombardment exhibits a minimum as the spike transmission probability varies. We also demonstrate that the occurrence of ISR strongly depends on the synaptic input regime, where it is most prominent in the balanced state of excitatory and inhibitory inputs.
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.
Stochastic synaptic plasticity with memristor crossbar arrays
Naous, Rawan; Al-Shedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.
2016-01-01
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.
The human motor neuron pools receive a dominant slow‐varying common synaptic input
Negro, Francesco; Yavuz, Utku Şükrü
2016-01-01
Key points Motor neurons in a pool receive both common and independent synaptic inputs, although the proportion and role of their common synaptic input is debated.Classic correlation techniques between motor unit spike trains do not measure the absolute proportion of common input and have limitations as a result of the non‐linearity of motor neurons.We propose a method that for the first time allows an accurate quantification of the absolute proportion of low frequency common synaptic input (60%) of common input, irrespective of their different functional and control properties.These results increase our knowledge about the role of common and independent input to motor neurons in force control. Abstract Motor neurons receive both common and independent synaptic inputs. This observation is classically based on the presence of a significant correlation between pairs of motor unit spike trains. The functional significance of different relative proportions of common input across muscles, individuals and conditions is still debated. One of the limitations in our understanding of correlated input to motor neurons is that it has not been possible so far to quantify the absolute proportion of common input with respect to the total synaptic input received by the motor neurons. Indeed, correlation measures of pairs of output spike trains only allow for relative comparisons. In the present study, we report for the first time an approach for measuring the proportion of common input in the low frequency bandwidth (60%) proportion of common low frequency oscillations with respect to their total synaptic input. These results suggest that the central nervous system provides a large amount of common input to motor neuron pools, in a similar way to that for muscles with different functional and control properties. PMID:27151459
Rawson, Randi L; Martin, E Anne; Williams, Megan E
2017-08-01
For most neurons to function properly, they need to develop synaptic specificity. This requires finding specific partner neurons, building the correct types of synapses, and fine-tuning these synapses in response to neural activity. Synaptic specificity is common at both a neuron's input and output synapses, whereby unique synapses are built depending on the partnering neuron. Neuroscientists have long appreciated the remarkable specificity of neural circuits but identifying molecular mechanisms mediating synaptic specificity has only recently accelerated. Here, we focus on recent progress in understanding input and output synaptic specificity in the mammalian brain. We review newly identified circuit examples for both and the latest research identifying molecular mediators including Kirrel3, FGFs, and DGLα. Lastly, we expect the pace of research on input and output specificity to continue to accelerate with the advent of new technologies in genomics, microscopy, and proteomics. Copyright © 2017 Elsevier Ltd. All rights reserved.
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....... If the spikes arrive at random times the changes in synaptic conductance are therefore stochastic and rapid during intense network activity. In comparison, sub-threshold intrinsic conductances vary smoothly in time. In the present study this discrepancy is investigated using two conductance-based models: a (1...... 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...
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
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.
Voltage-dependent amplification of synaptic inputs in respiratory motoneurones
Enríquez Denton, M; Wienecke, J; Zhang, M; Hultborn, H; Kirkwood, P A
2012-01-01
The role of persistent inward currents (PICs) in cat respiratory motoneurones (phrenic inspiratory and thoracic expiratory) was investigated by studying the voltage-dependent amplification of central respiratory drive potentials (CRDPs), recorded intracellularly, with action potentials blocked with the local anaesthetic derivative, QX-314. Decerebrate unanaesthetized or barbiturate-anaesthetized preparations were used. In expiratory motoneurones, plateau potentials were observed in the decerebrates, but not under anaesthesia. For phrenic motoneurones, no plateau potentials were observed in either state (except in one motoneurone after the abolition of the respiratory drive by means of a medullary lesion), but all motoneurones showed voltage-dependent amplification of the CRDPs, over a wide range of membrane potentials, too wide to result mainly from PIC activation. The measurements of the amplification were restricted to the phase of excitation, thus excluding the inhibitory phase. Amplification was found to be greatest for the smallest CRDPs in the lowest resistance motoneurones and was reduced or abolished following intracellular injection of the NMDA channel blocker, MK-801. Plateau potentials were readily evoked in non-phrenic cervical motoneurones in the same (decerebrate) preparations. We conclude that the voltage-dependent amplification of synaptic excitation in phrenic motoneurones is mainly the result of NMDA channel modulation rather than the activation of Ca2+ channel mediated PICs, despite phrenic motoneurones being strongly immunohistochemically labelled for CaV1.3 channels. The differential PIC activation in different motoneurones, all of which are CaV1.3 positive, leads us to postulate that the descending modulation of PICs is more selective than has hitherto been believed. PMID:22495582
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.
Closas, Pau; Guillamon, Antoni
2017-12-01
This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain's connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes.
Streeter, K.A.; Baker-Herman, T.L.
2014-01-01
Phrenic motor neurons receive rhythmic synaptic inputs throughout life. Since even brief disruption in phrenic neural activity is detrimental to life, on-going neural activity may play a key role in shaping phrenic motor output. To test the hypothesis that spinal mechanisms sense and respond to reduced phrenic activity, anesthetized, ventilated rats received micro-injections of procaine in the C2 ventrolateral funiculus (VLF) to transiently (~30 min) block axon conduction in bulbospinal axons...
Directory of Open Access Journals (Sweden)
Max F K Happel
Full Text Available Robust perception of auditory objects over a large range of sound intensities is a fundamental feature of the auditory system. However, firing characteristics of single neurons across the entire auditory system, like the frequency tuning, can change significantly with stimulus intensity. Physiological correlates of level-constancy of auditory representations hence should be manifested on the level of larger neuronal assemblies or population patterns. In this study we have investigated how information of frequency and sound level is integrated on the circuit-level in the primary auditory cortex (AI of the Mongolian gerbil. We used a combination of pharmacological silencing of corticocortically relayed activity and laminar current source density (CSD analysis. Our data demonstrate that with increasing stimulus intensities progressively lower frequencies lead to the maximal impulse response within cortical input layers at a given cortical site inherited from thalamocortical synaptic inputs. We further identified a temporally precise intercolumnar synaptic convergence of early thalamocortical and horizontal corticocortical inputs. Later tone-evoked activity in upper layers showed a preservation of broad tonotopic tuning across sound levels without shifts towards lower frequencies. Synaptic integration within corticocortical circuits may hence contribute to a level-robust representation of auditory information on a neuronal population level in the auditory cortex.
Directory of Open Access Journals (Sweden)
Gilles Erwann Martin
2015-07-01
Full Text Available It is widely accepted that long-lasting changes of synaptic strength in the nucleus accumbens, a brain region involved in drug reward, mediate acute and chronic effects of alcohol. However, our understanding of the mechanisms underlying the effects of alcohol on synaptic plasticity is limited by the fact that the nucleus accumbens receives glutamatergic inputs from distinct brain regions (e.g. the prefrontal cortex, the amygdala and the hippocampus, each region providing different information (e.g. spatial, emotional and cognitive. Combining whole-cell patch-clamp recordings and the optogenetic technique, we examined synaptic plasticity, and its regulation by alcohol, at cortical, hippocampal and amygdala inputs in fresh slices of mouse tissue. We showed that the origin of synaptic inputs determines the basic properties of glutamatergic synaptic transmission, the expression of spike-timing dependent long-term depression (tLTD and long-term potentiation (tLTP and their regulation by alcohol. While we observed both tLTP and tLTD at amygadala and hippocampal synapses, we showed that cortical inputs only undergo tLTD. Functionally, we provide evidence that acute EtOH has little effects on higher order information coming from the prefrontal cortex (PFCx, while severely impacting the ability of emotional and contextual information to induce long-lasting changes of synaptic strength.
Orientation selectivity of synaptic input to neurons in mouse and cat primary visual cortex.
Tan, Andrew Y Y; Brown, Brandon D; Scholl, Benjamin; Mohanty, Deepankar; Priebe, Nicholas J
2011-08-24
Primary visual cortex (V1) is the site at which orientation selectivity emerges in mammals: visual thalamus afferents to V1 respond equally to all stimulus orientations, whereas their target V1 neurons respond selectively to stimulus orientation. The emergence of orientation selectivity in V1 has long served as a model for investigating cortical computation. Recent evidence for orientation selectivity in mouse V1 opens cortical computation to dissection by genetic and imaging tools, but also raises two essential questions: (1) How does orientation selectivity in mouse V1 neurons compare with that in previously described species? (2) What is the synaptic basis for orientation selectivity in mouse V1? A comparison of orientation selectivity in mouse and in cat, where such measures have traditionally been made, reveals that orientation selectivity in mouse V1 is weaker than in cat V1, but that spike threshold plays a similar role in narrowing selectivity between membrane potential and spike rate. To uncover the synaptic basis for orientation selectivity, we made whole-cell recordings in vivo from mouse V1 neurons, comparing neuronal input selectivity-based on membrane potential, synaptic excitation, and synaptic inhibition-to output selectivity based on spiking. We found that a neuron's excitatory and inhibitory inputs are selective for the same stimulus orientations as is its membrane potential response, and that inhibitory selectivity is not broader than excitatory selectivity. Inhibition has different dynamics than excitation, adapting more rapidly. In neurons with temporally modulated responses, the timing of excitation and inhibition was different in mice and cats.
Graupner, Michael; Reyes, Alex D
2013-09-18
Correlations in the spiking activity of neurons have been found in many regions of the cortex under multiple experimental conditions and are postulated to have important consequences for neural population coding. While there is a large body of extracellular data reporting correlations of various strengths, the subthreshold events underlying the origin and magnitude of signal-independent correlations (called noise or spike count correlations) are unknown. Here we investigate, using intracellular recordings, how synaptic input correlations from shared presynaptic neurons translate into membrane potential and spike-output correlations. Using a pharmacologically activated thalamocortical slice preparation, we perform simultaneous recordings from pairs of layer IV neurons in the auditory cortex of mice and measure synaptic potentials/currents, membrane potentials, and spiking outputs. We calculate cross-correlations between excitatory and inhibitory inputs to investigate correlations emerging from the network. We furthermore evaluate membrane potential correlations near resting potential to study how excitation and inhibition combine and affect spike-output correlations. We demonstrate directly that excitation is correlated with inhibition thereby partially canceling each other and resulting in weak membrane potential and spiking correlations between neurons. Our data suggest that cortical networks are set up to partially cancel correlations emerging from the connections between neurons. This active decorrelation is achieved because excitation and inhibition closely track each other. Our results suggest that the numerous shared presynaptic inputs do not automatically lead to increased spiking correlations.
Streeter, K.A.; Baker-Herman, T.L.
2014-01-01
Phrenic motor neurons receive rhythmic synaptic inputs throughout life. Since even brief disruption in phrenic neural activity is detrimental to life, on-going neural activity may play a key role in shaping phrenic motor output. To test the hypothesis that spinal mechanisms sense and respond to reduced phrenic activity, anesthetized, ventilated rats received micro-injections of procaine in the C2 ventrolateral funiculus (VLF) to transiently (~30 min) block axon conduction in bulbospinal axons from medullary respiratory neurons that innervate one phrenic motor pool; during procaine injections, contralateral phrenic neural activity was maintained. Once axon conduction resumed, a prolonged increase in phrenic burst amplitude was observed in the ipsilateral phrenic nerve, demonstrating inactivity-induced phrenic motor facilitation (iPMF). Inhibition of tumor necrosis factor alpha (TNFα) and atypical PKC (aPKC) activity in spinal segments containing the phrenic motor nucleus impaired ipsilateral iPMF, suggesting a key role for spinal TNFα and aPKC in iPMF following unilateral axon conduction block. A small phrenic burst amplitude facilitation was also observed contralateral to axon conduction block, indicating crossed spinal phrenic motor facilitation (csPMF). csPMF was independent of spinal TNFα and aPKC. Ipsilateral iPMF and csPMF following unilateral withdrawal of phrenic synaptic inputs were associated with proportional increases in phrenic responses to chemoreceptor stimulation (hypercapnia), suggesting iPMF and csPMF increase phrenic dynamic range. These data suggest that local, spinal mechanisms sense and respond to reduced synaptic inputs to phrenic motor neurons. We hypothesize that iPMF and csPMF may represent compensatory mechanisms that assure adequate motor output is maintained in a physiological system in which prolonged inactivity ends life. PMID:24681155
Streeter, K A; Baker-Herman, T L
2014-06-01
Phrenic motor neurons receive rhythmic synaptic inputs throughout life. Since even brief disruption in phrenic neural activity is detrimental to life, on-going neural activity may play a key role in shaping phrenic motor output. To test the hypothesis that spinal mechanisms sense and respond to reduced phrenic activity, anesthetized, ventilated rats received micro-injections of procaine in the C2 ventrolateral funiculus (VLF) to transiently (~30min) block axon conduction in bulbospinal axons from medullary respiratory neurons that innervate one phrenic motor pool; during procaine injections, contralateral phrenic neural activity was maintained. Once axon conduction resumed, a prolonged increase in phrenic burst amplitude was observed in the ipsilateral phrenic nerve, demonstrating inactivity-induced phrenic motor facilitation (iPMF). Inhibition of tumor necrosis factor alpha (TNFα) and atypical PKC (aPKC) activity in spinal segments containing the phrenic motor nucleus impaired ipsilateral iPMF, suggesting a key role for spinal TNFα and aPKC in iPMF following unilateral axon conduction block. A small phrenic burst amplitude facilitation was also observed contralateral to axon conduction block, indicating crossed spinal phrenic motor facilitation (csPMF). csPMF was independent of spinal TNFα and aPKC. Ipsilateral iPMF and csPMF following unilateral withdrawal of phrenic synaptic inputs were associated with proportional increases in phrenic responses to chemoreceptor stimulation (hypercapnia), suggesting iPMF and csPMF increase phrenic dynamic range. These data suggest that local, spinal mechanisms sense and respond to reduced synaptic inputs to phrenic motor neurons. We hypothesize that iPMF and csPMF may represent compensatory mechanisms that assure adequate motor output is maintained in a physiological system in which prolonged inactivity ends life. Copyright © 2014 Elsevier Inc. All rights reserved.
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.
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.
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.
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.
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Wei Ling Lim
2016-08-01
Full Text Available Maternal dexamethasone (DEX; a glucocorticoid receptor agonist exposure delays pubertal onset and alters reproductive behaviour in the adult offspring. However, little is known whether maternal DEX exposure affects the offspring’s reproductive function by disrupting the gonadotropin-releasing hormone (GnRH neuronal function in the brain. Therefore, this study determined the exposure of maternal DEX on the GnRH neuronal spine development and synaptic cluster inputs to GnRH neurons using transgenic rats expressing enhanced green fluorescent protein (EGFP under the control of GnRH promoter. Pregnant females were administered with DEX (0.1mg/kg or vehicle (VEH, water daily during gestation day 13-20. Confocal imaging was used to examine the spine density of EGFP-GnRH neurons by three-dimensional rendering and synaptic cluster inputs to EGFP-GnRH neurons by synapsin I immunohistochemistry on postnatal day 0 (P0 males. The spine morphology and number on GnRH neurons did not change between the P0 males following maternal DEX and VEH treatment. The number of synaptic clusters within the organum vasculosum of the lamina terminalis (OVLT was decreased by maternal DEX exposure in P0 males. Furthermore, the number and levels of synaptic cluster inputs in close apposition with GnRH neurons was decreased following maternal DEX exposure in the OVLT region of P0 males. In addition, the post synaptic marker molecule, post-synaptic density 95 was observed in GnRH neurons following both DEX and VEH treatment. These results suggest that maternal DEX exposure alters neural afferent inputs to GnRH neurons during early postnatal stage, which could lead to reproductive dysfunction during adulthood.
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
Noise Analysis of Single-Ended Input Differential Amplifier using Stochastic Differential Equation
Tarun Kumar Rawat; Abhirup Lahiri; Ashish Gupta
2008-01-01
In this paper, we analyze the effect of noise in a single- ended input differential amplifier working at high frequencies. Both extrinsic and intrinsic noise are analyzed using time domain method employing techniques from stochastic calculus. Stochastic differential equations are used to obtain autocorrelation functions of the output noise voltage and other solution statistics like mean and variance. The analysis leads to important design implications and suggests changes in the device parame...
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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.
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
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.
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
Characterization of memory states of the Preisach operator with stochastic inputs
Energy Technology Data Exchange (ETDEWEB)
Amann, A. [Department of Applied Mathematics, University College Cork (Ireland); Brokate, M. [Zentrum Mathematik, Technische Universitaet Muenchen (Germany); McCarthy, S. [Department of Applied Mathematics, University College Cork (Ireland); Rachinskii, D., E-mail: d.rachinskii@ucc.ie [Department of Applied Mathematics, University College Cork (Ireland); Temnov, G. [Department of Mathematics, University College Cork (Ireland)
2012-05-01
The Preisach operator with inputs defined by a Markov process x{sup 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{sub 0} in the limit r{yields}{infinity} of infinitely long input history x{sup t}, t{sub 0}-r{<=}t{<=}t{sub 0}? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (m{sub k},M{sub k}) of elements from the monotone sequences of the local minimum input values m{sub k} and the local maximum input values M{sub 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{sup 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
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.
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.
Variance-based sensitivity indices for stochastic models with correlated inputs
Energy Technology Data Exchange (ETDEWEB)
Kala, Zdeněk [Brno University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics Veveří St. 95, ZIP 602 00, Brno (Czech Republic)
2015-03-10
The goal of this article is the formulation of the principles of one of the possible strategies in implementing correlation between input random variables so as to be usable for algorithm development and the evaluation of Sobol’s sensitivity analysis. With regard to the types of stochastic computational models, which are commonly found in structural mechanics, an algorithm was designed for effective use in conjunction with Monte Carlo methods. Sensitivity indices are evaluated for all possible permutations of the decorrelation procedures for input parameters. The evaluation of Sobol’s sensitivity coefficients is illustrated on an example in which a computational model was used for the analysis of the resistance of a steel bar in tension with statistically dependent input geometric characteristics.
Variance-based sensitivity indices for stochastic models with correlated inputs
International Nuclear Information System (INIS)
Kala, Zdeněk
2015-01-01
The goal of this article is the formulation of the principles of one of the possible strategies in implementing correlation between input random variables so as to be usable for algorithm development and the evaluation of Sobol’s sensitivity analysis. With regard to the types of stochastic computational models, which are commonly found in structural mechanics, an algorithm was designed for effective use in conjunction with Monte Carlo methods. Sensitivity indices are evaluated for all possible permutations of the decorrelation procedures for input parameters. The evaluation of Sobol’s sensitivity coefficients is illustrated on an example in which a computational model was used for the analysis of the resistance of a steel bar in tension with statistically dependent input geometric characteristics
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
Wang, Jianhui; Liu, Zhi; Chen, C L Philip; Zhang, Yun
2017-10-12
Hysteresis exists ubiquitously in physical actuators. Besides, actuator failures/faults may also occur in practice. Both effects would deteriorate the transient tracking performance, and even trigger instability. In this paper, we consider the problem of compensating for actuator failures and input hysteresis by proposing a fuzzy control scheme for stochastic nonlinear systems. Compared with the existing research on stochastic nonlinear uncertain systems, it is found that how to guarantee a prescribed transient tracking performance when taking into account actuator failures and hysteresis simultaneously also remains to be answered. Our proposed control scheme is designed on the basis of the fuzzy logic system and backstepping techniques for this purpose. It is proven that all the signals remain bounded and the tracking error is ensured to be within a preestablished bound with the failures of hysteretic actuator. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.
A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
Babuška, Ivo; Nobile, Fabio; Tempone, Raul
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.
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.
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
Virtanen, Mari A.; Lacoh, Claudia Marvine; Fiumelli, Hubert; Kosel, Markus; Tyagarajan, Shiva; de Roo, Mathias; Vutskits, Laszlo
2018-01-01
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.
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.
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
Directory of Open Access Journals (Sweden)
Meng Liu
2013-10-01
Full Text Available A stochastic single-species population system in a polluted environment with impulsive toxicant input is proposed and studied. Sufficient conditions for extinction, non-persistence in the mean, strong persistence in the mean and stochastic permanence of the population are established. The threshold between strong persistence in the mean and extinction is obtained. Some simulation figures are introduced to illustrate the main results.
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
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
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.
International Nuclear Information System (INIS)
Kaplan, D; Margaret Millings, M
2006-01-01
Stochastic modeling is being used in the Performance Assessment program to provide a probabilistic estimate of the range of risk that buried waste may pose. The objective of this task was to provide early guidance for stochastic modelers for the selection of the range and distribution (e.g., normal, log-normal) of distribution coefficients (K d ) and solubility values (K sp ) to be used in modeling subsurface radionuclide transport in E- and Z-Area on the Savannah River Site (SRS). Due to the project's schedule, some modeling had to be started prior to collecting the necessary field and laboratory data needed to fully populate these models. For the interim, the project will rely on literature values and some statistical analyses of literature data as inputs. Based on statistical analyses of some literature sorption tests, the following early guidance was provided: (1) Set the range to an order of magnitude for radionuclides with K d values >1000 mL/g and to a factor of two for K d values of sp values -6 M and to a factor of two for K d values of >10 -6 M. This decision is based on the literature. (3) The distribution of K d values with a mean >1000 mL/g will be log-normally distributed. Those with a K d value <1000 mL/g will be assigned a normal distribution. This is based on statistical analysis of non-site-specific data. Results from on-going site-specific field/laboratory research involving E-Area sediments will supersede this guidance; these results are expected in 2007
Directory of Open Access Journals (Sweden)
Jinyan Cao
2018-04-01
Full Text Available Steroid sex hormones and biological sex influence how the brain regulates motivated behavior, reward, and sensorimotor function in both normal and pathological contexts. Investigations into the underlying neural mechanisms have targeted the striatal brain regions, including the caudate–putamen, nucleus accumbens core (AcbC, and shell. These brain regions are of particular interest to neuroendocrinologists given that they express membrane-associated but not nuclear estrogen receptors, and also the well-established role of the sex steroid hormone 17β-estradiol (estradiol in modulating striatal dopamine systems. Indeed, output neurons of the striatum, the medium spiny neurons (MSNs, exhibit estradiol sensitivity and sex differences in electrophysiological properties. Here, we review sex differences in rat MSN glutamatergic synaptic input and intrinsic excitability across striatal regions, including evidence for estradiol-mediated sexual differentiation in the nucleus AcbC. In prepubertal animals, female MSNs in the caudate–putamen exhibit a greater intrinsic excitability relative to male MSNs, but no sex differences are detected in excitatory synaptic input. Alternatively, female MSNs in the nucleus AcbC exhibit increased excitatory synaptic input relative to male MSNs, but no sex differences in intrinsic excitability were detected. Increased excitatory synaptic input onto female MSNs in the nucleus AcbC is abolished after masculinizing estradiol or testosterone exposure during the neonatal critical period. No sex differences are detected in MSNs in prepubertal nucleus accumbens shell. Thus, despite possessing the same neuron type, striatal regions exhibit heterogeneity in sex differences in MSN electrophysiological properties, which likely contribute to the sex differences observed in striatal function.
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Jesus A Garrido
2013-05-01
Full Text Available The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at mossy fiber to granule cell synapses regulated the delay of the first spike and the weight at mossy fiber and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First spike timing was regulated with millisecond precision and the number of spikes ranged from 0 to 3. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.
Energy Technology Data Exchange (ETDEWEB)
Jin, Shi, E-mail: sjin@wisc.edu [Department of Mathematics, University of Wisconsin-Madison, Madison, WI 53706 (United States); Institute of Natural Sciences, Department of Mathematics, MOE-LSEC and SHL-MAC, Shanghai Jiao Tong University, Shanghai 200240 (China); Lu, Hanqing, E-mail: hanqing@math.wisc.edu [Department of Mathematics, University of Wisconsin-Madison, Madison, WI 53706 (United States)
2017-04-01
In this paper, we develop an Asymptotic-Preserving (AP) stochastic Galerkin scheme for the radiative heat transfer equations with random inputs and diffusive scalings. In this problem the random inputs arise due to uncertainties in cross section, initial data or boundary data. We use the generalized polynomial chaos based stochastic Galerkin (gPC-SG) method, which is combined with the micro–macro decomposition based deterministic AP framework in order to handle efficiently the diffusive regime. For linearized problem we prove the regularity of the solution in the random space and consequently the spectral accuracy of the gPC-SG method. We also prove the uniform (in the mean free path) linear stability for the space-time discretizations. Several numerical tests are presented to show the efficiency and accuracy of proposed scheme, especially in the diffusive regime.
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.
Norris, Brian J; Weaver, Adam L; Wenning, Angela; García, Paul S; Calabrese, Ronald L
2007-11-01
The central pattern generator (CPG) for heartbeat in medicinal leeches consists of seven identified pairs of segmental heart interneurons and one unidentified pair. Four of the identified pairs and the unidentified pair of interneurons make inhibitory synaptic connections with segmental heart motor neurons. The CPG produces a side-to-side asymmetric pattern of intersegmental coordination among ipsilateral premotor interneurons corresponding to a similarly asymmetric fictive motor pattern in heart motor neurons, and asymmetric constriction pattern of the two tubular hearts, synchronous and peristaltic. Using extracellular recordings from premotor interneurons and voltage-clamp recordings of ipsilateral segmental motor neurons in 69 isolated nerve cords, we assessed the strength and dynamics of premotor inhibitory synaptic output onto the entire ensemble of heart motor neurons and the associated conduction delays in both coordination modes. We conclude that premotor interneurons establish a stereotypical pattern of intersegmental synaptic connectivity, strengths, and dynamics that is invariant across coordination modes, despite wide variations among preparations. These data coupled with a previous description of the temporal pattern of premotor interneuron activity and relative phasing of motor neuron activity in the two coordination modes enable a direct assessment of how premotor interneurons through their temporal pattern of activity and their spatial pattern of synaptic connectivity, strengths, and dynamics coordinate segmental motor neurons into a functional pattern of activity.
L{sup 1} group consensus of multi-agent systems with switching topologies and stochastic inputs
Energy Technology Data Exchange (ETDEWEB)
Shang, Yilun, E-mail: shylmath@hotmail.com [Institute for Cyber Security, University of Texas at San Antonio, TX 78249 (United States); SUTD-MIT International Design Center, Singapore University of Technology and Design, Singapore 138682 (Singapore)
2013-10-01
Understanding how interacting subsystems of an overall system lead to cluster/group consensus is a key issue in the investigation of multi-agent systems. In this Letter, we study the L{sup 1} group consensus problem of discrete-time multi-agent systems with external stochastic inputs. Based on ergodicity theory and matrix analysis, L{sup 1} group consensus criteria are obtained for multi-agent systems with switching topologies. Some numerical examples are provided to illustrate the effectiveness and feasibility of the theoretical results.
On stabilization of linear systems with stochastic disturbances and input saturation
Stoorvogel, A.A.; Weiland, S.; Saberi, A.
2004-01-01
It is well-known that for linear systems internal asymptotic stability implies external stability in the sense that when the external input is in Lp then also the state will be in Lp. However, for the control of linear systems with saturation where the controlled system is nonlinear this implication
Effect of stimulation on the input parameters of stochastic leaky integrate-and-fire neuronal model
Czech Academy of Sciences Publication Activity Database
Lánský, Petr; Šanda, Pavel; He, J.
2010-01-01
Roč. 104, 3-4 (2010), s. 160-166 ISSN 0928-4257 R&D Projects: GA MŠk(CZ) LC554; GA AV ČR(CZ) IAA101120604 Institutional research plan: CEZ:AV0Z50110509 Keywords : membrane depolarization * input parameters * diffusion Subject RIV: BO - Biophysics Impact factor: 3.030, year: 2010
Norris, Brian J; Weaver, Adam L; Wenning, Angela; García, Paul S; Calabrese, Ronald L
2007-11-01
The central pattern generator (CPG) for heartbeat in leeches consists of seven identified pairs of segmental heart interneurons and one unidentified pair. Four of the identified pairs and the unidentified pair of interneurons make inhibitory synaptic connections with segmental heart motor neurons. The CPG produces a side-to-side asymmetric pattern of intersegmental coordination among ipsilateral premotor interneurons corresponding to a similarly asymmetric fictive motor pattern in heart motor neurons, and asymmetric constriction pattern of the two tubular hearts: synchronous and peristaltic. Using extracellular techniques, we recorded, in 61 isolated nerve cords, the activity of motor neurons in conjunction with the phase reference premotor heart interneuron, HN(4), and another premotor interneuron that allowed us to assess the coordination mode. These data were then coupled with a previous description of the temporal pattern of premotor interneuron activity in the two coordination modes to synthesize a global phase diagram for the known elements of the CPG and the entire motor neuron ensemble. These average data reveal the stereotypical side-to-side asymmetric patterns of intersegmental coordination among the motor neurons and show how this pattern meshes with the activity pattern of premotor interneurons. Analysis of animal-to-animal variability in this coordination indicates that the intersegmental phase progression of motor neuron activity in the midbody in the peristaltic coordination mode is the most stereotypical feature of the fictive motor pattern. Bilateral recordings from motor neurons corroborate the main features of the asymmetric motor pattern.
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…
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.
Directory of Open Access Journals (Sweden)
Weiwei Sun
2015-01-01
Full Text Available This paper presents H∞ excitation control design problem for power systems with input time delay and disturbances by using nonlinear Hamiltonian system theory. The impact of time delays introduced by remote signal transmission and processing in wide-area measurement system (WAMS is well considered. Meanwhile, the systems under investigation are disturbed by random fluctuation. First, under prefeedback technique, the power systems are described as a nonlinear Hamiltonian system. Then the H∞ excitation controller of generators connected to distant power systems with time delay and stochasticity is designed. Based on Lyapunov functional method, some sufficient conditions are proposed to guarantee the rationality and validity of the proposed control law. The closed-loop systems under the control law are asymptotically stable in mean square independent of the time delay. And we through a simulation of a two-machine power system prove the effectiveness of the results proposed in this paper.
'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)
Directory of Open Access Journals (Sweden)
Vassilis Kehayas
2014-03-01
Full Text Available Structural plasticity in the somatosensory cortex is maintained throughout life. In adult animals structural changes occur at the level of dendritic spines and axonal boutons in response to alterations in sensory experience. The causal relationship between synaptic activity and structural changes, however, is not clear. Hebbian-plasticity models predict that synapses will be stabilized at the nodes of neuronal networks that display high levels of coincident activity. Here, we aim at studying the effects of a targeted increase in coincident activity between segregated inputs on pyramidal cell synapses of the mouse somatosensory barrel cortex in vivo. Supragranular layers of the barrel cortex receive anatomically distinct inputs from two thalamic pathways: the ‘lemniscal’ pathway that originates in the ventral posteromedial (VPM nucleus and projects in a whisker-specific fashion to the barrel columns, and the ‘paralemniscal’ pathway that originates in the posteromedial (POm nucleus and projects to the cortex in a non-specific manner. Previous work from our lab shows that rhythmic (8Hz whisker stimulation-evoked LTP (RWS-LTP in layer (L 2/3 pyramidal cells relies on the combined activity of lemniscal and paralemniscal pathways. Here, we targeted ChR2 expression to POm neurons using AAV-mediated gene transfer in order to optically control the activity of those inputs. As a first step, we show that photostimulation of the POm nucleus induces NMDA-dependent, sub-threshold responses in L2/3 pyramidal cells similar to those that are required for the induction of RWS-LTP. In addition, simultaneous photostimulation of POm neurons together with whisker stimulation at low frequencies (1Hz can also elicit LTP, suggesting that coincident lemniscal and paralemniscal input can drive LTP induction. Next, we combined the ChR2-tdTomato expression in POm neurons with sparse AAV-mediated eGFP expression in L2/3 pyramidal cells in order to study the effects
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
Singh, B.; Henneberger, C.; Betances, D.; Arevalo, M.A.; Rodriguez-Tebar, A.; Meier, J.C.; Grantyn, R.
2006-01-01
Cultured neurons from bdnf-/- mice display reduced densities of synaptic terminals, although in vivo these deficits are small or absent. Here we aimed at clarifying the local responses to postsynaptic brain-derived neurotrophic factor (BDNF). To this end, solitary enhanced green fluorescent protein (EGFP)-labeled hippocampal neurons from bdnf-/- mice were compared with bdnf-/- neurons after transfection with BDNF, bdnf-/- neurons after transient exposure to exogenous BDNF, and bdnf+/+ neurons...
Directory of Open Access Journals (Sweden)
Séverine M. Sigoillot
2015-02-01
Full Text Available Precise patterns of connectivity are established by different types of afferents on a given target neuron, leading to well-defined and non-overlapping synaptic territories. What regulates the specific characteristics of each type of synapse, in terms of number, morphology, and subcellular localization, remains to be understood. Here, we show that the signaling pathway formed by the secreted complement C1Q-related protein C1QL1 and its receptor, the adhesion-GPCR brain angiogenesis inhibitor 3 (BAI3, controls the stereotyped pattern of connectivity established by excitatory afferents on cerebellar Purkinje cells. The BAI3 receptor modulates synaptogenesis of both parallel fiber and climbing fiber afferents. The restricted and timely expression of its ligand C1QL1 in inferior olivary neurons ensures the establishment of the proper synaptic territory for climbing fibers. Given the broad expression of C1QL and BAI proteins in the developing mouse brain, our study reveals a general mechanism contributing to the formation of a functional brain.
DEFF Research Database (Denmark)
Berg, Rune W.; Ditlevsen, Susanne
2013-01-01
When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and non-reproducible so one is therefore often restricted to single trial data. Here, we introduce means of estimating the inhibition and ex...... close to soma (recording site). Though our data is in current-clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab....... 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....... 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...
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.
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...
Input significance analysis: feature selection through synaptic ...
African Journals Online (AJOL)
Connection Weights (CW) and Garson's Algorithm (GA) and the classifier selected ... from the UCI Machine Learning Repository and executed in an online ... connectionist systems; evolving fuzzy neural network; connection weights; Garson's
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
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.
Directory of Open Access Journals (Sweden)
Feifei Bian
2017-01-01
Full Text Available A stochastic prey-predator system in a polluted environment with Beddington-DeAngelis functional response is proposed and analyzed. Firstly, for the system with white noise perturbation, by analyzing the limit system, the existence of boundary periodic solutions and positive periodic solutions is proved and the sufficient conditions for the existence of boundary periodic solutions and positive periodic solutions are derived. And then for the stochastic system, by introducing Markov regime switching, the sufficient conditions for extinction or persistence of such system are obtained. Furthermore, we proved that the system is ergodic and has a stationary distribution when the concentration of toxicant is a positive constant. Finally, two examples with numerical simulations are carried out in order to illustrate the theoretical results.
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.
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.
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.
Suprathreshold stochastic resonance in neural processing tuned by correlation.
Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng
2011-07-01
Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.
International Nuclear Information System (INIS)
Pandey, Anil Kumar; Saroha, Kartik; Patel, C.D.; Bal, C.S.; Kumar, Rakesh
2016-01-01
Administration of diuretics increases the urine output to clear radioactive urine from kidneys and bladder. Hence post-diuretic pelvic PET/CT scan enhances the probability of detection of abdomino-pelvic tumor. However, it causes discomfort in patients and has some side effects also. Application of supra threshold stochastic resonance (SSR) image processing algorithm on Pre-diuretic PET/CT scan may also increase the probability of detection of these tumors. Amount of noise and threshold are two variable parameters that effect the final image quality. This study was conducted to investigate the effect of these two variable parameters on the detection of abdomen-pelvic tumor
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.
Synaptic control of motoneuronal excitability
DEFF Research Database (Denmark)
Rekling, J C; Funk, G D; Bayliss, D A
2000-01-01
important in understanding the transformation of neural activity to motor behavior. Here, we review recent studies on the control of motoneuronal excitability, focusing on synaptic and cellular properties. We first present a background description of motoneurons: their development, anatomical organization......, and membrane properties, both passive and active. We then describe the general anatomical organization of synaptic input to motoneurons, followed by a description of the major transmitter systems that affect motoneuronal excitability, including ligands, receptor distribution, pre- and postsynaptic actions...... and norepinephrine, and neuropeptides, as well as the glutamate and GABA acting at metabotropic receptors, modulate motoneuronal excitability through pre- and postsynaptic actions. Acting principally via second messenger systems, their actions converge on common effectors, e.g., leak K(+) current, cationic inward...
Gao, Feng-Yin; Kang, Yan-Mei; Chen, Xi; Chen, Guanrong
2018-05-01
This paper reveals the effect of fractional Gaussian noise with Hurst exponent H ∈(1 /2 ,1 ) on the information capacity of a general nonlinear neuron model with binary signal input. The fGn and its corresponding fractional Brownian motion exhibit long-range, strong-dependent increments. It extends standard Brownian motion to many types of fractional processes found in nature, such as the synaptic noise. In the paper, for the subthreshold binary signal, sufficient conditions are given based on the "forbidden interval" theorem to guarantee the occurrence of stochastic resonance, while for the suprathreshold binary signal, the simulated results show that additive fGn with Hurst exponent H ∈(1 /2 ,1 ) could increase the mutual information or bits count. The investigation indicated that the synaptic noise with the characters of long-range dependence and self-similarity might be the driving factor for the efficient encoding and decoding of the nervous system.
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.
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
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...... treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility...... 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...
Synaptic Plasticity and Nociception
Institute of Scientific and Technical Information of China (English)
ChenJianguo
2004-01-01
Synaptic plasticity is one of the fields that progresses rapidly and has a lot of success in neuroscience. The two major types of synaptie plasticity: long-term potentiation ( LTP and long-term depression (LTD are thought to be the cellular mochanisms of learning and memory. Recently, accumulating evidence suggests that, besides serving as a cellular model for learning and memory, the synaptic plasticity involves in other physiological or pathophysiological processes, such as the perception of pain and the regulation of cardiovascular system. This minireview will focus on the relationship between synaptic plasticity and nociception.
Foulis, David J.; Pulmannov, Sylvia
2018-04-01
Using a representation theorem of Erik Alfsen, Frederic Schultz, and Erling Størmer for special JB-algebras, we prove that a synaptic algebra is norm complete (i.e., Banach) if and only if it is isomorphic to the self-adjoint part of a Rickart C∗-algebra. Also, we give conditions on a Banach synaptic algebra that are equivalent to the condition that it is isomorphic to the self-adjoint part of an AW∗-algebra. Moreover, we study some relationships between synaptic algebras and so-called generalized Hermitian algebras.
The Corticohippocampal Circuit, Synaptic Plasticity, and Memory
Basu, Jayeeta; Siegelbaum, Steven A.
2015-01-01
Synaptic plasticity serves as a cellular substrate for information storage in the central nervous system. The entorhinal cortex (EC) and hippocampus are interconnected brain areas supporting basic cognitive functions important for the formation and retrieval of declarative memories. Here, we discuss how information flow in the EC–hippocampal loop is organized through circuit design. We highlight recently identified corticohippocampal and intrahippocampal connections and how these long-range and local microcircuits contribute to learning. This review also describes various forms of activity-dependent mechanisms that change the strength of corticohippocampal synaptic transmission. A key point to emerge from these studies is that patterned activity and interaction of coincident inputs gives rise to associational plasticity and long-term regulation of information flow. Finally, we offer insights about how learning-related synaptic plasticity within the corticohippocampal circuit during sensory experiences may enable adaptive behaviors for encoding spatial, episodic, social, and contextual memories. PMID:26525152
Stochastic Control - External Models
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad
2005-01-01
This note is devoted to control of stochastic systems described in discrete time. We are concerned with external descriptions or transfer function model, where we have a dynamic model for the input output relation only (i.e.. no direct internal information). The methods are based on LTI systems...
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
Alteration of synaptic connectivity of oligodendrocyte precursor cells following demyelination
Sahel, Aurélia; Ortiz, Fernando C.; Kerninon, Christophe; Maldonado, Paloma P.; Angulo, María Cecilia; Nait-Oumesmar, Brahim
2015-01-01
Oligodendrocyte precursor cells (OPCs) are a major source of remyelinating oligodendrocytes in demyelinating diseases such as Multiple Sclerosis (MS). While OPCs are innervated by unmyelinated axons in the normal brain, the fate of such synaptic contacts after demyelination is still unclear. By combining electrophysiology and immunostainings in different transgenic mice expressing fluorescent reporters, we studied the synaptic innervation of OPCs in the model of lysolecithin (LPC)-induced demyelination of corpus callosum. Synaptic innervation of reactivated OPCs in the lesion was revealed by the presence of AMPA receptor-mediated synaptic currents, VGluT1+ axon-OPC contacts in 3D confocal reconstructions and synaptic junctions observed by electron microscopy. Moreover, 3D confocal reconstructions of VGluT1 and NG2 immunolabeling showed the existence of glutamatergic axon-OPC contacts in post-mortem MS lesions. Interestingly, patch-clamp recordings in LPC-induced lesions demonstrated a drastic decrease in spontaneous synaptic activity of OPCs early after demyelination that was not caused by an impaired conduction of compound action potentials. A reduction in synaptic connectivity was confirmed by the lack of VGluT1+ axon-OPC contacts in virtually all rapidly proliferating OPCs stained with EdU (50-ethynyl-20-deoxyuridine). At the end of the massive proliferation phase in lesions, the proportion of innervated OPCs rapidly recovers, although the frequency of spontaneous synaptic currents did not reach control levels. In conclusion, our results demonstrate that newly-generated OPCs do not receive synaptic inputs during their active proliferation after demyelination, but gain synapses during the remyelination process. Hence, glutamatergic synaptic inputs may contribute to inhibit OPC proliferation and might have a physiopathological relevance in demyelinating disorders. PMID:25852473
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
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert
2016-01-01
Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650
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 ...
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.
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.
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...
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.
Inverse stochastic resonance in networks of spiking neurons.
Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J
2017-07-01
Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.
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
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
Synaptic Plasticity, Dementia and Alzheimer Disease.
Skaper, Stephen D; Facci, Laura; Zusso, Morena; Giusti, Pietro
2017-01-01
shafts undergo dynamic changes in number, size and shape in response to variations in hormonal status, developmental stage, and changes in afferent input. It is perhaps not unexpected that loss of spine density may be linked to cognitive and memory impairment in AD, although the underlying mechanism(s) remain uncertain. This article aims to present a critical overview of current knowledge on the bases of synaptic dysfunction in neurodegenerative diseases, with a focus on AD, and will cover amyloid- and nonamyloid- driven mechanisms. We will consider also emerging data dealing with potential therapeutic approaches for ameliorating the cognitive and memory deficits associated with these disorders. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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 ...
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...
International Nuclear Information System (INIS)
Klauder, J.R.
1983-01-01
The author provides an introductory survey to stochastic quantization in which he outlines this new approach for scalar fields, gauge fields, fermion fields, and condensed matter problems such as electrons in solids and the statistical mechanics of quantum spins. (Auth.)
STOCHASTIC ASSESSMENT OF NIGERIAN STOCHASTIC ...
African Journals Online (AJOL)
eobe
STOCHASTIC ASSESSMENT OF NIGERIAN WOOD FOR BRIDGE DECKS ... abandoned bridges with defects only in their decks in both rural and urban locations can be effectively .... which can be seen as the detection of rare physical.
MAGUKs: multifaceted synaptic organizers.
Won, Sehoon; Levy, Jon M; Nicoll, Roger A; Roche, Katherine W
2017-04-01
The PSD-95 family of proteins, known as MAGUKs, have long been recognized to be central building blocks of the PSD. They are categorized as scaffolding proteins, which link surface-expressed receptors to the intracellular signaling molecules. Although the four members of the PSD-95 family (PSD-95, PSD-93, SAP102, and SAP97) have many shared roles in regulating synaptic function, recent studies have begun to delineate specific binding partners and roles in plasticity. In the current review, we will highlight the conserved and unique roles of these proteins. Published by Elsevier Ltd.
Asymmetric Temporal Integration of Layer 4 and Layer 2/3 Inputs in Visual Cortex
Hang, Giao B.; Dan, Yang
2010-01-01
Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices...
SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING.
Zhang, Wenhao; Li, Hanyu; Yang, Minda; Mesgarani, Nima
2016-03-01
A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.
Chang, Mou-Hsiung
2015-01-01
The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations, quantum Markov semigrou...
Stochastic models for the in silico simulation of synaptic processes
Bracciali, Andrea; Brunelli, Marcello; Cataldo, Enrico; Degano, Pierpaolo
2008-01-01
Background Research in life sciences is benefiting from a large availability of formal description techniques and analysis methodologies. These allow both the phenomena investigated to be precisely modeled and virtual experiments to be performed in silico. Such experiments may result in easier, faster, and satisfying approximations of their in vitro/vivo counterparts. A promising approach is represented by the study of biological phenomena as a collection of interactive entities through proce...
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.
Synaptic electronics: materials, devices and applications
International Nuclear Information System (INIS)
Kuzum, Duygu; Yu, Shimeng; Philip Wong, H-S
2013-01-01
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. (topical review)
Sensory optimization by stochastic tuning.
Jurica, Peter; Gepshtein, Sergei; Tyukin, Ivan; van Leeuwen, Cees
2013-10-01
Individually, visual neurons are each selective for several aspects of stimulation, such as stimulus location, frequency content, and speed. Collectively, the neurons implement the visual system's preferential sensitivity to some stimuli over others, manifested in behavioral sensitivity functions. We ask how the individual neurons are coordinated to optimize visual sensitivity. We model synaptic plasticity in a generic neural circuit and find that stochastic changes in strengths of synaptic connections entail fluctuations in parameters of neural receptive fields. The fluctuations correlate with uncertainty of sensory measurement in individual neurons: The higher the uncertainty the larger the amplitude of fluctuation. We show that this simple relationship is sufficient for the stochastic fluctuations to steer sensitivities of neurons toward a characteristic distribution, from which follows a sensitivity function observed in human psychophysics and which is predicted by a theory of optimal allocation of receptive fields. The optimal allocation arises in our simulations without supervision or feedback about system performance and independently of coupling between neurons, making the system highly adaptive and sensitive to prevailing stimulation. PsycINFO Database Record (c) 2013 APA, all rights reserved.
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....... This gives rise to Generalised Semi-Markov Decision Processes for which few analytical techniques are available. We restrict delays on output actions to be exponentially distributed while still admitting real-time constraints on the quality binders. This facilitates developing analytical techniques based...
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
International Nuclear Information System (INIS)
Bisognano, J.; Leemann, C.
1982-03-01
Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron
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
STOCHASTIC METHODS IN RISK ANALYSIS
Directory of Open Access Journals (Sweden)
Vladimíra OSADSKÁ
2017-06-01
Full Text Available In this paper, we review basic stochastic methods which can be used to extend state-of-the-art deterministic analytical methods for risk analysis. We can conclude that the standard deterministic analytical methods highly depend on the practical experience and knowledge of the evaluator and therefore, the stochastic methods should be introduced. The new risk analysis methods should consider the uncertainties in input values. We present how large is the impact on the results of the analysis solving practical example of FMECA with uncertainties modelled using Monte Carlo sampling.
Interregional synaptic maps among engram cells underlie memory formation.
Choi, Jun-Hyeok; Sim, Su-Eon; Kim, Ji-Il; Choi, Dong Il; Oh, Jihae; Ye, Sanghyun; Lee, Jaehyun; Kim, TaeHyun; Ko, Hyoung-Gon; Lim, Chae-Seok; Kaang, Bong-Kiun
2018-04-27
Memory resides in engram cells distributed across the brain. However, the site-specific substrate within these engram cells remains theoretical, even though it is generally accepted that synaptic plasticity encodes memories. We developed the dual-eGRASP (green fluorescent protein reconstitution across synaptic partners) technique to examine synapses between engram cells to identify the specific neuronal site for memory storage. We found an increased number and size of spines on CA1 engram cells receiving input from CA3 engram cells. In contextual fear conditioning, this enhanced connectivity between engram cells encoded memory strength. CA3 engram to CA1 engram projections strongly occluded long-term potentiation. These results indicate that enhanced structural and functional connectivity between engram cells across two directly connected brain regions forms the synaptic correlate for memory formation. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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
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
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.
Verisokin, Andrey Yu.; Postnov, Dmitry E.; Verveyko, Darya V.; Brazhe, Alexey R.
2018-04-01
The most abundant non-neuronal cells in the brain, astrocytes, populate all parts of the central nervous system (CNS). Astrocytic calcium activity ranging from subcellular sparkles to intercellular waves is believed to be the key to a plethora of regulatory pathways in the central nervous system from synaptic plasticity to blood flow regulation. Modeling of the calcium wave initiation and transmission and their spatiotemporal dynamics is therefore an important step stone in understanding the crucial cogs of cognition. Astrocytes are active sensors of ongoing neuronal and synaptic activity, and neurotransmitters diffusing from the synaptic cleft make a strong impact on the astrocytic activity. Here we propose a model describing the patterns of calcium wave formation at a single cell level and discuss the interplay between astrocyte shape the calcium waves dynamics driven by local stochastic surges of glutamate simulating synaptic activity.
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
Hardwiring of fine synaptic layers in the zebrafish visual pathway
Directory of Open Access Journals (Sweden)
Taylor Michael R
2008-12-01
Full Text Available Abstract Background Neuronal connections are often arranged in layers, which are divided into sublaminae harboring synapses with similar response properties. It is still debated how fine-grained synaptic layering is established during development. Here we investigated two stratified areas of the zebrafish visual pathway, the inner plexiform layer (IPL of the retina and the neuropil of the optic tectum, and determined if activity is required for their organization. Results The IPL of 5-day-old zebrafish larvae is composed of at least nine sublaminae, comprising the connections between different types of amacrine, bipolar, and ganglion cells (ACs, BCs, GCs. These sublaminae were distinguished by their expression of cell type-specific transgenic fluorescent reporters and immunohistochemical markers, including protein kinase Cβ (PKC, parvalbumin (Parv, zrf3, and choline acetyltransferase (ChAT. In the tectum, four retinal input layers abut a laminated array of neurites of tectal cells, which differentially express PKC and Parv. We investigated whether these patterns were affected by experimental disruptions of retinal activity in developing fish. Neither elimination of light inputs by dark rearing, nor a D, L-amino-phosphono-butyrate-induced reduction in the retinal response to light onset (but not offset altered IPL or tectal lamination. Moreover, thorough elimination of chemical synaptic transmission with Botulinum toxin B left laminar synaptic arrays intact. Conclusion Our results call into question a role for activity-dependent mechanisms – instructive light signals, balanced on and off BC activity, Hebbian plasticity, or a permissive role for synaptic transmission – in the synaptic stratification we examined. We propose that genetically encoded cues are sufficient to target groups of neurites to synaptic layers in this vertebrate visual system.
Readily releasable pool of synaptic vesicles measured at single synaptic contacts.
Trigo, Federico F; Sakaba, Takeshi; Ogden, David; Marty, Alain
2012-10-30
To distinguish between different models of vesicular release in brain synapses, it is necessary to know the number of vesicles of transmitter that can be released immediately at individual synapses by a high-calcium stimulus, the readily releasable pool (RRP). We used direct stimulation by calcium uncaging at identified, single-site inhibitory synapses to investigate the statistics of vesicular release and the size of the RRP. Vesicular release, detected as quantal responses in the postsynaptic neuron, showed an unexpected stochastic variation in the number of quanta from stimulus to stimulus at high intracellular calcium, with a mean of 1.9 per stimulus and a maximum of three or four. The results provide direct measurement of the RRP at single synaptic sites. They are consistent with models in which release proceeds from a small number of vesicle docking sites with an average occupancy around 0.7.
DEFF Research Database (Denmark)
Guzulaitis, Robertas; Hounsgaard, Jorn
2017-01-01
channels. Intrinsic outward rectification facilitates spiking by focusing synaptic depolarization near threshold for action potentials. By direct recording of synaptic currents, we also show that motoneurons are activated by out-of-phase peaks in excitation and inhibition during network activity, whereas......Regular firing in spinal motoneurons of red-eared turtles (Trachemys scripta elegans, either sex) evoked by steady depolarization at rest is replaced by irregular firing during functional network activity. The transition caused by increased input conductance and synaptic fluctuations in membrane...... potential was suggested to originate from intense concurrent inhibition and excitation. We show that the conductance increase in motoneurons during functional network activity is mainly caused by intrinsic outward rectification near threshold for action potentials by activation of voltage and Ca2+ gated K...
Secreted factors as synaptic organizers.
Johnson-Venkatesh, Erin M; Umemori, Hisashi
2010-07-01
A critical step in synaptic development is the differentiation of presynaptic and postsynaptic compartments. This complex process is regulated by a variety of secreted factors that serve as synaptic organizers. Specifically, fibroblast growth factors, Wnts, neurotrophic factors and various other intercellular signaling molecules are proposed to regulate presynaptic and/or postsynaptic differentiation. Many of these factors appear to function at both the neuromuscular junction and in the central nervous system, although the specific function of the molecules differs between the two. Here we review secreted molecules that organize the synaptic compartments and discuss how these molecules shape synaptic development, focusing on mammalian in vivo systems. Their critical role in shaping a functional neural circuit is underscored by their possible link to a wide range of neurological and psychiatric disorders both in animal models and by mutations identified in human patients. © The Authors (2010). Journal Compilation © Federation of European Neuroscience Societies and Blackwell Publishing Ltd.
Location-dependent excitatory synaptic interactions in pyramidal neuron dendrites.
Directory of Open Access Journals (Sweden)
Bardia F Behabadi
Full Text Available Neocortical pyramidal neurons (PNs receive thousands of excitatory synaptic contacts on their basal dendrites. Some act as classical driver inputs while others are thought to modulate PN responses based on sensory or behavioral context, but the biophysical mechanisms that mediate classical-contextual interactions in these dendrites remain poorly understood. We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. Distal excitation lowers the local spike threshold for more proximal inputs, while having little effect on peak responses at the soma. In contrast, proximal excitation lowers the threshold, but also substantially increases the gain of distally-driven responses. Our findings support the view that PN basal dendrites possess significant analog computing capabilities, and suggest that the diverse forms of nonlinear response modulation seen in the neocortex, including uni-modal, cross-modal, and attentional effects, could depend in part on pathway-specific biases in the spatial distribution of excitatory synaptic contacts onto PN basal dendritic arbors.
Synaptic consolidation across multiple timescales
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Lorric Ziegler
2014-03-01
Full Text Available The brain is bombarded with a continuous stream of sensory events, but retains only a small subset in memory. The selectivity of memory formation prevents our memory from being overloaded with irrelevant items that would rapidly bring the brain to its storage limit; moreover, selectivity also prevents overwriting previously formed memories with new ones. Memory formation in the hippocampus, as well as in other brain regions, is thought to be linked to changes in the synaptic connections between neurons. In this view, sensory events imprint traces at the level of synapses that reflect potential memory items. The question of memory selectivity can therefore be reformulated as follows: what are the reasons and conditions that some synaptic traces fade away whereas others are consolidated and persist? Experimentally, changes in synaptic strength induced by 'Hebbian' protocols fade away over a few hours (early long-term potentiation or e-LTP, unless these changes are consolidated. The experiments and conceptual theory of synaptic tagging and capture (STC provide a mechanistic explanation for the processes involved in consolidation. This theory suggests that the initial trace of synaptic plasticity sets a tag at the synapse, which then serves as a marker for potential consolidation of the changes in synaptic efficacy. The actual consolidation processes, transforming e-LTP into late LTP (l-LTP, require the capture of plasticity-related proteins (PRP. We translate the above conceptual model into a compact computational model that accounts for a wealth of in vitro data including experiments on cross-tagging, tag-resetting and depotentiation. A central ingredient is that synaptic traces are described with several variables that evolve on different time scales. Consolidation requires the transmission of information from a 'fast' synaptic trace to a 'slow' one through a 'write' process, including the formation of tags and the production of PRP for the
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)
Directory of Open Access Journals (Sweden)
Romanu Ekaterini
2006-01-01
Full Text Available This article shows the similarities between Claude Debussy’s and Iannis Xenakis’ philosophy of music and work, in particular the formers Jeux and the latter’s Metastasis and the stochastic works succeeding it, which seem to proceed parallel (with no personal contact to what is perceived as the evolution of 20th century Western music. Those two composers observed the dominant (German tradition as outsiders, and negated some of its elements considered as constant or natural by "traditional" innovators (i.e. serialists: the linearity of musical texture, its form and rhythm.
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.
Mechanisms of Synaptic Alterations in a Neuroinflammation Model of Autism
2015-10-01
inhibitory presynaptic input in the cortex of MIA offspring To determine if the altered number, shape and dynamic proper- ties of spines are...affects synaptic function in the cortex . We performed whole-cell voltage -clamp recordings from layer 2 pyramidal neurons in the somatosensory cortex ...highly dynamic struc- tures with new spines forming and others disappearing on a time scale of minutes (Dailey and Smith, 1996; Dunaevsky et al., 1999
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.
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 ...
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 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
Synaptic Effects of Electric Fields
Rahman, Asif
Learning and sensory processing in the brain relies on the effective transmission of information across synapses. The strength and efficacy of synaptic transmission is modifiable through training and can be modulated with noninvasive electrical brain stimulation. Transcranial electrical stimulation (TES), specifically, induces weak intensity and spatially diffuse electric fields in the brain. Despite being weak, electric fields modulate spiking probability and the efficacy of synaptic transmission. These effects critically depend on the direction of the electric field relative to the orientation of the neuron and on the level of endogenous synaptic activity. TES has been used to modulate a wide range of neuropsychiatric indications, for various rehabilitation applications, and cognitive performance in diverse tasks. How can a weak and diffuse electric field, which simultaneously polarizes neurons across the brain, have precise changes in brain function? Designing therapies to maximize desired outcomes and minimize undesired effects presents a challenging problem. A series of experiments and computational models are used to define the anatomical and functional factors leading to specificity of TES. Anatomical specificity derives from guiding current to targeted brain structures and taking advantage of the direction-sensitivity of neurons with respect to the electric field. Functional specificity originates from preferential modulation of neuronal networks that are already active. Diffuse electric fields may recruit connected brain networks involved in a training task and promote plasticity along active synaptic pathways. In vitro, electric fields boost endogenous synaptic plasticity and raise the ceiling for synaptic learning with repeated stimulation sessions. Synapses undergoing strong plasticity are preferentially modulated over weak synapses. Therefore, active circuits that are involved in a task could be more susceptible to stimulation than inactive circuits
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, the stochastic flow of mappings generated by a Feller convolution semigroup on a compact metric space is studied. This kind of flow is the generalization of superprocesses of stochastic flows and stochastic diffeomorphism induced by the strong solutions of stochastic differential equations.
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...
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.
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.
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
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.
Synaptic communication between neurons and NG2+ cells.
Paukert, Martin; Bergles, Dwight E
2006-10-01
Chemical synaptic transmission provides the basis for much of the rapid signaling that occurs within neuronal networks. However, recent studies have provided compelling evidence that synapses are not used exclusively for communication between neurons. Physiological and anatomical studies indicate that a distinct class of glia known as NG2(+) cells also forms direct synaptic junctions with both glutamatergic and GABAergic neurons. Glutamatergic signaling can influence intracellular Ca(2+) levels in NG2(+) cells by activating Ca(2+) permeable AMPA receptors, and these inputs can be potentiated through high frequency stimulation. Although the significance of this highly differentiated form of communication remains to be established, these neuro-glia synapses might enable neurons to influence rapidly the behavior of this ubiquitous class of glial progenitors.
Phonology: An Emergent Consequence of Memory Constraints and Sensory Input.
Lacerda, Francisco
2003-01-01
Presents a theoretical model that attempts to account for the early stages of language acquisition in terms of interaction between biological constraints and input characteristics. Notes that the model uses the implications of stochastic representations of the sensory input in a volatile and limited memory. Argues that phonological structure is a…
On the adaptivity gap of stochastic orienteering
Bansal, N.; Nagarajan, V.
2013-01-01
The 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
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
On the adaptivity gap of stochastic orienteering
Bansal, N.; Nagarajan, V.; Lee, J.; Vygen, J.
2014-01-01
The input to the stochastic orienteering problem [14] consists of a budget B and metric (V,d) where each vertex v¿¿¿V has a job with a deterministic reward and a random processing time (drawn from a known distribution). The processing times are independent across vertices. The goal is to obtain a
On the adaptivity gap of stochastic orienteering
Bansal, N.; Nagarajan, V.
2015-01-01
The input to the stochastic orienteering problem (Gupta et al. in SODA, pp 1522–1538, 2012) consists of a budget B and metric (V, d) where each vertex(Formula presented.) has a job with a deterministic reward and a random processing time (drawn from a known distribution). The processing times are
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...
Stochastic tools in turbulence
Lumey, John L
2012-01-01
Stochastic Tools in Turbulence discusses the available mathematical tools to describe stochastic vector fields to solve problems related to these fields. The book deals with the needs of turbulence in relation to stochastic vector fields, particularly, on three-dimensional aspects, linear problems, and stochastic model building. The text describes probability distributions and densities, including Lebesgue integration, conditional probabilities, conditional expectations, statistical independence, lack of correlation. The book also explains the significance of the moments, the properties of the
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
Directory of Open Access Journals (Sweden)
Stephen G Turney
Full Text Available During mammalian development, neuromuscular junctions and some other postsynaptic cells transition from multiple- to single-innervation as synaptic sites are exchanged between different axons. It is unclear whether one axon invades synaptic sites to drive off other inputs or alternatively axons expand their territory in response to sites vacated by other axons. Here we show that soon-to-be-eliminated axons rapidly reverse fate and grow to occupy vacant sites at a neuromuscular junction after laser removal of a stronger input. This reversal supports the idea that axons take over sites that were previously vacated. Indeed, during normal development we observed withdrawal followed by takeover. The stimulus for axon growth is not postsynaptic cell inactivity because axons grow into unoccupied sites even when target cells are functionally innervated. These results demonstrate competition at the synaptic level and enable us to provide a conceptual framework for understanding this form of synaptic plasticity.
Turney, Stephen G; Lichtman, Jeff W
2012-01-01
During mammalian development, neuromuscular junctions and some other postsynaptic cells transition from multiple- to single-innervation as synaptic sites are exchanged between different axons. It is unclear whether one axon invades synaptic sites to drive off other inputs or alternatively axons expand their territory in response to sites vacated by other axons. Here we show that soon-to-be-eliminated axons rapidly reverse fate and grow to occupy vacant sites at a neuromuscular junction after laser removal of a stronger input. This reversal supports the idea that axons take over sites that were previously vacated. Indeed, during normal development we observed withdrawal followed by takeover. The stimulus for axon growth is not postsynaptic cell inactivity because axons grow into unoccupied sites even when target cells are functionally innervated. These results demonstrate competition at the synaptic level and enable us to provide a conceptual framework for understanding this form of synaptic plasticity.
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…
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
Communicating Optimized Decision Input from Stochastic Turbulence Forecasts
National Research Council Canada - National Science Library
Szczes, Jeanne R
2008-01-01
.... It demonstrates the methodology and importance of incorporating ambiguity, the uncertainty in forecast uncertainty, into the decision making process using the Taijitu method to estimate ambiguity...
Choice of input fields in stochastic finite elements
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1996-01-01
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......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...... 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...
Choice of input fields in stochastic finite elements
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1999-01-01
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......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...... 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...
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 ...
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
Robust short-term memory without synaptic learning.
Directory of Open Access Journals (Sweden)
Samuel Johnson
Full Text Available 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.
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.
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.
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.
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...
International Nuclear Information System (INIS)
Wan Li; Zhou Qinghua
2007-01-01
The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem
Wan, Li; Zhou, Qinghua
2007-10-01
The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.
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
Synaptic integration of transplanted interneuron progenitor cells into native cortical networks.
Howard, MacKenzie A; Baraban, Scott C
2016-08-01
Interneuron-based cell transplantation is a powerful method to modify network function in a variety of neurological disorders, including epilepsy. Whether new interneurons integrate into native neural networks in a subtype-specific manner is not well understood, and the therapeutic mechanisms underlying interneuron-based cell therapy, including the role of synaptic inhibition, are debated. In this study, we tested subtype-specific integration of transplanted interneurons using acute cortical brain slices and visualized patch-clamp recordings to measure excitatory synaptic inputs, intrinsic properties, and inhibitory synaptic outputs. Fluorescently labeled progenitor cells from the embryonic medial ganglionic eminence (MGE) were used for transplantation. At 5 wk after transplantation, MGE-derived parvalbumin-positive (PV+) interneurons received excitatory synaptic inputs, exhibited mature interneuron firing properties, and made functional synaptic inhibitory connections to native pyramidal cells that were comparable to those of native PV+ interneurons. These findings demonstrate that MGE-derived PV+ interneurons functionally integrate into subtype-appropriate physiological niches within host networks following transplantation. Copyright © 2016 the American Physiological Society.
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.
Importance of vesicle release stochasticity in neuro-spike communication.
Ramezani, Hamideh; Akan, Ozgur B
2017-07-01
Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.
Synaptic vesicle distribution by conveyor belt.
Moughamian, Armen J; Holzbaur, Erika L F
2012-03-02
The equal distribution of synaptic vesicles among synapses along the axon is critical for robust neurotransmission. Wong et al. show that the continuous circulation of synaptic vesicles throughout the axon driven by molecular motors ultimately yields this even distribution. Copyright Â© 2012 Elsevier Inc. All rights reserved.
Spontaneous Vesicle Recycling in the Synaptic Bouton
Directory of Open Access Journals (Sweden)
Sven eTruckenbrodt
2014-12-01
Full Text Available The trigger for synaptic vesicle exocytosis is Ca2+, which enters the synaptic bouton following action potential stimulation. However, spontaneous release of neurotransmitter also occurs in the absence of stimulation in virtually all synaptic boutons. It has long been thought that this represents exocytosis driven by fluctuations in local Ca2+ levels. The vesicles responding to these fluctuations are thought to be the same ones that release upon stimulation, albeit potentially triggered by different Ca2+ sensors. This view has been challenged by several recent works, which have suggested that spontaneous release is driven by a separate pool of synaptic vesicles. Numerous articles appeared during the last few years in support of each of these hypotheses, and it has been challenging to bring them into accord. We speculate here on the origins of this controversy, and propose a solution that is related to developmental effects. Constitutive membrane traffic, needed for the biogenesis of vesicles and synapses, is responsible for high levels of spontaneous membrane fusion in young neurons, probably independent of Ca2+. The vesicles releasing spontaneously in such neurons are not related to other synaptic vesicle pools and may represent constitutively releasing vesicles (CRVs rather than bona fide synaptic vesicles. In mature neurons, constitutive traffic is much dampened, and the few remaining spontaneous release events probably represent bona fide spontaneously releasing synaptic vesicles (SRSVs responding to Ca2+ fluctuations, along with a handful of CRVs that participate in synaptic vesicle turnover.
Active hippocampal networks undergo spontaneous synaptic modification.
Directory of Open Access Journals (Sweden)
Masako Tsukamoto-Yasui
Full Text Available The brain is self-writable; as the brain voluntarily adapts itself to a changing environment, the neural circuitry rearranges its functional connectivity by referring to its own activity. How the internal activity modifies synaptic weights is largely unknown, however. Here we report that spontaneous activity causes complex reorganization of synaptic connectivity without any external (or artificial stimuli. Under physiologically relevant ionic conditions, CA3 pyramidal cells in hippocampal slices displayed spontaneous spikes with bistable slow oscillations of membrane potential, alternating between the so-called UP and DOWN states. The generation of slow oscillations did not require fast synaptic transmission, but their patterns were coordinated by local circuit activity. In the course of generating spontaneous activity, individual neurons acquired bidirectional long-lasting synaptic modification. The spontaneous synaptic plasticity depended on a rise in intracellular calcium concentrations of postsynaptic cells, but not on NMDA receptor activity. The direction and amount of the plasticity varied depending on slow oscillation patterns and synapse locations, and thus, they were diverse in a network. Once this global synaptic refinement occurred, the same neurons now displayed different patterns of spontaneous activity, which in turn exhibited different levels of synaptic plasticity. Thus, active networks continuously update their internal states through ongoing synaptic plasticity. With computational simulations, we suggest that with this slow oscillation-induced plasticity, a recurrent network converges on a more specific state, compared to that with spike timing-dependent plasticity alone.
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...
Synaptic plasticity in drug reward circuitry.
Winder, Danny G; Egli, Regula E; Schramm, Nicole L; Matthews, Robert T
2002-11-01
Drug addiction is a major public health issue worldwide. The persistence of drug craving coupled with the known recruitment of learning and memory centers in the brain has led investigators to hypothesize that the alterations in glutamatergic synaptic efficacy brought on by synaptic plasticity may play key roles in the addiction process. Here we review the present literature, examining the properties of synaptic plasticity within drug reward circuitry, and the effects that drugs of abuse have on these forms of plasticity. Interestingly, multiple forms of synaptic plasticity can be induced at glutamatergic synapses within the dorsal striatum, its ventral extension the nucleus accumbens, and the ventral tegmental area, and at least some of these forms of plasticity are regulated by behaviorally meaningful administration of cocaine and/or amphetamine. Thus, the present data suggest that regulation of synaptic plasticity in reward circuits is a tractable candidate mechanism underlying aspects of addiction.
Elitism and Stochastic Dominance
Bazen, Stephen; Moyes, Patrick
2011-01-01
Stochastic dominance has typically been used with a special emphasis on risk and inequality reduction something captured by the concavity of the utility function in the expected utility model. We claim that the applicability of the stochastic dominance approach goes far beyond risk and inequality measurement provided suitable adpations be made. We apply in the paper the stochastic dominance approach to the measurment of elitism which may be considered the opposite of egalitarianism. While the...
Singular stochastic differential equations
Cherny, Alexander S
2005-01-01
The authors introduce, in this research monograph on stochastic differential equations, a class of points termed isolated singular points. Stochastic differential equations possessing such points (called singular stochastic differential equations here) arise often in theory and in applications. However, known conditions for the existence and uniqueness of a solution typically fail for such equations. The book concentrates on the study of the existence, the uniqueness, and, what is most important, on the qualitative behaviour of solutions of singular stochastic differential equations. This is done by providing a qualitative classification of isolated singular points, into 48 possible types.
Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.
Directory of Open Access Journals (Sweden)
Brendan Chambers
2016-08-01
Full Text Available Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.
Spectrotemporal dynamics of auditory cortical synaptic receptive field plasticity.
Froemke, Robert C; Martins, Ana Raquel O
2011-09-01
The nervous system must dynamically represent sensory information in order for animals to perceive and operate within a complex, changing environment. Receptive field plasticity in the auditory cortex allows cortical networks to organize around salient features of the sensory environment during postnatal development, and then subsequently refine these representations depending on behavioral context later in life. Here we review the major features of auditory cortical receptive field plasticity in young and adult animals, focusing on modifications to frequency tuning of synaptic inputs. Alteration in the patterns of acoustic input, including sensory deprivation and tonal exposure, leads to rapid adjustments of excitatory and inhibitory strengths that collectively determine the suprathreshold tuning curves of cortical neurons. Long-term cortical plasticity also requires co-activation of subcortical neuromodulatory control nuclei such as the cholinergic nucleus basalis, particularly in adults. Regardless of developmental stage, regulation of inhibition seems to be a general mechanism by which changes in sensory experience and neuromodulatory state can remodel cortical receptive fields. We discuss recent findings suggesting that the microdynamics of synaptic receptive field plasticity unfold as a multi-phase set of distinct phenomena, initiated by disrupting the balance between excitation and inhibition, and eventually leading to wide-scale changes to many synapses throughout the cortex. These changes are coordinated to enhance the representations of newly-significant stimuli, possibly for improved signal processing and language learning in humans. Copyright © 2011 Elsevier B.V. All rights reserved.
Neuromodulation, development and synaptic plasticity.
Foehring, R C; Lorenzon, N M
1999-03-01
We discuss parallels in the mechanisms underlying use-dependent synaptic plasticity during development and long-term potentiation (LTP) and long-term depression (LTD) in neocortical synapses. Neuromodulators, such as norepinephrine, serotonin, and acetylcholine have also been implicated in regulating both developmental plasticity and LTP/LTD. There are many potential levels of interaction between neuromodulators and plasticity. Ion channels are substrates for modulation in many cell types. We discuss examples of modulation of voltage-gated Ca2+ channels and Ca(2+)-dependent K+ channels and the consequences for neocortical pyramidal cell firing behaviour. At the time when developmental plasticity is most evident in rat cortex, the substrate for modulation is changing as the densities and relative proportions of various ion channels types are altered during ontogeny. We discuss examples of changes in K+ and Ca2+ channels and the consequence for modulation of neuronal activity.
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.
Feedforward inhibition and synaptic scaling--two sides of the same coin?
Keck, Christian; Savin, Cristina; Lücke, Jörg
2012-01-01
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.
Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?
Lücke, Jörg
2012-01-01
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. PMID:22457610
Zhang, Xiaoyu; Ju, Han; Penney, Trevor B; VanDongen, Antonius M J
2017-01-01
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.
Jefferys, John; Fox, John; Jiruska, Premysl; Kronberg, Greg; Miranda, Dolores; Ruiz-Nuño, Ana; Bikson, Marom
2018-01-01
It is well established that non-synaptic mechanisms can generate electrographic seizures after blockade of synaptic function. We investigated the interaction of intact synaptic activity with non-synaptic mechanisms in the isolated CA1 region of rat hippocampal slices using the 'elevated-K+' model of epilepsy. Elevated K+ ictal bursts share waveform features with other models of electrographic seizures, including non-synaptic models where chemical synaptic transmission is suppressed, such as t...
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.
A heterogeneous stochastic FEM framework for elliptic PDEs
International Nuclear Information System (INIS)
Hou, Thomas Y.; Liu, Pengfei
2015-01-01
We introduce a new concept of sparsity for the stochastic elliptic operator −div(a(x,ω)∇(⋅)), which reflects the compactness of its inverse operator in the stochastic direction and allows for spatially heterogeneous stochastic structure. This new concept of sparsity motivates a heterogeneous stochastic finite element method (HSFEM) framework for linear elliptic equations, which discretizes the equations using the heterogeneous coupling of spatial basis with local stochastic basis to exploit the local stochastic structure of the solution space. We also provide a sampling method to construct the local stochastic basis for this framework using the randomized range finding techniques. The resulting HSFEM involves two stages and suits the multi-query setting: in the offline stage, the local stochastic structure of the solution space is identified; in the online stage, the equation can be efficiently solved for multiple forcing functions. An online error estimation and correction procedure through Monte Carlo sampling is given. Numerical results for several problems with high dimensional stochastic input are presented to demonstrate the efficiency of the HSFEM in the online stage
International Nuclear Information System (INIS)
Kimlinger, J.R.; Plechaty, E.F.
1982-01-01
The TART code is a Monte Carlo neutron/photon transport code that is only on the CRAY computer. All the input cards for the TART code are listed, and definitions for all input parameters are given. The execution and limitations of the code are described, and input for two sample problems are given
Synaptic Correlates of Low-Level Perception in V1.
Gerard-Mercier, Florian; Carelli, Pedro V; Pananceau, Marc; Troncoso, Xoana G; Frégnac, Yves
2016-04-06
The computational role of primary visual cortex (V1) in low-level perception remains largely debated. A dominant view assumes the prevalence of higher cortical areas and top-down processes in binding information across the visual field. Here, we investigated the role of long-distance intracortical connections in form and motion processing by measuring, with intracellular recordings, their synaptic impact on neurons in area 17 (V1) of the anesthetized cat. By systematically mapping synaptic responses to stimuli presented in the nonspiking surround of V1 receptive fields, we provide the first quantitative characterization of the lateral functional connectivity kernel of V1 neurons. Our results revealed at the population level two structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. First, subthreshold responses to oriented stimuli flashed in isolation in the nonspiking surround exhibited a geometric organization around the preferred orientation axis mirroring the psychophysical "association field" for collinear contour perception. Second, apparent motion stimuli, for which horizontal and feedforward synaptic inputs summed in-phase, evoked dominantly facilitatory nonlinear interactions, specifically during centripetal collinear activation along the preferred orientation axis, at saccadic-like speeds. This spatiotemporal integration property, which could constitute the neural correlate of a human perceptual bias in speed detection, suggests that local (orientation) and global (motion) information is already linked within V1. We propose the existence of a "dynamic association field" in V1 neurons, whose spatial extent and anisotropy are transiently updated and reshaped as a function of changes in the retinal flow statistics imposed during natural oculomotor exploration. The computational role of primary visual cortex in low-level perception remains debated. The expression of this "pop-out" perception is often assumed
A Markovian event-based framework for stochastic spiking neural networks.
Touboul, Jonathan D; Faugeras, Olivier D
2011-11-01
In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.
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)
Instantaneous stochastic perturbation theory
International Nuclear Information System (INIS)
Lüscher, Martin
2015-01-01
A form of stochastic perturbation theory is described, where the representative stochastic fields are generated instantaneously rather than through a Markov process. The correctness of the procedure is established to all orders of the expansion and for a wide class of field theories that includes all common formulations of lattice QCD.
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
Meyer, Joerg M.
2018-01-01
The contrary of stochastic independence splits up into two cases: pairs of events being favourable or being unfavourable. Examples show that both notions have quite unexpected properties, some of them being opposite to intuition. For example, transitivity does not hold. Stochastic dependence is also useful to explain cases of Simpson's paradox.
Bidirectional control of social hierarchy by synaptic efficacy in medial prefrontal cortex.
Wang, Fei; Zhu, Jun; Zhu, Hong; Zhang, Qi; Lin, Zhanmin; Hu, Hailan
2011-11-04
Dominance hierarchy has a profound impact on animals' survival, health, and reproductive success, but its neural circuit mechanism is virtually unknown. We found that dominance ranking in mice is transitive, relatively stable, and highly correlates among multiple behavior measures. Recording from layer V pyramidal neurons of the medial prefrontal cortex (mPFC) showed higher strength of excitatory synaptic inputs in mice with higher ranking, as compared with their subordinate cage mates. Furthermore, molecular manipulations that resulted in an increase and decrease in the synaptic efficacy in dorsal mPFC neurons caused an upward and downward movement in the social rank, respectively. These results provide direct evidence for mPFC's involvement in social hierarchy and suggest that social rank is plastic and can be tuned by altering synaptic strength in mPFC pyramidal cells.
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)
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...
Stabilization of memory States by stochastic facilitating synapses.
Miller, Paul
2013-12-06
Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.
Stochastic failure modelling of unidirectional composite ply failure
International Nuclear Information System (INIS)
Whiteside, M.B.; Pinho, S.T.; Robinson, P.
2012-01-01
Stochastic failure envelopes are generated through parallelised Monte Carlo Simulation of a physically based failure criteria for unidirectional carbon fibre/epoxy matrix composite plies. Two examples are presented to demonstrate the consequence on failure prediction of both statistical interaction of failure modes and uncertainty in global misalignment. Global variance-based Sobol sensitivity indices are computed to decompose the observed variance within the stochastic failure envelopes into contributions from physical input parameters. The paper highlights a selection of the potential advantages stochastic methodologies offer over the traditional deterministic approach.
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
Lectures on Dynamics of Stochastic Systems
Klyatskin, Valery I
2010-01-01
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. Models naturally render to statistical description, where random processes and fields express the input parameters and solutions. 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 the system and initial data. This book is a revised a
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
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.
Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy: A computational study.
Ruijter, B J; Hofmeijer, J; Meijer, H G E; van Putten, M J A M
2017-09-01
In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities. We used a mean field model comprising excitatory and inhibitory neurons, local synaptic connections, and input from thalamic afferents. Anoxic damage is modeled as aggravated short-term synaptic depression, with gradual recovery over many hours. Additionally, excitatory neurotransmission is potentiated, scaling with the severity of anoxic encephalopathy. Simulations were compared with continuous EEG recordings of 155 comatose patients after cardiac arrest. The simulations agree well with six common categories of EEG rhythms in postanoxic encephalopathy, including typical transitions in time. Plausible results were only obtained if excitatory synapses were more severely affected by short-term synaptic depression than inhibitory synapses. In postanoxic encephalopathy, the evolution of EEG patterns presumably results from gradual improvement of complete synaptic failure, where excitatory synapses are more severely affected than inhibitory synapses. The range of EEG patterns depends on the excitation-inhibition imbalance, probably resulting from long-term potentiation of excitatory neurotransmission. Our study is the first to relate microscopic synaptic dynamics in anoxic brain injury to both typical EEG observations and their evolution in time. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
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.
Directory of Open Access Journals (Sweden)
Jun Hu
Full Text Available Hypothalamic POMC neurons are required for glucose and energy homeostasis. POMC neurons have a wide synaptic connection with neurons both within and outside the hypothalamus, and their activity is controlled by a balance between excitatory and inhibitory synaptic inputs. Brain glucose-sensing plays an essential role in the maintenance of normal body weight and metabolism; however, the effect of glucose on synaptic transmission in POMC neurons is largely unknown. Here we identified three types of POMC neurons (EPSC(+, EPSC(-, and EPSC(+/- based on their glucose-regulated spontaneous excitatory postsynaptic currents (sEPSCs, using whole-cell patch-clamp recordings. Lowering extracellular glucose decreased the frequency of sEPSCs in EPSC(+ neurons, but increased it in EPSC(- neurons. Unlike EPSC(+ and EPSC(- neurons, EPSC(+/- neurons displayed a bi-phasic sEPSC response to glucoprivation. In the first phase of glucoprivation, both the frequency and the amplitude of sEPSCs decreased, whereas in the second phase, they increased progressively to the levels above the baseline values. Accordingly, lowering glucose exerted a bi-phasic effect on spontaneous action potentials in EPSC(+/- neurons. Glucoprivation decreased firing rates in the first phase, but increased them in the second phase. These data indicate that glucose induces distinct excitatory synaptic plasticity in different subpopulations of POMC neurons. This synaptic remodeling is likely to regulate the sensitivity of the melanocortin system to neuronal and hormonal signals.
Hu, Jun; Jiang, Lin; Low, Malcolm J.; Rui, Liangyou
2014-01-01
Hypothalamic POMC neurons are required for glucose and energy homeostasis. POMC neurons have a wide synaptic connection with neurons both within and outside the hypothalamus, and their activity is controlled by a balance between excitatory and inhibitory synaptic inputs. Brain glucose-sensing plays an essential role in the maintenance of normal body weight and metabolism; however, the effect of glucose on synaptic transmission in POMC neurons is largely unknown. Here we identified three types of POMC neurons (EPSC(+), EPSC(−), and EPSC(+/−)) based on their glucose-regulated spontaneous excitatory postsynaptic currents (sEPSCs), using whole-cell patch-clamp recordings. Lowering extracellular glucose decreased the frequency of sEPSCs in EPSC(+) neurons, but increased it in EPSC(−) neurons. Unlike EPSC(+) and EPSC(−) neurons, EPSC(+/−) neurons displayed a bi-phasic sEPSC response to glucoprivation. In the first phase of glucoprivation, both the frequency and the amplitude of sEPSCs decreased, whereas in the second phase, they increased progressively to the levels above the baseline values. Accordingly, lowering glucose exerted a bi-phasic effect on spontaneous action potentials in EPSC(+/−) neurons. Glucoprivation decreased firing rates in the first phase, but increased them in the second phase. These data indicate that glucose induces distinct excitatory synaptic plasticity in different subpopulations of POMC neurons. This synaptic remodeling is likely to regulate the sensitivity of the melanocortin system to neuronal and hormonal signals. PMID:25127258
Hu, Jun; Jiang, Lin; Low, Malcolm J; Rui, Liangyou
2014-01-01
Hypothalamic POMC neurons are required for glucose and energy homeostasis. POMC neurons have a wide synaptic connection with neurons both within and outside the hypothalamus, and their activity is controlled by a balance between excitatory and inhibitory synaptic inputs. Brain glucose-sensing plays an essential role in the maintenance of normal body weight and metabolism; however, the effect of glucose on synaptic transmission in POMC neurons is largely unknown. Here we identified three types of POMC neurons (EPSC(+), EPSC(-), and EPSC(+/-)) based on their glucose-regulated spontaneous excitatory postsynaptic currents (sEPSCs), using whole-cell patch-clamp recordings. Lowering extracellular glucose decreased the frequency of sEPSCs in EPSC(+) neurons, but increased it in EPSC(-) neurons. Unlike EPSC(+) and EPSC(-) neurons, EPSC(+/-) neurons displayed a bi-phasic sEPSC response to glucoprivation. In the first phase of glucoprivation, both the frequency and the amplitude of sEPSCs decreased, whereas in the second phase, they increased progressively to the levels above the baseline values. Accordingly, lowering glucose exerted a bi-phasic effect on spontaneous action potentials in EPSC(+/-) neurons. Glucoprivation decreased firing rates in the first phase, but increased them in the second phase. These data indicate that glucose induces distinct excitatory synaptic plasticity in different subpopulations of POMC neurons. This synaptic remodeling is likely to regulate the sensitivity of the melanocortin system to neuronal and hormonal signals.
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.
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
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.
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.
Hunt, Robert F.; Scheff, Stephen W.; Smith, Bret N.
2011-01-01
Functional plasticity of synaptic networks in the dentate gyrus has been implicated in the development of posttraumatic epilepsy and in cognitive dysfunction after traumatic brain injury, but little is known about potentially pathogenic changes in inhibitory circuits. We examined synaptic inhibition of dentate granule cells and excitability of surviving GABAergic hilar interneurons 8–13 weeks after cortical contusion brain injury in transgenic mice that express enhanced green fluorescent protein in a subpopulation of inhibitory neurons. Whole-cell voltage-clamp recordings in granule cells revealed a reduction in spontaneous and miniature IPSC frequency after head injury; no concurrent change in paired-pulse ratio was found in granule cells after paired electrical stimulation of the hilus. Despite reduced inhibitory input to granule cells, action potential and EPSC frequencies were increased in hilar GABA neurons from slices ipsilateral to the injury, versus those from control or contralateral slices. Further, increased excitatory synaptic activity was detected in hilar GABA neurons ipsilateral to the injury after glutamate photostimulation of either the granule cell or CA3 pyramidal cell layers. Together, these findings suggest that excitatory drive to surviving hilar GABA neurons is enhanced by convergent input from both pyramidal and granule cells, but synaptic inhibition of granule cells is not fully restored after injury. This rewiring of circuitry regulating hilar inhibitory neurons may reflect an important compensatory mechanism, but it may also contribute to network destabilization by increasing the relative impact of surviving individual interneurons in controlling granule cell excitability in the posttraumatic dentate gyrus. PMID:21543618
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.
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
Directory of Open Access Journals (Sweden)
Sandra J Kuhlman
2010-06-01
Full Text Available Spike-timing-dependent plasticity (STDP, a form of Hebbian plasticity, is inherently stabilizing. Whether and how GABAergic inhibition influences STDP is not well understood. Using a model neuron driven by converging inputs modifiable by STDP, we determined that a sufficient level of inhibition was critical to ensure that temporal coherence (correlation among presynaptic spike times of synaptic inputs, rather than initial strength or number of inputs within a pathway, controlled postsynaptic spike timing. Inhibition exerted this effect by preferentially reducing synaptic efficacy, the ability of inputs to evoke postsynaptic action potentials, of the less coherent inputs. In visual cortical slices, inhibition potently reduced synaptic efficacy at ages during but not before the critical period of ocular dominance (OD plasticity. Whole-cell recordings revealed that the amplitude of unitary IPSCs from parvalbumin positive (Pv+ interneurons to pyramidal neurons increased during the critical period, while the synaptic decay time-constant decreased. In addition, intrinsic properties of Pv+ interneurons matured, resulting in an increase in instantaneous firing rate. Our results suggest that maturation of inhibition in visual cortex ensures that the temporally coherent inputs (e.g. those from the open eye during monocular deprivation control postsynaptic spike times of binocular neurons, a prerequisite for Hebbian mechanisms to induce OD plasticity.
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.)
A new chance-constrained DEA model with birandom input and output data
Tavana, M.; Shiraz, R. K.; Hatami-Marbini, A.
2013-01-01
The purpose of conventional Data Envelopment Analysis (DEA) is to evaluate the performance of a set of firms or Decision-Making Units using deterministic input and output data. However, the input and output data in the real-life performance evaluation problems are often stochastic. The stochastic input and output data in DEA can be represented with random variables. Several methods have been proposed to deal with the random input and output data in DEA. In this paper, we propose a new chance-...
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
Common Input to Motor Units of Intrinsic and Extrinsic Hand Muscles During Two-Digit Object Hold
Winges, Sara A.; Kornatz, Kurt W.; Santello, Marco
2008-01-01
Anatomical and physiological evidence suggests that common input to motor neurons of hand muscles is an important neural mechanism for hand control. To gain insight into the synaptic input underlying the coordination of hand muscles, significant effort has been devoted to describing the distribution of common input across motor units of extrinsic muscles. Much less is known, however, about the distribution of common input to motor units belonging to different intrinsic muscles and to intrinsi...
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.
Introduction to stochastic calculus
Karandikar, Rajeeva L
2018-01-01
This book sheds new light on stochastic calculus, the branch of mathematics that is most widely applied in financial engineering and mathematical finance. The first book to introduce pathwise formulae for the stochastic integral, it provides a simple but rigorous treatment of the subject, including a range of advanced topics. The book discusses in-depth topics such as quadratic variation, Ito formula, and Emery topology. The authors briefly address continuous semi-martingales to obtain growth estimates and study solution of a stochastic differential equation (SDE) by using the technique of random time change. Later, by using Metivier–Pellumail inequality, the solutions to SDEs driven by general semi-martingales are discussed. The connection of the theory with mathematical finance is briefly discussed and the book has extensive treatment on the representation of martingales as stochastic integrals and a second fundamental theorem of asset pricing. Intended for undergraduate- and beginning graduate-level stud...
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.
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.
International Nuclear Information System (INIS)
Meyder, R.
1983-12-01
The code system SSYST-3 is designed to analyse the thermal and mechanical behaviour of a fuel rod during a LOCA. The report contains a complete input-list for all modules and several tested inputs for a LOCA analysis. (orig.)
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.
Approximating Preemptive Stochastic Scheduling
Megow Nicole; Vredeveld Tjark
2009-01-01
We present constant approximative policies for preemptive stochastic scheduling. We derive policies with a guaranteed performance ratio of 2 for scheduling jobs with release dates on identical parallel machines subject to minimizing the sum of weighted completion times. Our policies as well as their analysis apply also to the recently introduced more general model of stochastic online scheduling. The performance guarantee we give matches the best result known for the corresponding determinist...
The stochastic goodwill problem
Marinelli, Carlo
2003-01-01
Stochastic control problems related to optimal advertising under uncertainty are considered. In particular, we determine the optimal strategies for the problem of maximizing the utility of goodwill at launch time and minimizing the disutility of a stream of advertising costs that extends until the launch time for some classes of stochastic perturbations of the classical Nerlove-Arrow dynamics. We also consider some generalizations such as problems with constrained budget and with discretionar...
International Nuclear Information System (INIS)
Hueffel, H.
1990-01-01
After a brief review of the BRST formalism and of the Parisi-Wu stochastic quantization method we introduce the BRST stochastic quantization scheme. It allows the second quantization of constrained Hamiltonian systems in a manifestly gauge symmetry preserving way. The examples of the relativistic particle, the spinning particle and the bosonic string are worked out in detail. The paper is closed by a discussion on the interacting field theory associated to the relativistic point particle system. 58 refs. (Author)
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.
The development of the deterministic nonlinear PDEs in particle physics to stochastic case
Abdelrahman, Mahmoud A. E.; Sohaly, M. A.
2018-06-01
In the present work, accuracy method called, Riccati-Bernoulli Sub-ODE technique is used for solving the deterministic and stochastic case of the Phi-4 equation and the nonlinear Foam Drainage equation. Also, the control on the randomness input is studied for stability stochastic process solution.
Diacylglycerol kinases in the coordination of synaptic plasticity
Directory of Open Access Journals (Sweden)
Dongwon Lee
2016-08-01
Full Text Available 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 evidence 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.
Pesavento, Michael J; Pinto, David J
2012-11-01
Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.
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
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.
Bidirectional Classical Stochastic Processes with Measurements and Feedback
Hahne, G. E.
2005-01-01
A measurement on a quantum system is said to cause the "collapse" of the quantum state vector or density matrix. An analogous collapse occurs with measurements on a classical stochastic process. This paper addresses the question of describing the response of a classical stochastic process when there is feedback from the output of a measurement to the input, and is intended to give a model for quantum-mechanical processes that occur along a space-like reaction coordinate. The classical system can be thought of in physical terms as two counterflowing probability streams, which stochastically exchange probability currents in a way that the net probability current, and hence the overall probability, suitably interpreted, is conserved. The proposed formalism extends the . mathematics of those stochastic processes describable with linear, single-step, unidirectional transition probabilities, known as Markov chains and stochastic matrices. It is shown that a certain rearrangement and combination of the input and output of two stochastic matrices of the same order yields another matrix of the same type. Each measurement causes the partial collapse of the probability current distribution in the midst of such a process, giving rise to calculable, but non-Markov, values for the ensuing modification of the system's output probability distribution. The paper concludes with an analysis of a classical probabilistic version of the so-called grandfather paradox.
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...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... 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...
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.
DEFF Research Database (Denmark)
2013-01-01
This is a very simple program to help you put together input files for use in Gries' (2007) R-based collostruction analysis program. It basically puts together a text file with a frequency list of lexemes in the construction and inserts a column where you can add the corpus frequencies. It requires...... it as input for basic collexeme collostructional analysis (Stefanowitsch & Gries 2003) in Gries' (2007) program. ColloInputGenerator is, in its current state, based on programming commands introduced in Gries (2009). Projected updates: Generation of complete work-ready frequency lists....
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.
Electric Dipole Theory of Chemical Synaptic Transmission
Wei, Ling Y.
1968-01-01
In this paper we propose that chemicals such as acetylcholine are electric dipoles which when oriented and arranged in a large array could produce an electric field strong enough to drive positive ions over the junction barrier of the post-synaptic membrane and thus initiate excitation or produce depolarization. This theory is able to explain a great number of facts such as cleft size, synaptic delay, nonregeneration, subthreshold integration, facilitation with repetition, and the calcium and magnesium effects. It also shows why and how acetylcholine could act as excitatory or inhibitory transmitters under different circumstances. Our conclusion is that the nature of synaptic transmission is essentially electrical, be it mediated by electrical or chemical transmitters. PMID:4296121
Astroglial Metabolic Networks Sustain Hippocampal Synaptic Transmission
Rouach, Nathalie; Koulakoff, Annette; Abudara, Veronica; Willecke, Klaus; Giaume, Christian
2008-12-01
Astrocytes provide metabolic substrates to neurons in an activity-dependent manner. However, the molecular mechanisms involved in this function, as well as its role in synaptic transmission, remain unclear. Here, we show that the gap-junction subunit proteins connexin 43 and 30 allow intercellular trafficking of glucose and its metabolites through astroglial networks. This trafficking is regulated by glutamatergic synaptic activity mediated by AMPA receptors. In the absence of extracellular glucose, the delivery of glucose or lactate to astrocytes sustains glutamatergic synaptic transmission and epileptiform activity only when they are connected by gap junctions. These results indicate that astroglial gap junctions provide an activity-dependent intercellular pathway for the delivery of energetic metabolites from blood vessels to distal neurons.
Astroglial metabolic networks sustain hippocampal synaptic transmission.
Rouach, Nathalie; Koulakoff, Annette; Abudara, Veronica; Willecke, Klaus; Giaume, Christian
2008-12-05
Astrocytes provide metabolic substrates to neurons in an activity-dependent manner. However, the molecular mechanisms involved in this function, as well as its role in synaptic transmission, remain unclear. Here, we show that the gap-junction subunit proteins connexin 43 and 30 allow intercellular trafficking of glucose and its metabolites through astroglial networks. This trafficking is regulated by glutamatergic synaptic activity mediated by AMPA receptors. In the absence of extracellular glucose, the delivery of glucose or lactate to astrocytes sustains glutamatergic synaptic transmission and epileptiform activity only when they are connected by gap junctions. These results indicate that astroglial gap junctions provide an activity-dependent intercellular pathway for the delivery of energetic metabolites from blood vessels to distal neurons.
International Nuclear Information System (INIS)
Marklund, J.E.; Bergstroem, U.; Edlund, O.
1980-01-01
The computer program BIOPATH describes the flow of radioactivity within a given ecosystem after a postulated release of radioactive material and the resulting dose for specified population groups. The present report accounts for the input data necessary to run BIOPATH. The report also contains descriptions of possible control cards and an input example as well as a short summary of the basic theory.(author)
International Nuclear Information System (INIS)
Carr, S.; Lane, G.; Rowling, G.
1986-11-01
This document describes the input procedures, input data files and operating instructions for the SYVAC A/C 1.03 computer program. SYVAC A/C 1.03 simulates the groundwater mediated movement of radionuclides from underground facilities for the disposal of low and intermediate level wastes to the accessible environment, and provides an estimate of the subsequent radiological risk to man. (author)
Keates, Simeon; Robinson, Peter
1999-01-01
For users with motion impairments, the standard keyboard and mouse arrangement for computer access often presents problems. Other approaches have to be adopted to overcome this. In this paper, we will describe the development of a prototype multimodal input system based on two gestural input channels. Results from extensive user trials of this system are presented. These trials showed that the physical and cognitive loads on the user can quickly become excessive and detrimental to the interac...
Synaptic Vesicle Endocytosis in Different Model Systems
Directory of Open Access Journals (Sweden)
Quan Gan
2018-06-01
Full Text Available Neurotransmission in complex animals depends on a choir of functionally distinct synapses releasing neurotransmitters in a highly coordinated manner. During synaptic signaling, vesicles fuse with the plasma membrane to release their contents. The rate of vesicle fusion is high and can exceed the rate at which synaptic vesicles can be re-supplied by distant sources. Thus, local compensatory endocytosis is needed to replenish the synaptic vesicle pools. Over the last four decades, various experimental methods and model systems have been used to study the cellular and molecular mechanisms underlying synaptic vesicle cycle. Clathrin-mediated endocytosis is thought to be the predominant mechanism for synaptic vesicle recycling. However, recent studies suggest significant contribution from other modes of endocytosis, including fast compensatory endocytosis, activity-dependent bulk endocytosis, ultrafast endocytosis, as well as kiss-and-run. Currently, it is not clear whether a universal model of vesicle recycling exist for all types of synapses. It is possible that each synapse type employs a particular mode of endocytosis. Alternatively, multiple modes of endocytosis operate at the same synapse, and the synapse toggles between different modes depending on its activity level. Here we compile review and research articles based on well-characterized model systems: frog neuromuscular junctions, C. elegans neuromuscular junctions, Drosophila neuromuscular junctions, lamprey reticulospinal giant axons, goldfish retinal ribbon synapses, the calyx of Held, and rodent hippocampal synapses. We will compare these systems in terms of their known modes and kinetics of synaptic vesicle endocytosis, as well as the underlying molecular machineries. We will also provide the future development of this field.
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
Distributed Fault Detection for a Class of Nonlinear Stochastic Systems
Directory of Open Access Journals (Sweden)
Bingyong Yan
2014-01-01
Full Text Available A novel distributed fault detection strategy for a class of nonlinear stochastic systems is presented. Different from the existing design procedures for fault detection, a novel fault detection observer, which consists of a nonlinear fault detection filter and a consensus filter, is proposed to detect the nonlinear stochastic systems faults. Firstly, the outputs of the nonlinear stochastic systems act as inputs of a consensus filter. Secondly, a nonlinear fault detection filter is constructed to provide estimation of unmeasurable system states and residual signals using outputs of the consensus filter. Stability analysis of the consensus filter is rigorously investigated. Meanwhile, the design procedures of the nonlinear fault detection filter are given in terms of linear matrix inequalities (LMIs. Taking the influence of the system stochastic noises into consideration, an outstanding feature of the proposed scheme is that false alarms can be reduced dramatically. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed fault detection approach.
International Nuclear Information System (INIS)
Haran, O.; Shvarts, D.; Thieberger, R.
1998-01-01
Classical transport of neutral particles in a binary, scattering, stochastic media is discussed. It is assumed that the cross-sections of the constituent materials and their volume fractions are known. The inner structure of the media is stochastic, but there exist a statistical knowledge about the lump sizes, shapes and arrangement. The transmission through the composite media depends on the specific heterogeneous realization of the media. The current research focuses on the averaged transmission through an ensemble of realizations, frm which an effective cross-section for the media can be derived. The problem of one dimensional transport in stochastic media has been studied extensively [1]. In the one dimensional description of the problem, particles are transported along a line populated with alternating material segments of random lengths. The current work discusses transport in two-dimensional stochastic media. The phenomenon that is unique to the multi-dimensional description of the problem is obstacle bypassing. Obstacle bypassing tends to reduce the opacity of the media, thereby reducing its effective cross-section. The importance of this phenomenon depends on the manner in which the obstacles are arranged in the media. Results of transport simulations in multi-dimensional stochastic media are presented. Effective cross-sections derived from the simulations are compared against those obtained for the one-dimensional problem, and against those obtained from effective multi-dimensional models, which are partially based on a Markovian assumption
Stochastic Collocation Applications in Computational Electromagnetics
Directory of Open Access Journals (Sweden)
Dragan Poljak
2018-01-01
Full Text Available The paper reviews the application of deterministic-stochastic models in some areas of computational electromagnetics. Namely, in certain problems there is an uncertainty in the input data set as some properties of a system are partly or entirely unknown. Thus, a simple stochastic collocation (SC method is used to determine relevant statistics about given responses. The SC approach also provides the assessment of related confidence intervals in the set of calculated numerical results. The expansion of statistical output in terms of mean and variance over a polynomial basis, via SC method, is shown to be robust and efficient approach providing a satisfactory convergence rate. This review paper provides certain computational examples from the previous work by the authors illustrating successful application of SC technique in the areas of ground penetrating radar (GPR, human exposure to electromagnetic fields, and buried lines and grounding systems.
Qi, Z; Kikuchi, S; Tretter, F; Voit, E O
2011-05-01
Major depressive disorder (MDD) affects about 16% of the general population and is a leading cause of death in the United States and around the world. Aggravating the situation is the fact that "drug use disorders" are highly comorbid in MDD patients, and VICE VERSA. Drug use and MDD share a common component, the dopamine system, which is critical in many motivation and reward processes, as well as in the regulation of stress responses in MDD. A potentiating mechanism in drug use disorders appears to be synaptic plasticity, which is regulated by dopamine transmission. In this article, we describe a computational model of the synaptic plasticity of GABAergic medium spiny neurons in the nucleus accumbens, which is critical in the reward system. The model accounts for effects of both dopamine and glutamate transmission. Model simulations show that GABAergic medium spiny neurons tend to respond to dopamine stimuli with synaptic potentiation and to glutamate signals with synaptic depression. Concurrent dopamine and glutamate signals cause various types of synaptic plasticity, depending on input scenarios. Interestingly, the model shows that a single 0.5 mg/kg dose of amphetamine can cause synaptic potentiation for over 2 h, a phenomenon that makes synaptic plasticity of medium spiny neurons behave quasi as a bistable system. The model also identifies mechanisms that could potentially be critical to correcting modifications of synaptic plasticity caused by drugs in MDD patients. An example is the feedback loop between protein kinase A, phosphodiesterase, and the second messenger cAMP in the postsynapse. Since reward mechanisms activated by psychostimulants could be crucial in establishing addiction comorbidity in patients with MDD, this model might become an aid for identifying and targeting specific modules within the reward system and lead to a better understanding and potential treatment of comorbid drug use disorders in MDD. © Georg Thieme Verlag KG Stuttgart · New
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 ...
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 err...
Stochastic approach to microphysics
Energy Technology Data Exchange (ETDEWEB)
Aron, J.C.
1987-01-01
The presently widespread idea of ''vacuum population'', together with the quantum concept of vacuum fluctuations leads to assume a random level below that of matter. This stochastic approach starts by a reminder of the author's previous work, first on the relation of diffusion laws with the foundations of microphysics, and then on hadron spectrum. Following the latter, a random quark model is advanced; it gives to quark pairs properties similar to those of a harmonic oscillator or an elastic string, imagined as an explanation to their asymptotic freedom and their confinement. The stochastic study of such interactions as electron-nucleon, jets in e/sup +/e/sup -/ collisions, or pp -> ..pi../sup 0/ + X, gives form factors closely consistent with experiment. The conclusion is an epistemological comment (complementarity between stochastic and quantum domains, E.P.R. paradox, etc...).
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 ...
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.
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)
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)
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 Feedforward Control Technique
Halyo, Nesim
1990-01-01
Class of commanded trajectories modeled as stochastic process. Advanced Transport Operating Systems (ATOPS) research and development program conducted by NASA Langley Research Center aimed at developing capabilities for increases in capacities of airports, safe and accurate flight in adverse weather conditions including shear, winds, avoidance of wake vortexes, and reduced consumption of fuel. Advances in techniques for design of modern controls and increased capabilities of digital flight computers coupled with accurate guidance information from Microwave Landing System (MLS). Stochastic feedforward control technique developed within context of ATOPS program.
Markov stochasticity coordinates
International Nuclear Information System (INIS)
Eliazar, Iddo
2017-01-01
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
DEFF Research Database (Denmark)
Simonsen, Maria
This thesis treats stochastic systems with switching dynamics. Models with these characteristics are studied from several perspectives. Initially in a simple framework given in the form of stochastic differential equations and, later, in an extended form which fits into the framework of sliding...... mode control. It is investigated how to understand and interpret solutions to models of switched systems, which are exposed to discontinuous dynamics and uncertainties (primarily) in the form of white noise. The goal is to gain knowledge about the performance of the system by interpreting the solution...
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
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... of vortices: topological charge ±1 (higher order are unstable). Positive and negative vortex densities np(x, y, z) and nn(x, y, z) ⊲ Vortex density: V = np + nn ⊲ Topological charge density: T = np − nn – p. 4/24 Subfields of SSO ⊲ Homogeneous, normally...
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
Markov stochasticity coordinates
Energy Technology Data Exchange (ETDEWEB)
Eliazar, Iddo, E-mail: iddo.eliazar@intel.com
2017-01-15
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
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.
Directory of Open Access Journals (Sweden)
Alexi Nott
2015-01-01
Full Text Available An experience-dependent postnatal increase in GABAergic inhibition in the visual cortex is important for the closure of a critical period of enhanced synaptic plasticity. Although maturation of the subclass of parvalbumin (Pv–expressing GABAergic interneurons is known to contribute to critical period closure, the role of epigenetics on cortical inhibition and synaptic plasticity has not been explored. The transcription regulator, histone deacetylase 2 (HDAC2, has been shown to modulate synaptic plasticity and learning processes in hippocampal excitatory neurons. We found that genetic deletion of HDAC2 specifically from Pv interneurons reduces inhibitory input in the visual cortex of adult mice and coincides with enhanced long-term depression that is more typical of young mice. These findings show that HDAC2 loss in Pv interneurons leads to a delayed closure of the critical period in the visual cortex and supports the hypothesis that HDAC2 is a key negative regulator of synaptic plasticity in the adult brain.
Nott, Alexi; Cho, Sukhee; Seo, Jinsoo; Tsai, Li-Huei
2015-01-01
An experience-dependent postnatal increase in GABAergic inhibition in the visual cortex is important for the closure of a critical period of enhanced synaptic plasticity. Although maturation of the subclass of Parvalbumin (Pv)-expressing GABAergic interneurons is known to contribute to critical period closure, the role of epigenetics on cortical inhibition and synaptic plasticity has not been explored. The transcription regulator, histone deacetylase 2 (HDAC2), has been shown to modulate synaptic plasticity and learning processes in hippocampal excitatory neurons. We found that genetic deletion of HDAC2 specifically from Pv-interneurons reduces inhibitory input in the visual cortex of adult mice, and coincides with enhanced long-term depression (LTD) that is more typical of young mice. These findings show that HDAC2 loss in Pv-interneurons leads to a delayed closure of the critical period in the visual cortex and supports the hypothesis that HDAC2 is a key negative regulator of synaptic plasticity in the adult brain.
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.
Li, Y W; Bayliss, D A
1998-06-01
1. We studied electrophysiological properties, synaptic transmission and modulation by 5-hydroxytryptamine (5-HT) of caudal raphe neurons using whole-cell recording in a neonatal rat brain slice preparation; recorded neurons were identified as serotonergic by post-hoc immunohistochemical detection of tryptophan hydroxylase, the 5-HT-synthesizing enzyme. 2. Serotonergic neurons fired spontaneously (approximately 1 Hz), with maximal steady state firing rates of < 4 Hz. 5-Hydroxytryptamine caused hyperpolarization and cessation of spike activity in these neurons by activating inwardly rectifying K+ conductance via somatodendritic 5-HT1A receptors. 3. Unitary glutamatergic excitatory post-synaptic potentials (EPSP) and currents (EPSC) were evoked in serotonergic neurons by local electrical stimulation. Evoked EPSC were potently inhibited by 5-HT, an effect mediated by presynaptic 5-HT1B receptors. 4. In conclusion, serotonergic caudal raphe neurons are spontaneously active in vitro; they receive prominent glutamatergic synaptic inputs. 5-Hydroxytryptamine regulates serotonergic neuronal activity of the caudal raphe by decreasing spontaneous activity via somatodendritic 5-HT1A receptors and by inhibiting excitatory synaptic transmission onto these neurons via presynaptic 5-HT1B receptors. These local modulatory mechanisms provide multiple levels of feedback autoregulation of serotonergic raphe neurons by 5-HT.
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.
Synaptic ribbon. Conveyor belt or safety belt?
Parsons, T D; Sterling, P
2003-02-06
The synaptic ribbon in neurons that release transmitter via graded potentials has been considered as a conveyor belt that actively moves vesicles toward their release sites. But evidence has accumulated to the contrary, and it now seems plausible that the ribbon serves instead as a safety belt to tether vesicles stably in mutual contact and thus facilitate multivesicular release by compound exocytosis.
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
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.
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 quantisation: theme and variation
International Nuclear Information System (INIS)
Klauder, J.R.; Kyoto Univ.
1987-01-01
The paper on stochastic quantisation is a contribution to the book commemorating the sixtieth birthday of E.S. Fradkin. Stochastic quantisation reformulates Euclidean quantum field theory in the language of Langevin equations. The generalised free field is discussed from the viewpoint of stochastic quantisation. An artificial family of highly singular model theories wherein the space-time derivatives are dropped altogether is also examined. Finally a modified form of stochastic quantisation is considered. (U.K.)
Synaptic remodeling, synaptic growth and the storage of long-term memory in Aplysia.
Bailey, Craig H; Kandel, Eric R
2008-01-01
Synaptic remodeling and synaptic growth accompany various forms of long-term memory. Storage of the long-term memory for sensitization of the gill-withdrawal reflex in Aplysia has been extensively studied in this respect and is associated with the growth of new synapses by the sensory neurons onto their postsynaptic target neurons. Recent time-lapse imaging studies of living sensory-to-motor neuron synapses in culture have monitored both functional and structural changes simultaneously so as to follow remodeling and growth at the same specific synaptic connections continuously over time and to examine the functional contribution of these learning-related structural changes to the different time-dependent phases of memory storage. Insights provided by these studies suggest the synaptic differentiation and growth induced by learning in the mature nervous system are highly dynamic and often rapid processes that can recruit both molecules and mechanisms used for de novo synapse formation during development.
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...
Gating of Long-Term Potentiation by Nicotinic Acetylcholine Receptors at the Cerebellum Input Stage
F. Prestori (Francesca); C. Bonardi (Claudia); L. Mapelli (Lisa); P. Lombardo (Paola); R. Goselink (Rianne); M.E. de Stefano (Maria Egle); D. Gandolfi (Daniela); J. Mapelli (Jonathan); D. Bertrand (Daniel); M. Schonewille (Martijn); C.I. de Zeeuw (Chris); E. D'Angelo (Egidio)
2013-01-01
textabstractThe brain needs mechanisms able to correlate plastic changes with local circuit activity and internal functional states. At the cerebellum input stage, uncontrolled induction of long-term potentiation or depression (LTP or LTD) between mossy fibres and granule cells can saturate synaptic
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)
Stochastic Estimation via Polynomial Chaos
2015-10-01
AFRL-RW-EG-TR-2015-108 Stochastic Estimation via Polynomial Chaos Douglas V. Nance Air Force Research...COVERED (From - To) 20-04-2015 – 07-08-2015 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Stochastic Estimation via Polynomial Chaos ...This expository report discusses fundamental aspects of the polynomial chaos method for representing the properties of second order stochastic
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.
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...
Energy Technology Data Exchange (ETDEWEB)
Aghajanian, G K; McCall, R B [Yale Univ., New Haven, CT (USA). School of Medicine
1980-12-01
Serotonergic nerve terminals in the facial motor nucleus were labelled with (/sup 3/H)5-hydroxytryptamine. When serotonergic nerve terminals were destroyed (by the selective neurotoxin 5,7-dihydroxytryptamine) the labelling was lost. By electron-microscopic autoradiography, labelled serotonergic terminals were found to make axo-dendritic or axo-somatic junctions with facial motor neurons. No axo-axonic junctions were observed. These morphological findings are consistent with physiological studies which indicate that 5-hydroxytryptamine facilitates the excitation of facial motoneurons through a direct postsynaptic action.
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)
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
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 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...
Research in Stochastic Processes.
1982-10-31
Office of Scientific Research Grant AFOSR F49620 82 C 0009 Period: 1 Noveber 1981 through 31 October 1982 Title: Research in Stochastic Processes Co...STA4ATIS CAMBANIS The work briefly described here was developed in connection with problems arising from and related to the statistical comunication
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
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.
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... 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...
Nagendran, Tharkika; Larsen, Rylan S; Bigler, Rebecca L; Frost, Shawn B; Philpot, Benjamin D; Nudo, Randolph J; Taylor, Anne Marion
2017-09-20
Injury of CNS nerve tracts remodels circuitry through dendritic spine loss and hyper-excitability, thus influencing recovery. Due to the complexity of the CNS, a mechanistic understanding of injury-induced synaptic remodeling remains unclear. Using microfluidic chambers to separate and injure distal axons, we show that axotomy causes retrograde dendritic spine loss at directly injured pyramidal neurons followed by retrograde presynaptic hyper-excitability. These remodeling events require activity at the site of injury, axon-to-soma signaling, and transcription. Similarly, directly injured corticospinal neurons in vivo also exhibit a specific increase in spiking following axon injury. Axotomy-induced hyper-excitability of cultured neurons coincides with elimination of inhibitory inputs onto injured neurons, including those formed onto dendritic spines. Netrin-1 downregulation occurs following axon injury and exogenous netrin-1 applied after injury normalizes spine density, presynaptic excitability, and inhibitory inputs at injured neurons. Our findings show that intrinsic signaling within damaged neurons regulates synaptic remodeling and involves netrin-1 signaling.Spinal cord injury can induce synaptic reorganization and remodeling in the brain. Here the authors study how severed distal axons signal back to the cell body to induce hyperexcitability, loss of inhibition and enhanced presynaptic release through netrin-1.
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.
The ISI distribution of the stochastic Hodgkin-Huxley neuron.
Rowat, Peter F; Greenwood, Priscilla E
2014-01-01
The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.
Synaptic Mechanisms Generating Orientation Selectivity in the ON Pathway of the Rabbit Retina.
Venkataramani, Sowmya; Taylor, W Rowland
2016-03-16
Neurons that signal the orientation of edges within the visual field have been widely studied in primary visual cortex. Much less is known about the mechanisms of orientation selectivity that arise earlier in the visual stream. Here we examine the synaptic and morphological properties of a subtype of orientation-selective ganglion cell in the rabbit retina. The receptive field has an excitatory ON center, flanked by excitatory OFF regions, a structure similar to simple cell receptive fields in primary visual cortex. Examination of the light-evoked postsynaptic currents in these ON-type orientation-selective ganglion cells (ON-OSGCs) reveals that synaptic input is mediated almost exclusively through the ON pathway. Orientation selectivity is generated by larger excitation for preferred relative to orthogonal stimuli, and conversely larger inhibition for orthogonal relative to preferred stimuli. Excitatory orientation selectivity arises in part from the morphology of the dendritic arbors. Blocking GABAA receptors reduces orientation selectivity of the inhibitory synaptic inputs and the spiking responses. Negative contrast stimuli in the flanking regions produce orientation-selective excitation in part by disinhibition of a tonic NMDA receptor-mediated input arising from ON bipolar cells. Comparison with earlier studies of OFF-type OSGCs indicates that diverse synaptic circuits have evolved in the retina to detect the orientation of edges in the visual input. A core goal for visual neuroscientists is to understand how neural circuits at each stage of the visual system extract and encode features from the visual scene. This study documents a novel type of orientation-selective ganglion cell in the retina and shows that the receptive field structure is remarkably similar to that of simple cells in primary visual cortex. However, the data indicate that, unlike in the cortex, orientation selectivity in the retina depends on the activity of inhibitory interneurons. The
Stabilization of (state, input)-disturbed CSTRs through the port-Hamiltonian systems approach
Lu, Yafei; Fang, Zhou; Gao, Chuanhou
2017-01-01
It is a universal phenomenon that the state and input of the continuous stirred tank reactor (CSTR) systems are both disturbed. This paper proposes a (state, input)-disturbed port-Hamiltonian framework that can be used to model and further designs a stochastic passivity based controller to asymptotically stabilize in probability the (state, input)-disturbed CSTR (sidCSTR) systems. The opposite entropy function and the availability function are selected as the Hamiltonian for the model and con...
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...
Butler, Corwin R; Boychuk, Jeffery A; Smith, Bret N
2017-01-01
Following traumatic brain injury (TBI), treatment with rapamycin suppresses mammalian (mechanistic) target of rapamycin (mTOR) activity and specific components of hippocampal synaptic reorganization associated with altered cortical excitability and seizure susceptibility. Reemergence of seizures after cessation of rapamycin treatment suggests, however, an incomplete suppression of epileptogenesis. Hilar inhibitory interneurons regulate dentate granule cell (DGC) activity, and de novo synaptic input from both DGCs and CA3 pyramidal cells after TBI increases their excitability but effects of rapamycin treatment on the injury-induced plasticity of interneurons is only partially described. Using transgenic mice in which enhanced green fluorescent protein (eGFP) is expressed in the somatostatinergic subset of hilar inhibitory interneurons, we tested the effect of daily systemic rapamycin treatment (3 mg/kg) on the excitability of hilar inhibitory interneurons after controlled cortical impact (CCI)-induced focal brain injury. Rapamycin treatment reduced, but did not normalize, the injury-induced increase in excitability of surviving eGFP+ hilar interneurons. The injury-induced increase in response to selective glutamate photostimulation of DGCs was reduced to normal levels after mTOR inhibition, but the postinjury increase in synaptic excitation arising from CA3 pyramidal cell activity was unaffected by rapamycin treatment. The incomplete suppression of synaptic reorganization in inhibitory circuits after brain injury could contribute to hippocampal hyperexcitability and the eventual reemergence of the epileptogenic process upon cessation of mTOR inhibition. Further, the cell-selective effect of mTOR inhibition on synaptic reorganization after CCI suggests possible mechanisms by which rapamycin treatment modifies epileptogenesis in some models but not others.
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.
Garcia, Neus; Santafé, Manel M; Tomàs, Marta; Lanuza, Maria A; Besalduch, Nuria; Tomàs, Josep
2010-04-05
Confocal immunohistochemistry shows that neurotrophin-3 (NT-3) and its receptor tropomyosin-related tyrosin kinase C (trkC) are present in both neonatal (P6) and adult (P45) mouse motor nerve terminals in neuromuscular junctions (NMJ) colocalized with several synaptic proteins. NT-3 incubation (1-3h, in the range 10-200ng/ml) does not change the size of the evoked and spontaneous endplate potentials at P45. However, NT-3 (1h, 100ng/ml) strongly potentiates evoked ACh release from the weak (70%) and the strong (50%) axonal inputs on dually innervated postnatal endplates (P6) but not in the most developed postnatal singly innervated synapses at P6. The present results indicate that NT-3 has a role in the developmental mechanism that eliminates redundant synapses though it cannot modulate synaptic transmission locally as the NMJ matures.
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.
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...... 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 Sizing of Energy Storage Systems for Wind Integration
Directory of Open Access Journals (Sweden)
D. D. Le
2018-06-01
Full Text Available In this paper, we present an optimal capacity decision model for energy storage systems (ESSs in combined operation with wind energy in power systems. We use a two-stage stochastic programming approach to take into account both wind and load uncertainties. The planning problem is formulated as an AC optimal power flow (OPF model with the objective of minimizing ESS installation cost and system operation cost. Stochastic wind and load inputs for the model are generated from historical data using clustering technique. The model is tested on the IEEE 39-bus system.
Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik
2016-07-01
Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.
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.
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.
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
Synaptic and intrinsic activation of GABAergic neurons in the cardiorespiratory brainstem network.
Directory of Open Access Journals (Sweden)
Julie G Frank
Full Text Available GABAergic pathways in the brainstem play an essential role in respiratory rhythmogenesis and interactions between the respiratory and cardiovascular neuronal control networks. However, little is known about the identity and function of these GABAergic inhibitory neurons and what determines their activity. In this study we have identified a population of GABAergic neurons in the ventrolateral medulla that receive increased excitatory post-synaptic potentials during inspiration, but also have spontaneous firing in the absence of synaptic input. Using transgenic mice that express GFP under the control of the Gad1 (GAD67 gene promoter, we determined that this population of GABAergic neurons is in close apposition to cardioinhibitory parasympathetic cardiac neurons in the nucleus ambiguus (NA. These neurons fire in synchronization with inspiratory activity. Although they receive excitatory glutamatergic synaptic inputs during inspiration, this excitatory neurotransmission was not altered by blocking nicotinic receptors, and many of these GABAergic neurons continue to fire after synaptic blockade. The spontaneous firing in these GABAergic neurons was not altered by the voltage-gated calcium channel blocker cadmium chloride that blocks both neurotransmission to these neurons and voltage-gated Ca(2+ currents, but spontaneous firing was diminished by riluzole, demonstrating a role of persistent sodium channels in the spontaneous firing in these cardiorespiratory GABAergic neurons that possess a pacemaker phenotype. The spontaneously firing GABAergic neurons identified in this study that increase their activity during inspiration would support respiratory rhythm generation if they acted primarily to inhibit post-inspiratory neurons and thereby release inspiration neurons to increase their activity. This population of inspiratory-modulated GABAergic neurons could also play a role in inhibiting neurons that are most active during expiration and provide a
Synaptic and intrinsic activation of GABAergic neurons in the cardiorespiratory brainstem network.
Frank, Julie G; Mendelowitz, David
2012-01-01
GABAergic pathways in the brainstem play an essential role in respiratory rhythmogenesis and interactions between the respiratory and cardiovascular neuronal control networks. However, little is known about the identity and function of these GABAergic inhibitory neurons and what determines their activity. In this study we have identified a population of GABAergic neurons in the ventrolateral medulla that receive increased excitatory post-synaptic potentials during inspiration, but also have spontaneous firing in the absence of synaptic input. Using transgenic mice that express GFP under the control of the Gad1 (GAD67) gene promoter, we determined that this population of GABAergic neurons is in close apposition to cardioinhibitory parasympathetic cardiac neurons in the nucleus ambiguus (NA). These neurons fire in synchronization with inspiratory activity. Although they receive excitatory glutamatergic synaptic inputs during inspiration, this excitatory neurotransmission was not altered by blocking nicotinic receptors, and many of these GABAergic neurons continue to fire after synaptic blockade. The spontaneous firing in these GABAergic neurons was not altered by the voltage-gated calcium channel blocker cadmium chloride that blocks both neurotransmission to these neurons and voltage-gated Ca(2+) currents, but spontaneous firing was diminished by riluzole, demonstrating a role of persistent sodium channels in the spontaneous firing in these cardiorespiratory GABAergic neurons that possess a pacemaker phenotype. The spontaneously firing GABAergic neurons identified in this study that increase their activity during inspiration would support respiratory rhythm generation if they acted primarily to inhibit post-inspiratory neurons and thereby release inspiration neurons to increase their activity. This population of inspiratory-modulated GABAergic neurons could also play a role in inhibiting neurons that are most active during expiration and provide a framework for
Augustinaite, Sigita; Heggelund, Paul
2018-05-24
Synaptic short-term plasticity (STP) regulates synaptic transmission in an activity-dependent manner and thereby has important roles in the signal processing in the brain. In some synapses, a presynaptic train of action potentials elicits post-synaptic potentials that gradually increase during the train (facilitation), but in other synapses, these potentials gradually decrease (depression). We studied STP in neurons in the visual thalamic relay, the dorsal lateral geniculate nucleus (dLGN). The dLGN contains two types of neurons: excitatory thalamocortical (TC) neurons, which transfer signals from retinal afferents to visual cortex, and local inhibitory interneurons, which form an inhibitory feedforward loop that regulates the thalamocortical signal transmission. The overall STP in the retino-thalamic relay is short-term depression, but the distinct kind and characteristics of the plasticity at the different types of synapses are unknown. We studied STP in the excitatory responses of interneurons to stimulation of retinal afferents, in the inhibitory responses of TC neurons to stimulation of afferents from interneurons, and in the disynaptic inhibitory responses of TC neurons to stimulation of retinal afferents. Moreover, we studied STP at the direct excitatory input to TC neurons from retinal afferents. The STP at all types of the synapses showed short-term depression. This depression can accentuate rapid changes in the stream of signals and thereby promote detectability of significant features in the sensory input. In vision, detection of edges and contours is essential for object perception, and the synaptic short-term depression in the early visual pathway provides important contributions to this detection process. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.
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
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 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...
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 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.
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.
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...
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 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.
Stochastic stacking without filters
International Nuclear Information System (INIS)
Johnson, R.P.; Marriner, J.
1982-12-01
The rate of accumulation of antiprotons is a critical factor in the design of p anti p colliders. A design of a system to accumulate higher anti p fluxes is presented here which is an alternative to the schemes used at the CERN AA and in the Fermilab Tevatron I design. Contrary to these stacking schemes, which use a system of notch filters to protect the dense core of antiprotons from the high power of the stack tail stochastic cooling, an eddy current shutter is used to protect the core in the region of the stack tail cooling kicker. Without filters one can have larger cooling bandwidths, better mixing for stochastic cooling, and easier operational criteria for the power amplifiers. In the case considered here a flux of 1.4 x 10 8 per sec is achieved with a 4 to 8 GHz bandwidth
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
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.
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 split determinant algorithms
International Nuclear Information System (INIS)
Horvatha, Ivan
2000-01-01
I propose a large class of stochastic Markov processes associated with probability distributions analogous to that of lattice gauge theory with dynamical fermions. The construction incorporates the idea of approximate spectral split of the determinant through local loop action, and the idea of treating the infrared part of the split through explicit diagonalizations. I suggest that exact algorithms of practical relevance might be based on Markov processes so constructed
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.
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
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
Stochasticity Modeling in Memristors
Naous, Rawan; Al-Shedivat, Maruan; Salama, Khaled N.
2015-01-01
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.
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 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
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...
Aspects if stochastic models for short-term hydropower scheduling and bidding
Energy Technology Data Exchange (ETDEWEB)
Belsnes, Michael Martin [Sintef Energy, Trondheim (Norway); Follestad, Turid [Sintef Energy, Trondheim (Norway); Wolfgang, Ove [Sintef Energy, Trondheim (Norway); Fosso, Olav B. [Dep. of electric power engineering NTNU, Trondheim (Norway)
2012-07-01
This report discusses challenges met when turning from deterministic to stochastic decision support models for short-term hydropower scheduling and bidding. The report describes characteristics of the short-term scheduling and bidding problem, different market and bidding strategies, and how a stochastic optimization model can be formulated. A review of approaches for stochastic short-term modelling and stochastic modelling for the input variables inflow and market prices is given. The report discusses methods for approximating the predictive distribution of uncertain variables by scenario trees. Benefits of using a stochastic over a deterministic model are illustrated by a case study, where increased profit is obtained to a varying degree depending on the reservoir filling and price structure. Finally, an approach for assessing the effect of using a size restricted scenario tree to approximate the predictive distribution for stochastic input variables is described. The report is a summary of the findings of Work package 1 of the research project #Left Double Quotation Mark#Optimal short-term scheduling of wind and hydro resources#Right Double Quotation Mark#. The project aims at developing a prototype for an operational stochastic short-term scheduling model. Based on the investigations summarized in the report, it is concluded that using a deterministic equivalent formulation of the stochastic optimization problem is convenient and sufficient for obtaining a working prototype. (author)
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)
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
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.
Local synaptic signaling enhances the stochastic transport of motor-driven cargo in neurons
Newby, Jay; Bressloff, Paul C
2010-01-01
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
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
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.
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.
Stochastic processes and quantum theory
International Nuclear Information System (INIS)
Klauder, J.R.
1975-01-01
The author analyses a variety of stochastic processes, namely real time diffusion phenomena, which are analogues of imaginary time quantum theory and convariant imaginary time quantum field theory. He elaborates some standard properties involving probability measures and stochastic variables and considers a simple class of examples. Finally he develops the fact that certain stochastic theories actually exhibit divergences that simulate those of covariant quantum field theory and presents examples of both renormaizable and unrenormalizable behavior. (V.J.C.)
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
Probabilistic DHP adaptive critic for nonlinear stochastic control systems.
Herzallah, Randa
2013-06-01
Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Káarnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained. Copyright © 2013 Elsevier Ltd. All rights reserved.
Stochastic Analysis with Financial Applications
Kohatsu-Higa, Arturo; Sheu, Shuenn-Jyi
2011-01-01
Stochastic analysis has a variety of applications to biological systems as well as physical and engineering problems, and its applications to finance and insurance have bloomed exponentially in recent times. The goal of this book is to present a broad overview of the range of applications of stochastic analysis and some of its recent theoretical developments. This includes numerical simulation, error analysis, parameter estimation, as well as control and robustness properties for stochastic equations. This book also covers the areas of backward stochastic differential equations via the (non-li
Inhibitory Gating of Basolateral Amygdala Inputs to the Prefrontal Cortex.
McGarry, Laura M; Carter, Adam G
2016-09-07
Interactions between the prefrontal cortex (PFC) and basolateral amygdala (BLA) regulate emotional behaviors. However, a circuit-level understanding of functional connections between these brain regions remains incomplete. The BLA sends prominent glutamatergic projections to the PFC, but the overall influence of these inputs is predominantly inhibitory. Here we combine targeted recordings and optogenetics to examine the synaptic underpinnings of this inhibition in the mouse infralimbic PFC. We find that BLA inputs preferentially target layer 2 corticoamygdala over neighboring corticostriatal neurons. However, these inputs make even stronger connections onto neighboring parvalbumin and somatostatin expressing interneurons. Inhibitory connections from these two populations of interneurons are also much stronger onto corticoamygdala neurons. Consequently, BLA inputs are able to drive robust feedforward inhibition via two parallel interneuron pathways. Moreover, the contributions of these interneurons shift during repetitive activity, due to differences in short-term synaptic dynamics. Thus, parvalbumin interneurons are activated at the start of stimulus trains, whereas somatostatin interneuron activation builds during these trains. Together, these results reveal how the BLA impacts the PFC through a complex interplay of direct excitation and feedforward inhibition. They also highlight the roles of targeted connections onto multiple projection neurons and interneurons in this cortical circuit. Our findings provide a mechanistic understanding for how the BLA can influence the PFC circuit, with important implications for how this circuit participates in the regulation of emotion. The prefrontal cortex (PFC) and basolateral amygdala (BLA) interact to control emotional behaviors. Here we show that BLA inputs elicit direct excitation and feedforward inhibition of layer 2 projection neurons in infralimbic PFC. BLA inputs are much stronger at corticoamygdala neurons compared
Synaptic Contacts Enhance Cell-to-Cell Tau Pathology Propagation
Directory of Open Access Journals (Sweden)
Sara Calafate
2015-05-01
Full Text Available Accumulation of insoluble Tau protein aggregates and stereotypical propagation of Tau pathology through the brain are common hallmarks of tauopathies, including Alzheimer’s disease (AD. Propagation of Tau pathology appears to occur along connected neurons, but whether synaptic contacts between neurons are facilitating propagation has not been demonstrated. Using quantitative in vitro models, we demonstrate that, in parallel to non-synaptic mechanisms, synapses, but not merely the close distance between the cells, enhance the propagation of Tau pathology between acceptor hippocampal neurons and Tau donor cells. Similarly, in an artificial neuronal network using microfluidic devices, synapses and synaptic activity are promoting neuronal Tau pathology propagation in parallel to the non-synaptic mechanisms. Our work indicates that the physical presence of synaptic contacts between neurons facilitate Tau pathology propagation. These findings can have implications for synaptic repair therapies, which may turn out to have adverse effects by promoting propagation of Tau pathology.
Attractor neural networks with resource-efficient synaptic connectivity
Pehlevan, Cengiz; Sengupta, Anirvan
Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.
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.
International Nuclear Information System (INIS)
Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile
2014-01-01
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.
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 ...
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.
International Nuclear Information System (INIS)
Hardwick, Robert J.; Vennin, Vincent; Wands, David; Byrnes, Christian T.; Torrado, Jesús
2017-01-01
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.
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
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...
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....
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
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…
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
Stochastic conditional intensity processes
DEFF Research Database (Denmark)
Bauwens, Luc; Hautsch, Nikolaus
2006-01-01
model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence......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 proposed...... for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process...
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.)
Trajectory averaging for stochastic approximation MCMC algorithms
Liang, Faming
2010-01-01
to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic
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.
Li, Ya-tang; Liu, Bao-hua; Chou, Xiao-lin; Zhang, Li I; Tao, Huizhong W
2015-08-05
In the primary visual cortex (V1), orientation-selective neurons can be categorized into simple and complex cells primarily based on their receptive field (RF) structures. In mouse V1, although previous studies have examined the excitatory/inhibitory interplay underlying orientation selectivity (OS) of simple cells, the synaptic bases for that of complex cells have remained obscure. Here, by combining in vivo loose-patch and whole-cell recordings, we found that complex cells, identified by their overlapping on/off subfields, had significantly weaker OS than simple cells at both spiking and subthreshold membrane potential response levels. Voltage-clamp recordings further revealed that although excitatory inputs to complex and simple cells exhibited a similar degree of OS, inhibition in complex cells was more narrowly tuned than excitation, whereas in simple cells inhibition was more broadly tuned than excitation. The differential inhibitory tuning can primarily account for the difference in OS between complex and simple cells. Interestingly, the differential synaptic tuning correlated well with the spatial organization of synaptic input: the inhibitory visual RF in complex cells was more elongated in shape than its excitatory counterpart and also was more elongated than that in simple cells. Together, our results demonstrate that OS of complex and simple cells is differentially shaped by cortical inhibition based on its orientation tuning profile relative to excitation, which is contributed at least partially by the spatial organization of RFs of presynaptic inhibitory neurons. Simple and complex cells, two classes of principal neurons in the primary visual cortex (V1), are generally thought to be equally selective for orientation. In mouse V1, we report that complex cells, identified by their overlapping on/off subfields, has significantly weaker orientation selectivity (OS) than simple cells. This can be primarily attributed to the differential tuning selectivity
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.
Experimental Implementation of a Biometric Laser Synaptic Sensor
Directory of Open Access Journals (Sweden)
Alexander N. Pisarchik
2013-12-01
Full Text Available We fabricate a biometric laser fiber synaptic sensor to transmit information from one neuron cell to the other by an optical way. The optical synapse is constructed on the base of an erbium-doped fiber laser, whose pumped diode current is driven by a pre-synaptic FitzHugh–Nagumo electronic neuron, and the laser output controls a post-synaptic FitzHugh–Nagumo electronic neuron. The implemented laser synapse displays very rich dynamics, including fixed points, periodic orbits with different frequency-locking ratios and chaos. These regimes can be beneficial for efficient biorobotics, where behavioral flexibility subserved by synaptic connectivity is a challenge.
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.
Neurons with two sites of synaptic integration learn invariant representations.
Körding, K P; König, P
2001-12-01
Neurons in mammalian cerebral cortex combine specific responses with respect to some stimulus features with invariant responses to other stimulus features. For example, in primary visual cortex, complex cells code for orientation of a contour but ignore its position to a certain degree. In higher areas, such as the inferotemporal cortex, translation-invariant, rotation-invariant, and even view point-invariant responses can be observed. Such properties are of obvious interest to artificial systems performing tasks like pattern recognition. It remains to be resolved how such response properties develop in biological systems. Here we present an unsupervised learning rule that addresses this problem. It is based on a neuron model with two sites of synaptic integration, allowing qualitatively different effects of input to basal and apical dendritic trees, respectively. Without supervision, the system learns to extract invariance properties using temporal or spatial continuity of stimuli. Furthermore, top-down information can be smoothly integrated in the same framework. Thus, this model lends a physiological implementation to approaches of unsupervised learning of invariant-response properties.
Directory of Open Access Journals (Sweden)
Jianmin Yang
2014-05-01
Full Text Available Experience-dependent plasticity shapes postnatal development of neural circuits, but the mechanisms that refine dendritic arbors, remodel spines, and impair synaptic activity are poorly understood. Mature brain-derived neurotrophic factor (BDNF modulates neuronal morphology and synaptic plasticity, including long-term potentiation (LTP via TrkB activation. BDNF is initially translated as proBDNF, which binds p75NTR. In vitro, recombinant proBDNF modulates neuronal structure and alters hippocampal long-term plasticity, but the actions of endogenously expressed proBDNF are unclear. Therefore, we generated a cleavage-resistant probdnf knockin mouse. Our results demonstrate that proBDNF negatively regulates hippocampal dendritic complexity and spine density through p75NTR. Hippocampal slices from probdnf mice exhibit depressed synaptic transmission, impaired LTP, and enhanced long-term depression (LTD in area CA1. These results suggest that proBDNF acts in vivo as a biologically active factor that regulates hippocampal structure, synaptic transmission, and plasticity, effects that are distinct from those of mature BDNF.
Synaptic organization and division of labor in the exceptionally polymorphic ant Pheidole rhea.
Gordon, Darcy G; Traniello, James F A
2018-05-29
Social insect polyphenisms provide models to examine the neural basis of division of labor and anatomy of the invertebrate social brain. Worker size-related behavior is hypothesized to enhance task performance, raising questions concerning the integration of morphology, behavior, and cellular neuroarchitecture, and how variation in sensory inputs and cognitive demands of behaviorally differentiated workers is reflected in higher-order processing ability. We used the highly polymorphic ant Pheidole rhea, which has three distinct worker size classes - minors, soldiers, and supersoldiers - to examine variation in synaptic circuitry across worker size and social role. We hypothesized that the density and size of synaptic complexes (microglomeruli, MG) would be positively associated with behavioral repertoire and the relative size of the mushroom bodies (MB). Supersoldiers had significantly larger and less dense MG in the lip (olfactory region) of the MB calyx (MBC), and larger MG in the collar (visual region) compared to minors. Soldiers were intermediate in synaptic phenotype: they did not differ significantly in MG density from minors and supersoldiers, had MG of similar size to minors in the lip, and did not differ from these two worker groups in MG size in the collar. Results suggest a complex relationship between MG density, size, behavior, and worker body size involving a conserved and plastic neurobiological development plan, although workers show strong variation in size and social role. Copyright © 2018 Elsevier B.V. All rights reserved.
Vail, Graham; Cheng, Aifang; Han, Yu Ray; Zhao, Teng; Du, Shengwang; Loy, Michael M T; Herrup, Karl; Plummer, Mark R
2016-07-01
Ataxia telangiectasia is a multisystemic disorder that includes a devastating neurodegeneration phenotype. The ATM (ataxia-telangiectasia mutated) protein is well-known for its role in the DNA damage response, yet ATM is also found in association with cytoplasmic vesicular structures: endosomes and lysosomes, as well as neuronal synaptic vesicles. In keeping with this latter association, electrical stimulation of the Schaffer collateral pathway in hippocampal slices from ATM-deficient mice does not elicit normal long-term potentiation (LTP). The current study was undertaken to assess the nature of this deficit. Theta burst-induced LTP was reduced in Atm(-/-) animals, with the reduction most pronounced at burst stimuli that included 6 or greater trains. To assess whether the deficit was associated with a pre- or postsynaptic failure, we analyzed paired-pulse facilitation and found that it too was significantly reduced in Atm(-/-) mice. This indicates a deficit in presynaptic function. As further evidence that these synaptic effects of ATM deficiency were presynaptic, we used stochastic optical reconstruction microscopy. Three-dimensional reconstruction revealed that ATM is significantly more closely associated with Piccolo (a presynaptic marker) than with Homer1 (a postsynaptic marker). These results underline how, in addition to its nuclear functions, ATM plays an important functional role in the neuronal synapse where it participates in the regulation of presynaptic vesicle physiology. Copyright © 2016 the American Physiological Society.
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.
Kim, Sei Eun; Lee, Seul Yi; Blanco, Cynthia L; Kim, Jun Hee
2014-08-20
The human fetus starts to hear and undergoes major developmental changes in the auditory system during the third trimester of pregnancy. Although there are significant data regarding development of the auditory system in rodents, changes in intrinsic properties and synaptic function of auditory neurons in developing primate brain at hearing onset are poorly understood. We performed whole-cell patch-clamp recordings of principal neurons in the medial nucleus of trapezoid body (MNTB) in preterm and term baboon brainstem slices to study the structural and functional maturation of auditory synapses. Each MNTB principal neuron received an excitatory input from a single calyx of Held terminal, and this one-to-one pattern of innervation was already formed in preterm baboons delivered at 67% of normal gestation. There was no difference in frequency or amplitude of spontaneous excitatory postsynaptic synaptic currents between preterm and term MNTB neurons. In contrast, the frequency of spontaneous GABA(A)/glycine receptor-mediated inhibitory postsynaptic synaptic currents, which were prevalent in preterm MNTB neurons, was significantly reduced in term MNTB neurons. Preterm MNTB neurons had a higher input resistance than term neurons and fired in bursts, whereas term MNTB neurons fired a single action potential in response to suprathreshold current injection. The maturation of intrinsic properties and dominance of excitatory inputs in the primate MNTB allow it to take on its mature role as a fast and reliable relay synapse. Copyright © 2014 the authors 0270-6474/14/3411399-06$15.00/0.
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.
Schilstra, Maria J; Martin, Stephen R
2009-01-01
Stochastic simulations may be used to describe changes with time of a reaction system in a way that explicitly accounts for the fact that molecules show a significant degree of randomness in their dynamic behavior. The stochastic approach is almost invariably used when small numbers of molecules or molecular assemblies are involved because this randomness leads to significant deviations from the predictions of the conventional deterministic (or continuous) approach to the simulation of biochemical kinetics. Advances in computational methods over the three decades that have elapsed since the publication of Daniel Gillespie's seminal paper in 1977 (J. Phys. Chem. 81, 2340-2361) have allowed researchers to produce highly sophisticated models of complex biological systems. However, these models are frequently highly specific for the particular application and their description often involves mathematical treatments inaccessible to the nonspecialist. For anyone completely new to the field to apply such techniques in their own work might seem at first sight to be a rather intimidating prospect. However, the fundamental principles underlying the approach are in essence rather simple, and the aim of this article is to provide an entry point to the field for a newcomer. It focuses mainly on these general principles, both kinetic and computational, which tend to be not particularly well covered in specialist literature, and shows that interesting information may even be obtained using very simple operations in a conventional spreadsheet.
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...
Behavioral Stochastic Resonance
Freund, Jan A.; Schimansky-Geier, Lutz; Beisner, Beatrix; Neiman, Alexander; Russell, David F.; Yakusheva, Tatyana; Moss, Frank
2001-03-01
Zooplankton emit weak electric fields into the surrounding water that originate from their own muscular activities associated with swimming and feeding. Juvenile paddlefish prey upon single zooplankton by detecting and tracking these weak electric signatures. The passive electric sense in the fish is provided by an elaborate array of electroreceptors, Ampullae Lorenzini, spread over the surface of an elongated rostrum. We have previously shown that the fish use stochastic resonance to enhance prey capture near the detection threshold of their sensory system. But stochastic resonance requires an external source of electrical noise in order to function. The required noise can be provided by a swarm of plankton, for example Daphnia. Thus juvenile paddlefish can detect and attack single Daphnia as outliers in the vicinity of the swarm by making use of noise from the swarm itself. From the power spectral density of the noise plus the weak signal from a single Daphnia we calculate the signal-to-noise ratio and the Fisher information at the surface of the paddlefish's rostrum. The results predict a specific attack pattern for the paddlefish that appears to be experimentally testable.
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
Thermal mixtures in stochastic mechanics
Energy Technology Data Exchange (ETDEWEB)
Guerra, F [Rome Univ. (Italy). Ist. di Matematica; Loffredo, M I [Salerno Univ. (Italy). Ist. di Fisica
1981-01-17
Stochastic mechanics is extended to systems in thermal equilibrium. The resulting stochastic processes are mixtures of Nelson processes. Their Markov property is investigated in some simple cases. It is found that in order to inforce Markov property the algebra of observable associated to the present must be suitably enlarged.
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 of...
Alternative Asymmetric Stochastic Volatility Models
M. Asai (Manabu); M.J. McAleer (Michael)
2010-01-01
textabstractThe stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model, based on the leverage and size effects. The model is
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.
Asymmetric temporal integration of layer 4 and layer 2/3 inputs in visual cortex.
Hang, Giao B; Dan, Yang
2011-01-01
Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices. We found that the integration is sublinear and temporally asymmetric, with larger responses if layer 2/3 input preceded layer 4 input. The sublinearity depended on inhibition, and the asymmetry was largely attributable to the difference between the two inhibitory inputs. Interestingly, the asymmetric integration was specific to pyramidal neurons, and it strongly affected their spiking output. Thus via cortical inhibition, the temporal order of activation of layer 2/3 and layer 4 pathways can exert powerful control of cortical output during visual processing.
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.
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.
Grassi, S; Frondaroli, A; Scarduzio, M; Dutia, M B; Dieni, C; Pettorossi, V E
2010-02-17
We investigated the effects of the neurosteroid 17beta-estradiol (E(2)) on the evoked and spontaneous activity of rat medial vestibular nucleus (MVN) neurons in brainstem slices. E(2) enhances the synaptic response to vestibular nerve stimulation in type B neurons and depresses the spontaneous discharge in both type A and B neurons. The amplitude of the field potential, as well as the excitatory post-synaptic potential (EPSP) and current (EPSC), in type B neurons, are enhanced by E(2). Both effects are long-term phenomena since they outlast the drug washout. The enhancement of synaptic response is mainly due to facilitation of glutamate release mediated by pre-synaptic N-methyl-D-aspartate receptors (NMDARs), since the reduction of paired pulse ratio (PPR) and the increase of miniature EPSC frequency after E(2) are abolished under D-(-)-2-amino-5-phosphonopentanoic acid (AP-5). E(2) also facilitates post-synaptic NMDARs, but it does not affect directly alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and group I-metabotropic glutamate receptors (mGluRs-I). In contrast, the depression of the spontaneous discharge of type A and type B neurons appears to depend on E(2) modulation of intrinsic ion conductances, as the effect remains after blockade of glutamate, GABA and glycine receptors (GlyRs). The net effect of E(2) is to enhance the signal-to-noise ratio of the synaptic response in type B neurons, relative to resting activity of all MVN neurons. These findings provide evidence for a novel potential mechanism to modulate the responsiveness of vestibular neurons to afferent inputs, and so regulate vestibular function in vivo.
Variance decomposition in stochastic simulators.
Le Maître, O P; Knio, O M; Moraes, A
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.
Variance decomposition in stochastic simulators
Le Maître, O. P.; Knio, O. M.; Moraes, A.
2015-06-01
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.
Brownian motion and stochastic calculus
Karatzas, Ioannis
1998-01-01
This book is designed as a text for graduate courses in stochastic processes. It is written for readers familiar with measure-theoretic probability and discrete-time processes who wish to explore stochastic processes in continuous time. The vehicle chosen for this exposition is Brownian motion, which is presented as the canonical example of both a martingale and a Markov process with continuous paths. In this context, the theory of stochastic integration and stochastic calculus is developed. The power of this calculus is illustrated by results concerning representations of martingales and change of measure on Wiener space, and these in turn permit a presentation of recent advances in financial economics (option pricing and consumption/investment optimization). This book contains a detailed discussion of weak and strong solutions of stochastic differential equations and a study of local time for semimartingales, with special emphasis on the theory of Brownian local time. The text is complemented by a large num...
Variance decomposition in stochastic simulators
Energy Technology Data Exchange (ETDEWEB)
Le Maître, O. P., E-mail: olm@limsi.fr [LIMSI-CNRS, UPR 3251, Orsay (France); Knio, O. M., E-mail: knio@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States); Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa [King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)
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.
Variance decomposition in stochastic simulators
Le Maî tre, O. P.; Knio, O. M.; Moraes, Alvaro
2015-01-01
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.
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".
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)
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.
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.
Methods for solving the stochastic point reactor kinetic equations
International Nuclear Information System (INIS)
Quabili, E.R.; Karasulu, M.
1979-01-01
Two new methods are presented for analysis of the statistical properties of nonlinear outputs of a point reactor to stochastic non-white reactivity inputs. They are Bourret's approximation and logarithmic linearization. The results have been compared with the exact results, previously obtained in the case of Gaussian white reactivity input. It was found that when the reactivity noise has short correlation time, Bourret's approximation should be recommended because it yields results superior to those yielded by logarithmic linearization. When the correlation time is long, Bourret's approximation is not valid, but in that case, if one can assume the reactivity noise to be Gaussian, one may use the logarithmic linearization. (author)
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.
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.
Glutamatergic synaptic plasticity in the mesocorticolimbic system in addiction
Directory of Open Access Journals (Sweden)
Aile evan Huijstee
2015-01-01
Full Text Available Addictive drugs remodel the brain’s reward circuitry, the mesocorticolimbic dopamine 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 behaviour, 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 behavioural 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. Remodelling 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.
Phosphodiesterase Inhibition to Target the Synaptic Dysfunction in Alzheimer's Disease
Bales, Kelly R.; Plath, Niels; Svenstrup, Niels; Menniti, Frank S.
Alzheimer's Disease (AD) is a disease of synaptic dysfunction that ultimately proceeds to neuronal death. There is a wealth of evidence that indicates the final common mediator of this neurotoxic process is the formation and actions on synaptotoxic b-amyloid (Aβ). The premise in this review is that synaptic dysfunction may also be an initiating factor in for AD and promote synaptotoxic Aβ formation. This latter hypothesis is consistent with the fact that the most common risk factors for AD, apolipoprotein E (ApoE) allele status, age, education, and fitness, encompass suboptimal synaptic function. Thus, the synaptic dysfunction in AD may be both cause and effect, and remediating synaptic dysfunction in AD may have acute effects on the symptoms present at the initiation of therapy and also slow disease progression. The cyclic nucleotide (cAMP and cGMP) signaling systems are intimately involved in the regulation of synaptic homeostasis. The phosphodiesterases (PDEs) are a superfamily of enzymes that critically regulate spatial and temporal aspects of cyclic nucleotide signaling through metabolic inactivation of cAMP and cGMP. Thus, targeting the PDEs to promote improved synaptic function, or 'synaptic resilience', may be an effective and facile approach to new symptomatic and disease modifying therapies for AD. There continues to be a significant drug discovery effort aimed at discovering PDE inhibitors to treat a variety of neuropsychiatric disorders. Here we review the current status of those efforts as they relate to potential new therapies for AD.
Synaptogenic proteins and synaptic organizers: "many hands make light work".
Brose, Nils
2009-03-12
Synaptogenesis is thought to be mediated by cell adhesion proteins, which induce the initial contact between an axon and its target cell and subsequently recruit and organize the presynaptic and postsynaptic protein machinery required for synaptic transmission. A new study by Linhoff and colleagues in this issue of Neuron identifies adhesion proteins of the LRRTM family as novel synaptic organizers.
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…
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
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.
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
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 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...
Propagator of stochastic electrodynamics
International Nuclear Information System (INIS)
Cavalleri, G.
1981-01-01
The ''elementary propagator'' for the position of a free charged particle subject to the zero-point electromagnetic field with Lorentz-invariant spectral density proportionalω 3 is obtained. The nonstationary process for the position is solved by the stationary process for the acceleration. The dispersion of the position elementary propagator is compared with that of quantum electrodynamics. Finally, the evolution of the probability density is obtained starting from an initial distribution confined in a small volume and with a Gaussian distribution in the velocities. The resulting probability density for the position turns out to be equal, to within radiative corrections, to psipsi* where psi is the Kennard wave packet. If the radiative corrections are retained, the present result is new since the corresponding expression in quantum electrodynamics has not yet been found. Besides preceding quantum electrodynamics for this problem, no renormalization is required in stochastic electrodynamics
Nicotinic mechanisms influencing synaptic plasticity in the hippocampus
Institute of Scientific and Technical Information of China (English)
Andon Nicholas PLACZEK; Tao A ZHANG; John Anthony DANI
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.
Synaptic transmission block by presynaptic injection of oligomeric amyloid beta
Moreno, Herman; Yu, Eunah; Pigino, Gustavo; Hernandez, Alejandro I.; Kim, Natalia; Moreira, Jorge E.; Sugimori, Mutsuyuki; Llinás, Rodolfo R.
2009-01-01
Early Alzheimer's disease (AD) pathophysiology is characterized by synaptic changes induced by degradation products of amyloid precursor protein (APP). The exact mechanisms of such modulation are unknown. Here, we report that nanomolar concentrations of intraaxonal oligomeric (o)Aβ42, but not oAβ40 or extracellular oAβ42, acutely inhibited synaptic transmission at the squid giant synapse. Further characterization of this phenotype demonstrated that presynaptic calcium currents were unaffected. However, electron microscopy experiments revealed diminished docked synaptic vesicles in oAβ42-microinjected terminals, without affecting clathrin-coated vesicles. The molecular events of this modulation involved casein kinase 2 and the synaptic vesicle rapid endocytosis pathway. These findings open the possibility of a new therapeutic target aimed at ameliorating synaptic dysfunction in AD. PMID:19304802
[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.
RES: Regularized Stochastic BFGS Algorithm
Mokhtari, Aryan; Ribeiro, Alejandro
2014-12-01
RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.
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
Hyperactivity of newborn Pten knock-out neurons results from increased excitatory synaptic drive.
Williams, Michael R; DeSpenza, Tyrone; Li, Meijie; Gulledge, Allan T; Luikart, Bryan W
2015-01-21
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. Copyright © 2015 the authors 0270-6474/15/350943-17$15.00/0.
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.
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.
Gao, Xiaoyan; Tang, Mingliang; Li, Zhifeng; Zha, Yingying; Cheng, Guosheng; Yin, Shuting; Chen, Jutao; Ruan, Di-yun; Chen, Lin; Wang, Ming
2013-04-01
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.
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.
Stochastic estimation of electricity consumption
International Nuclear Information System (INIS)
Kapetanovic, I.; Konjic, T.; Zahirovic, Z.
1999-01-01
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)
Linear stochastic neutron transport theory
International Nuclear Information System (INIS)
Lewins, J.
1978-01-01
A new and direct derivation of the Bell-Pal fundamental equation for (low power) neutron stochastic behaviour in the Boltzmann continuum model is given. The development includes correlation of particle emission direction in induced and spontaneous fission. This leads to generalizations of the backward and forward equations for the mean and variance of neutron behaviour. The stochastic importance for neutron transport theory is introduced and related to the conventional deterministic importance. Defining equations and moment equations are derived and shown to be related to the backward fundamental equation with the detector distribution of the operational definition of stochastic importance playing the role of an adjoint source. (author)
Stochasticity in the Josephson map
International Nuclear Information System (INIS)
Nomura, Y.; Ichikawa, Y.H.; Filippov, A.T.
1996-04-01
The Josephson map describes nonlinear dynamics of systems characterized by standard map with the uniform external bias superposed. The intricate structures of the phase space portrait of the Josephson map are examined on the basis of the tangent map associated with the Josephson map. Numerical observation of the stochastic diffusion in the Josephson map is examined in comparison with the renormalized diffusion coefficient calculated by the method of characteristic function. The global stochasticity of the Josephson map occurs at the values of far smaller stochastic parameter than the case of the standard map. (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
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.
Ankyrins: Roles in synaptic biology and pathology.
Smith, Katharine R; Penzes, Peter
2018-05-03
Ankyrins are broadly expressed adaptors that organize diverse membrane proteins into specialized domains and link them to the sub-membranous cytoskeleton. In neurons, ankyrins are known to have essential roles in organizing the axon initial segment and nodes of Ranvier. However, recent studies have revealed novel functions for ankyrins at synapses, where they organize and stabilize neurotransmitter receptors, modulate dendritic spine morphology and control adhesion to the presynaptic site. Ankyrin genes have also been highly associated with a range of neurodevelopmental and psychiatric diseases, including bipolar disorder, schizophrenia and autism, which all demonstrate overlap in their genetics, mechanisms and phenotypes. This review discusses the novel synaptic functions of ankyrin proteins in neurons, and places these exciting findings in the context of ANK genes as key neuropsychiatric disorder risk-factors. Copyright © 2018 Elsevier Inc. All rights reserved.
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.
Optogenetic acidification of synaptic vesicles and lysosomes.
Rost, Benjamin R; Schneider, Franziska; Grauel, M Katharina; Wozny, Christian; Bentz, Claudia; Blessing, Anja; Rosenmund, Tanja; Jentsch, Thomas J; Schmitz, Dietmar; Hegemann, Peter; Rosenmund, Christian
2015-12-01
Acidification is required for the function of many intracellular organelles, but methods to acutely manipulate their intraluminal pH have not been available. Here we present a targeting strategy to selectively express the light-driven proton pump Arch3 on synaptic vesicles. Our new tool, pHoenix, can functionally replace endogenous proton pumps, enabling optogenetic control of vesicular acidification and neurotransmitter accumulation. Under physiological conditions, glutamatergic vesicles are nearly full, as additional vesicle acidification with pHoenix only slightly increased the quantal size. By contrast, we found that incompletely filled vesicles exhibited a lower release probability than full vesicles, suggesting preferential exocytosis of vesicles with high transmitter content. Our subcellular targeting approach can be transferred to other organelles, as demonstrated for a pHoenix variant that allows light-activated acidification of lysosomes.
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.
Chowdhury, Srikanta; Yamanaka, Akihiro
2016-01-01
Orexin/hypocretin neurons play a crucial role in the regulation of sleep/wakefulness, primarily in the maintenance of wakefulness. These neurons innervate wide areas of the brain and receive diverse synaptic inputs including those from serotonergic (5-HT) neurons in the raphe nucleus. Previously we showed that pharmacological application of 5-HT directly inhibited orexin neurons via 5-HT1A receptors. However, it was still unclear how 5-HT neurons regulated orexin neurons since 5-HT neurons co...
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.
Stochastic backgrounds of gravitational waves
International Nuclear Information System (INIS)
Maggiore, M.
2001-01-01
We review the motivations for the search for stochastic backgrounds of gravitational waves and we compare the experimental sensitivities that can be reached in the near future with the existing bounds and with the theoretical predictions. (author)
Stochastic theories of quantum mechanics
International Nuclear Information System (INIS)
De la Pena, L.; Cetto, A.M.
1991-01-01
The material of this article is organized into five sections. In Sect. I the basic characteristics of quantum systems are briefly discussed, with emphasis on their stochastic properties. In Sect. II a version of stochastic quantum mechanics is presented, to conclude that the quantum formalism admits an interpretation in terms of stochastic processes. In Sect. III the elements of stochastic electrodynamics are described, and its possibilities and limitations as a fundamental theory of quantum systems are discussed. Section IV contains a recent reformulation that overcomes the limitations of the theory discussed in the foregoing section. Finally, in Sect. V the theorems of EPR, Von Neumann and Bell are discussed briefly. The material is pedagogically presented and includes an ample list of references, but the details of the derivations are generally omitted. (Author)
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.)
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 singular optics (Conference paper)
CSIR Research Space (South Africa)
Roux, FS
2014-09-01
Full Text Available The study of optical vortices in stochastic optical fields involves various quantities, including the vortex density and topological charge density, that are defined in terms of local expectation values of distributions of optical vortices...
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)
Stochastic theory of fatigue corrosion
Hu, Haiyun
1999-10-01
A stochastic theory of corrosion has been constructed. The stochastic equations are described giving the transportation corrosion rate and fluctuation corrosion coefficient. In addition the pit diameter distribution function, the average pit diameter and the most probable pit diameter including other related empirical formula have been derived. In order to clarify the effect of stress range on the initiation and growth behaviour of pitting corrosion, round smooth specimen were tested under cyclic loading in 3.5% NaCl solution.
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 runaway of dynamical systems
International Nuclear Information System (INIS)
Pfirsch, D.; Graeff, P.
1984-10-01
One-dimensional, stochastic, dynamical systems are well studied with respect to their stability properties. Less is known for the higher dimensional case. This paper derives sufficient and necessary criteria for the asymptotic divergence of the entropy (runaway) and sufficient ones for the moments of n-dimensional, stochastic, dynamical systems. The crucial implication is the incompressibility of their flow defined by the equations of motion in configuration space. Two possible extensions to compressible flow systems are outlined. (orig.)
Stochastic Models of Polymer Systems
2016-01-01
Distribution Unlimited Final Report: Stochastic Models of Polymer Systems The views, opinions and/or findings contained in this report are those of the...ADDRESS. Princeton University PO Box 0036 87 Prospect Avenue - 2nd floor Princeton, NJ 08544 -2020 14-Mar-2014 ABSTRACT Number of Papers published in...peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Stochastic Models of Polymer Systems Report Title
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)
Optimal Liquidation under Stochastic Liquidity
Becherer, Dirk; Bilarev, Todor; Frentrup, Peter
2016-01-01
We solve explicitly a two-dimensional singular control problem of finite fuel type for infinite time horizon. The problem stems from the optimal liquidation of an asset position in a financial market with multiplicative and transient price impact. Liquidity is stochastic in that the volume effect process, which determines the inter-temporal resilience of the market in spirit of Predoiu, Shaikhet and Shreve (2011), is taken to be stochastic, being driven by own random noise. The optimal contro...
Memory effects on stochastic resonance
Neiman, Alexander; Sung, Wokyung
1996-02-01
We study the phenomenon of stochastic resonance (SR) in a bistable system with internal colored noise. In this situation the system possesses time-dependent memory friction connected with noise via the fluctuation-dissipation theorem, so that in the absence of periodic driving the system approaches the thermodynamic equilibrium state. For this non-Markovian case we find that memory usually suppresses stochastic resonance. However, for a large memory time SR can be enhanced by the memory.
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 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 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.
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.
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.
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.
Serotonin increases synaptic activity in olfactory bulb glomeruli.
Brill, Julia; Shao, Zuoyi; Puche, Adam C; Wachowiak, Matt; Shipley, Michael T
2016-03-01
Serotoninergic fibers densely innervate olfactory bulb glomeruli, the first sites of synaptic integration in the olfactory system. Acting through 5HT2A receptors, serotonin (5HT) directly excites external tufted cells (ETCs), key excitatory glomerular neurons, and depolarizes some mitral cells (MCs), the olfactory bulb's main output neurons. We further investigated 5HT action on MCs and determined its effects on the two major classes of glomerular interneurons: GABAergic/dopaminergic short axon cells (SACs) and GABAergic periglomerular cells (PGCs). In SACs, 5HT evoked a depolarizing current mediated by 5HT2C receptors but did not significantly impact spike rate. 5HT had no measurable direct effect in PGCs. Serotonin increased spontaneous excitatory and inhibitory postsynaptic currents (sEPSCs and sIPSCs) in PGCs and SACs. Increased sEPSCs were mediated by 5HT2A receptors, suggesting that they are primarily due to enhanced excitatory drive from ETCs. Increased sIPSCs resulted from elevated excitatory drive onto GABAergic interneurons and augmented GABA release from SACs. Serotonin-mediated GABA release from SACs was action potential independent and significantly increased miniature IPSC frequency in glomerular neurons. When focally applied to a glomerulus, 5HT increased MC spontaneous firing greater than twofold but did not increase olfactory nerve-evoked responses. Taken together, 5HT modulates glomerular network activity in several ways: 1) it increases ETC-mediated feed-forward excitation onto MCs, SACs, and PGCs; 2) it increases inhibition of glomerular interneurons; 3) it directly triggers action potential-independent GABA release from SACs; and 4) these network actions increase spontaneous MC firing without enhancing responses to suprathreshold sensory input. This may enhance MC sensitivity while maintaining dynamic range. Copyright © 2016 the American Physiological Society.
Stochastic Effects in Microstructure
Directory of Open Access Journals (Sweden)
Glicksman M.E.
2002-01-01
Full Text Available We are currently studying microstructural responses to diffusion-limited coarsening in two-phase materials. A mathematical solution to late-stage multiparticle diffusion in finite systems is formulated with account taken of particle-particle interactions and their microstructural correlations, or "locales". The transition from finite system behavior to that for an infinite microstructure is established analytically. Large-scale simulations of late-stage phase coarsening dynamics show increased fluctuations with increasing volume fraction, Vv, of the mean flux entering or leaving particles of a given size class. Fluctuations about the mean flux were found to depend on the scaled particle size, R/, where R is the radius of a particle and is the radius of the dispersoid averaged over the population within the microstructure. Specifically, small (shrinking particles tend to display weak fluctuations about their mean flux, whereas particles of average, or above average size, exhibit strong fluctuations. Remarkably, even in cases of microstructures with a relatively small volume fraction (Vv ~ 10-4, the particle size distribution is broader than that for the well-known Lifshitz-Slyozov limit predicted at zero volume fraction. The simulation results reported here provide some additional surprising insights into the effect of diffusion interactions and stochastic effects during evolution of a microstructure, as it approaches its thermodynamic end-state.
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...
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 ...
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...
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.
Li, Ying; van den Pol, Anthony N.
2010-01-01
In contrast to the local axons of GABA neurons of the cortex and hippocampus, lateral hypothalamic neurons containing melanin concentrating hormone (MCH) and GABA send long axons throughout the brain and play key roles in energy homeostasis and mental status. In adults, MCH neurons maintain a hyperpolarized membrane potential and most of the synaptic input is inhibitory. In contrast, we found that developing MCH neurons received substantially more excitatory synaptic input. Based on gramicidicin-perforated patch recordings in hypothalamic slices from MCH-GFP transgenic mice, we found that GABA was the primary excitatory synaptic transmitter in embryonic and neonatal ages up to postnatal day 10. Surprisingly, glutamate assumed only a minor excitatory role, if any. GABA plays a complex role in developing MCH neurons, with its actions conditionally dependent on a number of factors. GABA depolarization could lead to an increase in spikes either independently or in summation with other depolarizing stimuli, or alternately, depending on the relative timing of other depolarizing events, could lead to shunting inhibition. The developmental shift from depolarizing to hyperpolarizing occurred later in the dendrites than in the cell body. Early GABA depolarization was based on a Cl− dependent inward current. An interesting secondary depolarization in mature neurons that followed an initial hyperpolarization was based on a bicarbonate mechanism. Thus during the early developmental period when food consumption is high, MCH neurons are more depolarized than in the adult, and an increased level of excitatory synaptic input to these orexigenic cells is mediated by GABA. PMID:19955372
Li, Ying; van den Pol, Anthony N
2009-12-02
In contrast to the local axons of GABA neurons of the cortex and hippocampus, lateral hypothalamic neurons containing melanin concentrating hormone (MCH) and GABA send long axons throughout the brain and play key roles in energy homeostasis and mental status. In adults, MCH neurons maintain a hyperpolarized membrane potential and most of the synaptic input is inhibitory. In contrast, we found that developing MCH neurons received substantially more excitatory synaptic input. Based on gramicidin-perforated patch recordings in hypothalamic slices from MCH-green fluorescent protein transgenic mice, we found that GABA was the primary excitatory synaptic transmitter in embryonic and neonatal ages up to postnatal day 10. Surprisingly, glutamate assumed only a minor excitatory role, if any. GABA plays a complex role in developing MCH neurons, with its actions conditionally dependent on a number of factors. GABA depolarization could lead to an increase in spikes either independently or in summation with other depolarizing stimuli, or alternately, depending on the relative timing of other depolarizing events, could lead to shunting inhibition. The developmental shift from depolarizing to hyperpolarizing occurred later in the dendrites than in the cell body. Early GABA depolarization was based on a Cl(-)-dependent inward current. An interesting secondary depolarization in mature neurons that followed an initial hyperpolarization was based on a bicarbonate mechanism. Thus during the early developmental period when food consumption is high, MCH neurons are more depolarized than in the adult, and an increased level of excitatory synaptic input to these orexigenic cells is mediated by GABA.
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
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
Enhanced Input in LCTL Pedagogy
Directory of Open Access Journals (Sweden)
Marilyn S. Manley
2009-08-01
Full Text Available Language materials for the more-commonly-taught languages (MCTLs often include visual input enhancement (Sharwood Smith 1991, 1993 which makes use of typographical cues like bolding and underlining to enhance the saliency of targeted forms. For a variety of reasons, this paper argues that the use of enhanced input, both visual and oral, is especially important as a tool for the lesscommonly-taught languages (LCTLs. As there continues to be a scarcity of teaching resources for the LCTLs, individual teachers must take it upon themselves to incorporate enhanced input into their own self-made materials. Specific examples of how to incorporate both visual and oral enhanced input into language teaching are drawn from the author’s own experiences teaching Cuzco Quechua. Additionally, survey results are presented from the author’s Fall 2010 semester Cuzco Quechua language students, supporting the use of both visual and oral enhanced input.
Enhanced Input in LCTL Pedagogy
Directory of Open Access Journals (Sweden)
Marilyn S. Manley
2010-08-01
Full Text Available Language materials for the more-commonly-taught languages (MCTLs often include visual input enhancement (Sharwood Smith 1991, 1993 which makes use of typographical cues like bolding and underlining to enhance the saliency of targeted forms. For a variety of reasons, this paper argues that the use of enhanced input, both visual and oral, is especially important as a tool for the lesscommonly-taught languages (LCTLs. As there continues to be a scarcity of teaching resources for the LCTLs, individual teachers must take it upon themselves to incorporate enhanced input into their own self-made materials. Specific examples of how to incorporate both visual and oral enhanced input into language teaching are drawn from the author’s own experiences teaching Cuzco Quechua. Additionally, survey results are presented from the author’s Fall 2010 semester Cuzco Quechua language students, supporting the use of both visual and oral enhanced input.
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.
Directory of Open Access Journals (Sweden)
Joshua G.A Pinto
2015-02-01
Full Text Available Although many potential neuroplasticity based therapies have been developed in the lab, few have translated into established clinical treatments for human neurologic or neuropsychiatric diseases. Animal models, especially of the visual system, have shaped our understanding of neuroplasticity by characterizing the mechanisms that promote neural changes and defining timing of the sensitive period. The lack of knowledge about development of synaptic plasticity mechanisms in human cortex, and about alignment of synaptic age between animals and humans, has limited translation of neuroplasticity therapies. In this study, we quantified expression of a set of highly conserved pre- and post-synaptic proteins (Synapsin, Synaptophysin, PSD-95, Gephyrin and found that synaptic development in human primary visual cortex continues into late childhood. Indeed, this is many years longer than suggested by neuroanatomical studies and points to a prolonged sensitive period for plasticity in human sensory cortex. In addition, during childhood we found waves of inter-individual variability that are different for the 4 proteins and include a stage during early development (<1 year when only Gephyrin has high inter-individual variability. We also found that pre- and post-synaptic protein balances develop quickly, suggesting that maturation of certain synaptic functions happens within the first year or two of life. A multidimensional analysis (principle component analysis showed that most of the variance was captured by the sum of the 4 synaptic proteins. We used that sum to compare development of human and rat visual cortex and identified a simple linear equation that provides robust alignment of synaptic age between humans and rats. Alignment of synaptic ages is important for age-appropriate targeting and effective translation of neuroplasticity therapies from the lab to the clinic.
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.
A presynaptic role for PKA in synaptic tagging and memory
Park, Alan Jung; Havekes, Robbert; Choi, Jennifer H K; Luczak, Vincent; Nie, Ting; Huang, Ted; Abel, Ted
2014-01-01
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
Neuro-inspired computing using resistive synaptic devices
2017-01-01
This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. • Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level; • Includes detailed discussion of the peripheral circuits and array architecture design of the neuro-crossbar system; • Focuses on...
Learning and Memory, Part II: Molecular Mechanisms of Synaptic Plasticity
Lombroso, Paul; Ogren, Marilee
2009-01-01
The molecular events that are responsible for strengthening synaptic connections and how these are linked to memory and learning are discussed. The laboratory preparations that allow the investigation of these events are also described.
Multistate Resistive Switching Memory for Synaptic Memory Applications
Hota, Mrinal Kanti; Hedhili, Mohamed N.; Wehbe, Nimer; McLachlan, Martyn A.; Alshareef, Husam N.
2016-01-01
memory performance is observed. Conventional synaptic operation in terms of potentiation, depression plasticity, and Ebbinghaus forgetting process are also studied. The memory mechanism is shown to originate from the migration of the oxygen vacancies
Binocular Rivalry in a Competitive Neural Network with Synaptic Depression
Kilpatrick, Zachary P.; Bressloff, Paul C.
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
Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition.
Khubieh, Ayah; Ratté, Stéphanie; Lankarany, Milad; Prescott, Steven A
2016-08-01
The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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.
Synaptic Control of Secretory Trafficking in Dendrites
Directory of Open Access Journals (Sweden)
Cyril Hanus
2014-06-01
Full Text Available Localized signaling in neuronal dendrites requires tight spatial control of membrane composition. Upon initial synthesis, nascent secretory cargo in dendrites exits the endoplasmic reticulum (ER from local zones of ER complexity that are spatially coupled to post-ER compartments. Although newly synthesized membrane proteins can be processed locally, the mechanisms that control the spatial range of secretory cargo transport in dendritic segments are unknown. Here, we monitored the dynamics of nascent membrane proteins in dendritic post-ER compartments under regimes of low or increased neuronal activity. In response to activity blockade, post-ER carriers are highly mobile and are transported over long distances. Conversely, increasing synaptic activity dramatically restricts the spatial scale of post-ER trafficking along dendrites. This activity-induced confinement of secretory cargo requires site-specific phosphorylation of the kinesin motor KIF17 by Ca2+/calmodulin-dependent protein kinases (CaMK. Thus, the length scales of early secretory trafficking in dendrites are tuned by activity-dependent regulation of microtubule-dependent transport.
Design principles of electrical synaptic plasticity.
O'Brien, John
2017-09-08
Essentially all animals with nervous systems utilize electrical synapses as a core element of communication. Electrical synapses, formed by gap junctions between neurons, provide rapid, bidirectional communication that accomplishes tasks distinct from and complementary to chemical synapses. These include coordination of neuron activity, suppression of voltage noise, establishment of electrical pathways that define circuits, and modulation of high order network behavior. In keeping with the omnipresent demand to alter neural network function in order to respond to environmental cues and perform tasks, electrical synapses exhibit extensive plasticity. In some networks, this plasticity can have dramatic effects that completely remodel circuits or remove the influence of certain cell types from networks. Electrical synaptic plasticity occurs on three distinct time scales, ranging from milliseconds to days, with different mechanisms accounting for each. This essay highlights principles that dictate the properties of electrical coupling within networks and the plasticity of the electrical synapses, drawing examples extensively from retinal networks. Copyright © 2017 The Author. Published by Elsevier B.V. All rights reserved.
Epigenetic Basis of Neuronal and Synaptic Plasticity.
Karpova, Nina N; Sales, Amanda J; Joca, Samia R
2017-01-01
Neuronal network and plasticity change as a function of experience. Altered neural connectivity leads to distinct transcriptional programs of neuronal plasticity-related genes. The environmental challenges throughout life may promote long-lasting reprogramming of gene expression and the development of brain disorders. The modifications in neuronal epigenome mediate gene-environmental interactions and are required for activity-dependent regulation of neuronal differentiation, maturation and plasticity. Here, we highlight the latest advances in understanding the role of the main players of epigenetic machinery (DNA methylation and demethylation, histone modifications, chromatin-remodeling enzymes, transposons, and non-coding RNAs) in activity-dependent and long- term neural and synaptic plasticity. The review focuses on both the transcriptional and post-transcriptional regulation of gene expression levels, including the processes of promoter activation, alternative splicing, regulation of stability of gene transcripts by natural antisense RNAs, and alternative polyadenylation. Further, we discuss the epigenetic aspects of impaired neuronal plasticity and the pathogenesis of neurodevelopmental (Rett syndrome, Fragile X Syndrome, genomic imprinting disorders, schizophrenia, and others), stressrelated (mood disorders) and neurodegenerative Alzheimer's, Parkinson's and Huntington's disorders. The review also highlights the pharmacological compounds that modulate epigenetic programming of gene expression, the potential treatment strategies of discussed brain disorders, and the questions that should be addressed during the development of effective and safe approaches for the treatment of brain disorders.
Cholesterol asymmetry in synaptic plasma membranes.
Wood, W Gibson; Igbavboa, Urule; Müller, Walter E; Eckert, Gunter P
2011-03-01
Lipids are essential for the structural and functional integrity of membranes. Membrane lipids are not randomly distributed but are localized in different domains. A common characteristic of these membrane domains is their association with cholesterol. Lipid rafts and caveolae are examples of cholesterol enriched domains, which have attracted keen interest. However, two other important cholesterol domains are the exofacial and cytofacial leaflets of the plasma membrane. The two leaflets that make up the bilayer differ in their fluidity, electrical charge, lipid distribution, and active sites of certain proteins. The synaptic plasma membrane (SPM) cytofacial leaflet contains over 85% of the total SPM cholesterol as compared with the exofacial leaflet. This asymmetric distribution of cholesterol is not fixed or immobile but can be modified by different conditions in vivo: (i) chronic ethanol consumption; (ii) statins; (iii) aging; and (iv) apoE isoform. Several potential candidates have been proposed as mechanisms involved in regulation of SPM cholesterol asymmetry: apoE, low-density lipoprotein receptor, sterol carrier protein-2, fatty acid binding proteins, polyunsaturated fatty acids, P-glycoprotein and caveolin-1. This review examines cholesterol asymmetry in SPM, potential mechanisms of regulation and impact on membrane structure and function. © 2011 The Authors. Journal of Neurochemistry © 2011 International Society for Neurochemistry.
Directory of Open Access Journals (Sweden)
Harsh Sancheti
Full Text Available Alzheimer's disease is a progressive neurodegenerative disease that entails impairments of memory, thinking and behavior and culminates into brain atrophy. Impaired glucose uptake (accumulating into energy deficits and synaptic plasticity have been shown to be affected in the early stages of Alzheimer's disease. This study examines the ability of lipoic acid to increase brain glucose uptake and lead to improvements in synaptic plasticity on a triple transgenic mouse model of Alzheimer's disease (3xTg-AD that shows progression of pathology as a function of age; two age groups: 6 months (young and 12 months (old were used in this study. 3xTg-AD mice fed 0.23% w/v lipoic acid in drinking water for 4 weeks showed an insulin mimetic effect that consisted of increased brain glucose uptake, activation of the insulin receptor substrate and of the PI3K/Akt signaling pathway. Lipoic acid supplementation led to important changes in synaptic function as shown by increased input/output (I/O and long term potentiation (LTP (measured by electrophysiology. Lipoic acid was more effective in stimulating an insulin-like effect and reversing the impaired synaptic plasticity in the old mice, wherein the impairment of insulin signaling and synaptic plasticity was more pronounced than those in young mice.
Reduced Synaptic Vesicle Recycling during Hypoxia in Cultured Cortical Neurons
Fedorovich, Sergei; Hofmeijer, Jeannette; van Putten, Michel Johannes Antonius Maria; le Feber, Jakob
2017-01-01
Improvement of neuronal recovery in the ischemic penumbra, an area around the core of a brain infarct with some remaining perfusion, has a large potential for the development of therapy against acute ischemic stroke. However, mechanisms that lead to either recovery or secondary damage in the penumbra largely remain unclear. Recent studies in cultured networks of cortical neurons showed that failure of synaptic transmission (referred to as synaptic failure) is a critical factor in the penumbra...
Common mechanisms of synaptic plasticity in vertebrates and invertebrates
Glanzman, David L.
2016-01-01
Until recently, the literature on learning-related synaptic plasticity in invertebrates has been dominated by models assuming plasticity is mediated by presynaptic changes, whereas the vertebrate literature has been dominated by models assuming it is mediated by postsynaptic changes. Here I will argue that this situation does not reflect a biological reality and that, in fact, invertebrate and vertebrate nervous systems share a common set of mechanisms of synaptic plasticity. PMID:20152143
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...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...
Bottom-up and Top-down Input Augment the Variability of Cortical Neurons
Nassi, Jonathan J.; Kreiman, Gabriel; Born, Richard T.
2016-01-01
SUMMARY Neurons in the cerebral cortex respond inconsistently to a repeated sensory stimulus, yet they underlie our stable sensory experiences. Although the nature of this variability is unknown, its ubiquity has encouraged the general view that each cell produces random spike patterns that noisily represent its response rate. In contrast, here we show that reversibly inactivating distant sources of either bottom-up or top-down input to cortical visual areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variability of single neurons. A simple model in which a fraction of the pre-synaptic input is silenced can reproduce this reduction in variability, provided that there exist temporal correlations primarily within, but not between, excitatory and inhibitory input pools. A large component of the variability of cortical neurons may therefore arise from synchronous input produced by signals arriving from multiple sources. PMID:27427459