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

Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

Science.gov (United States)

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.

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

Short- and long-term modulation of synaptic inputs to brain reward areas by nicotine

NARCIS (Netherlands)

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.

Orientation selectivity of synaptic input to neurons in mouse and cat primary visual cortex.

Science.gov (United States)

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.

Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex.

Science.gov (United States)

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.

Noise Analysis of Single-Ended Input Differential Amplifier using Stochastic Differential Equation

OpenAIRE

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...

Decreased spinal synaptic inputs to phrenic motor neurons elicit localized inactivity-induced phrenic motor facilitation

Science.gov (United States)

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

Stochastic synaptic plasticity with memristor crossbar arrays

KAUST Repository

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

KAUST Repository

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.

High pressure and [Ca2+] produce an inverse modulation of synaptic input strength, network excitability and frequency response in the rat dentate gyrus

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

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

Decreased spinal synaptic inputs to phrenic motor neurons elicit localized inactivity-induced phrenic motor facilitation.

Science.gov (United States)

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.

Suprathreshold stochastic resonance in neural processing tuned by correlation.

Science.gov (United States)

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.

Sex Differences in Medium Spiny Neuron Excitability and Glutamatergic Synaptic Input: Heterogeneity Across Striatal Regions and Evidence for Estradiol-Dependent Sexual Differentiation

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.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Science.gov (United States)

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

2016-01-01

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

Estimation of the synaptic input firing rates and characterization of the stimulation effects in an auditory neuron

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

A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

KAUST Repository

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.

Decreased spinal synaptic inputs to phrenic motor neurons elicit localized inactivity-induced phrenic motor facilitation

OpenAIRE

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...

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.

Spacecraft reorientation control in presence of attitude constraint considering input saturation and stochastic disturbance

Science.gov (United States)

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.

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

Local establishment of repetitive long-term potentiation-induced synaptic enhancement in cultured hippocampal slices with divided input pathways.

Science.gov (United States)

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.

Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

Science.gov (United States)

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

Characteristic effects of stochastic oscillatory forcing on neural firing: analytical theory and comparison to paddlefish electroreceptor data.

Science.gov (United States)

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.

Persistence and extinction of a stochastic single-species population model in a polluted environment with impulsive toxicant input

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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.

The role of additive neurogenesis and synaptic plasticity in a hippocampal memory model with grid-cell like input.

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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

The temporoammonic input to the hippocampal CA1 region displays distinctly different synaptic plasticity compared to the Schaffer collateral input in vivo: significance for synaptic information processing

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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.

Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs

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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

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

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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

Statistical mechanics of attractor neural network models with synaptic depression

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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.

Enhancement of information transmission with stochastic resonance in hippocampal CA1 neuron models: effects of noise input location.

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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.

An asymptotic-preserving stochastic Galerkin method for the radiative heat transfer equations with random inputs and diffusive scalings

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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.

Inverse stochastic resonance in networks of spiking neurons.

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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.

Distinct Subunit Domains Govern Synaptic Stability and Specificity of the Kainate Receptor

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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.

Flexible Proton-Gated Oxide Synaptic Transistors on Si Membrane.

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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.

Asymmetry between ON and OFF α ganglion cells of mouse retina: integration of signal and noise from synaptic inputs.

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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

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

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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.

Inverse Stochastic Resonance in Cerebellar Purkinje Cells.

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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.

Fractional Gaussian noise-enhanced information capacity of a nonlinear neuron model with binary signal input

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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.

Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures and Input Hysteresis.

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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.

Influence of Synaptic Depression on Memory Storage Capacity

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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.

L{sup 1} group consensus of multi-agent systems with switching topologies and stochastic inputs

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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.

Synaptic integration of transplanted interneuron progenitor cells into native cortical networks.

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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.

Alteration of synaptic connectivity of oligodendrocyte precursor cells following demyelination

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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

Sensory optimization by stochastic tuning.

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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.

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

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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.

Synaptic inputs compete during rapid formation of the calyx of Held: a new model system for neural development.

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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.

Feedforward inhibition and synaptic scaling--two sides of the same coin?

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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?

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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.

Stability Analysis on Sparsely Encoded Associative Memory with Short-Term Synaptic Dynamics

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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.

The Thouless-Anderson-Palmer equation for an analogue neural network with temporally fluctuating white synaptic noise

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

Location-dependent excitatory synaptic interactions in pyramidal neuron dendrites.

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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.

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Science.gov (United States)

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

Brain-inspired Stochastic Models and Implementations

KAUST Repository

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.

SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING.

Science.gov (United States)

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.

Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

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.

Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

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

Cortically-controlled population stochastic facilitation as a plausible substrate for guiding sensory transfer across the thalamic gateway.

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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

Development of inhibitory synaptic inputs on layer 2/3 pyramidal neurons in the rat medial prefrontal cortex

KAUST Repository

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.

Development of inhibitory synaptic inputs on layer 2/3 pyramidal neurons in the rat medial prefrontal cortex

KAUST Repository

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.

Maturation of GABAergic inhibition promotes strengthening of temporally coherent inputs among convergent pathways.

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.

Asymmetric Temporal Integration of Layer 4 and Layer 2/3 Inputs in Visual Cortex

OpenAIRE

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...

Raindrops of synaptic noise on dual excitability landscape: an approach to astrocyte network modelling

Science.gov (United States)

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.

Recurrent network models for perfect temporal integration of fluctuating correlated inputs.

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Hiroshi Okamoto

2009-06-01

Full Text Available Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.

Stochastic 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...

Synaptic excitation in spinal motoneurons alternates with synaptic inhibition and is balanced by outward rectification during rhythmic motor network activity

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...

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.

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...

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

Importance of vesicle release stochasticity in neuro-spike communication.

Science.gov (United States)

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.

Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams

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.

TGF-β Signaling in Dopaminergic Neurons Regulates Dendritic Growth, Excitatory-Inhibitory Synaptic Balance, and Reversal Learning

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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.

Hardwiring of fine synaptic layers in the zebrafish visual pathway

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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.

Early neonatal loss of inhibitory synaptic input to the spinal motor neurons confers spina bifida-like leg dysfunction in a chicken model

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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.

The Corticohippocampal Circuit, Synaptic Plasticity, and Memory

Science.gov (United States)

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

Familiarity Detection is an Intrinsic Property of Cortical Microcircuits with Bidirectional Synaptic Plasticity.

Science.gov (United States)

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.

Operant conditioning of synaptic and spiking activity patterns in single hippocampal neurons.

Science.gov (United States)

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.

Intense synaptic activity enhances temporal resolution in spinal motoneurons.

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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.

Robust short-term memory without synaptic learning.

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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.

Science.gov (United States)

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.

Robust Short-Term Memory without Synaptic Learning

Science.gov (United States)

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

Stabilization of memory States by stochastic facilitating synapses.

Science.gov (United States)

Miller, Paul

2013-12-06

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

Long lasting protein synthesis- and activity-dependent spine shrinkage and elimination after synaptic depression.

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.

Synaptic metaplasticity underlies tetanic potentiation in Lymnaea: a novel paradigm.

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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.

Readily releasable pool of synaptic vesicles measured at single synaptic contacts.

Science.gov (United States)

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.

Precise synaptic efficacy alignment suggests potentiation dominated learning

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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

A new chance-constrained DEA model with birandom input and output data

OpenAIRE

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-...

Synaptic Cell Adhesion

OpenAIRE

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...

Models of the stochastic activity of neurones

CERN Document Server

Holden, Arun Vivian

1976-01-01

These notes have grown from a series of seminars given at Leeds between 1972 and 1975. They represent an attempt to gather together the different kinds of model which have been proposed to account for the stochastic activity of neurones, and to provide an introduction to this area of mathematical biology. A striking feature of the electrical activity of the nervous system is that it appears stochastic: this is apparent at all levels of recording, ranging from intracellular recordings to the electroencephalogram. The chapters start with fluctuations in membrane potential, proceed through single unit and synaptic activity and end with the behaviour of large aggregates of neurones: L have chgaen this seque~~e\\/~~';uggest that the interesting behaviourr~f :the nervous system - its individuality, variability and dynamic forms - may in part result from the stochastic behaviour of its components. I would like to thank Dr. Julio Rubio for reading and commenting on the drafts, Mrs. Doris Beighton for producing the fin...

Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise.

KAUST Repository

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.

Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study

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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.

Effects of active conductance distribution over dendrites on the synaptic integration in an identified nonspiking interneuron.

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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

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.

EARLY GUIDANCE FOR ASSIGNING DISTRIBUTION PARAMETERS TO GEOCHEMICAL INPUT TERMS TO STOCHASTIC TRANSPORT MODELS

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

Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

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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.

Asymmetric temporal integration of layer 4 and layer 2/3 inputs in visual cortex.

Science.gov (United States)

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.

Synaptic Mechanisms Generating Orientation Selectivity in the ON Pathway of the Rabbit Retina.

Science.gov (United States)

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

Dynamics of stochastic systems

CERN Document Server

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 ...

Reversing the outcome of synapse elimination at developing neuromuscular junctions in vivo: evidence for synaptic competition and its mechanism.

Science.gov (United States)

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.

Synaptic Conductance Estimates of the Connection Between Local Inhibitor Interneurons and Pyramidal Neurons in Layer 2/3 of a Cortical Column

Science.gov (United States)

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

Synaptic communication between neurons and NG2+ cells.

Science.gov (United States)

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.

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

Interregional synaptic maps among engram cells underlie memory formation.

Science.gov (United States)

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.

Stochasticity in Ca2+ increase in spines enables robust and sensitive information coding.

Directory of Open Access Journals (Sweden)

Takuya Koumura

Full Text Available A dendritic spine is a very small structure (∼0.1 µm3 of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca2+ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca2+ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca2+ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca2+ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca2+ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.

The role of cAMP in synaptic homeostasis in response to environmental temperature challenges and hyperexcitability mutations

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.

Reversing the outcome of synapse elimination at developing neuromuscular junctions in vivo: evidence for synaptic competition and its mechanism.

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.

Input modeling with phase-type distributions and Markov models theory and applications

CERN Document Server

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...

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

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

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

Science.gov (United States)

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.

Stochastic Systems Uncertainty Quantification and Propagation

CERN Document Server

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...

Spike timing precision of neuronal circuits.

Science.gov (United States)

Kilinc, Deniz; Demir, Alper

2018-04-17

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

Synaptic Plasticity, Dementia and Alzheimer Disease.

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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.

Bidirectional Classical Stochastic Processes with Measurements and Feedback

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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.

Bidirectional control of social hierarchy by synaptic efficacy in medial prefrontal cortex.

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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.

Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy: A computational study.

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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.

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...

Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes

Science.gov (United States)

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.

STOCHASTIC METHODS IN RISK ANALYSIS

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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.

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

H∞ Excitation Control Design for Stochastic Power Systems with Input Delay Based on Nonlinear Hamiltonian System Theory

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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.

Distal axotomy enhances retrograde presynaptic excitability onto injured pyramidal neurons via trans-synaptic signaling.

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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.

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

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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.

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.

Mechanisms of glycine release, which build up synaptic and extrasynaptic glycine levels: the role of synaptic and non-synaptic glycine transporters.

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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

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...

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)

How input fluctuations reshape the dynamics of a biological switching system

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Hu, Bo; Kessler, David A.; Rappel, Wouter-Jan; Levine, Herbert

2012-12-01

An important task in quantitative biology is to understand the role of stochasticity in biochemical regulation. Here, as an extension of our recent work [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.107.148101 107, 148101 (2011)], we study how input fluctuations affect the stochastic dynamics of a simple biological switch. In our model, the on transition rate of the switch is directly regulated by a noisy input signal, which is described as a non-negative mean-reverting diffusion process. This continuous process can be a good approximation of the discrete birth-death process and is much more analytically tractable. Within this setup, we apply the Feynman-Kac theorem to investigate the statistical features of the output switching dynamics. Consistent with our previous findings, the input noise is found to effectively suppress the input-dependent transitions. We show analytically that this effect becomes significant when the input signal fluctuates greatly in amplitude and reverts slowly to its mean.

All-optical functional synaptic connectivity mapping in acute brain slices using the calcium integrator CaMPARI.

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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

Synaptic reorganization of inhibitory hilar interneuron circuitry after traumatic brain injury in mice

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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

HDAC2 expression in parvalbumin interneurons regulates synaptic plasticity in the mouse visual cortex

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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.

HDAC2 expression in parvalbumin interneurons regulates synaptic plasticity in the mouse visual cortex.

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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.

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

Science.gov (United States)

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.

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...

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.

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.

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

Science.gov (United States)

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

2015-01-01

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

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

Science.gov (United States)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

Metabolic Turnover of Synaptic Proteins: Kinetics, Interdependencies and Implications for Synaptic Maintenance

Science.gov (United States)

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

Schizophrenia-Related Microdeletion Impairs Emotional Memory through MicroRNA-Dependent Disruption of Thalamic Inputs to the Amygdala

Directory of Open Access Journals (Sweden)

Tae-Yeon Eom

2017-05-01

Full Text Available Individuals with 22q11.2 deletion syndrome (22q11DS are at high risk of developing psychiatric diseases such as schizophrenia. Individuals with 22q11DS and schizophrenia are impaired in emotional memory, anticipating, recalling, and assigning a correct context to emotions. The neuronal circuits responsible for these emotional memory deficits are unknown. Here, we show that 22q11DS mouse models have disrupted synaptic transmission at thalamic inputs to the lateral amygdala (thalamo-LA projections. This synaptic deficit is caused by haploinsufficiency of the 22q11DS gene Dgcr8, which is involved in microRNA processing, and is mediated by the increased dopamine receptor Drd2 levels in the thalamus and by reduced probability of glutamate release from thalamic inputs. This deficit in thalamo-LA synaptic transmission is sufficient to cause fear memory deficits. Our results suggest that dysregulation of the Dgcr8–Drd2 mechanism at thalamic inputs to the amygdala underlies emotional memory deficits in 22q11DS.

Schizophrenia-Related Microdeletion Impairs Emotional Memory through MicroRNA-Dependent Disruption of Thalamic Inputs to the Amygdala.

Science.gov (United States)

Eom, Tae-Yeon; Bayazitov, Ildar T; Anderson, Kara; Yu, Jing; Zakharenko, Stanislav S

2017-05-23

Individuals with 22q11.2 deletion syndrome (22q11DS) are at high risk of developing psychiatric diseases such as schizophrenia. Individuals with 22q11DS and schizophrenia are impaired in emotional memory, anticipating, recalling, and assigning a correct context to emotions. The neuronal circuits responsible for these emotional memory deficits are unknown. Here, we show that 22q11DS mouse models have disrupted synaptic transmission at thalamic inputs to the lateral amygdala (thalamo-LA projections). This synaptic deficit is caused by haploinsufficiency of the 22q11DS gene Dgcr8, which is involved in microRNA processing, and is mediated by the increased dopamine receptor Drd2 levels in the thalamus and by reduced probability of glutamate release from thalamic inputs. This deficit in thalamo-LA synaptic transmission is sufficient to cause fear memory deficits. Our results suggest that dysregulation of the Dgcr8-Drd2 mechanism at thalamic inputs to the amygdala underlies emotional memory deficits in 22q11DS. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

A central pattern generator producing alternative outputs: pattern, strength, and dynamics of premotor synaptic input to leech heart motor neurons.

Science.gov (United States)

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.

Phonology: An Emergent Consequence of Memory Constraints and Sensory Input.

Science.gov (United States)

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…

Peripheral Sensory Deprivation Restores Critical-Period-like Plasticity to Adult Somatosensory Thalamocortical Inputs

Directory of Open Access Journals (Sweden)

Seungsoo Chung

2017-06-01

Full Text Available Recent work has shown that thalamocortical (TC inputs can be plastic after the developmental critical period has closed, but the mechanism that enables re-establishment of plasticity is unclear. Here, we find that long-term potentiation (LTP at TC inputs is transiently restored in spared barrel cortex following either a unilateral infra-orbital nerve (ION lesion, unilateral whisker trimming, or unilateral ablation of the rodent barrel cortex. Restoration of LTP is associated with increased potency at TC input and reactivates anatomical map plasticity induced by whisker follicle ablation. The reactivation of TC LTP is accompanied by reappearance of silent synapses. Both LTP and silent synapse formation are preceded by transient re-expression of synaptic GluN2B-containing N-methyl-D-aspartate (NMDA receptors, which are required for the reappearance of TC plasticity. These results clearly demonstrate that peripheral sensory deprivation reactivates synaptic plasticity in the mature layer 4 barrel cortex with features similar to the developmental critical period.

Electrophysical properties, synaptic transmission and neuromodulation in serotonergic caudal raphe neurons.

Science.gov (United States)

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.

Isolation of Synaptosomes, Synaptic Plasma Membranes, and Synaptic Junctional Complexes.

Science.gov (United States)

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

Voltage-dependent amplification of synaptic inputs in respiratory motoneurones

Science.gov (United States)

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

Synaptic and intrinsic activation of GABAergic neurons in the cardiorespiratory brainstem network.

Science.gov (United States)

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

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

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

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.

Mechanisms of Synaptic Alterations in a Neuroinflammation Model of Autism

Science.gov (United States)

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

Synaptic Basis for Differential Orientation Selectivity between Complex and Simple Cells in Mouse Visual Cortex.

Science.gov (United States)

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

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

Science.gov (United States)

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.

Common Input to Motor Units of Intrinsic and Extrinsic Hand Muscles During Two-Digit Object Hold

OpenAIRE

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...

Influence of active dendritic currents on input-output processing in spinal motoneurons in vivo.

Science.gov (United States)

Lee, R H; Kuo, J J; Jiang, M C; Heckman, C J

2003-01-01

The extensive dendritic tree of the adult spinal motoneuron generates a powerful persistent inward current (PIC). We investigated how this dendritic PIC influenced conversion of synaptic input to rhythmic firing. A linearly increasing, predominantly excitatory synaptic input was generated in triceps ankle extensor motoneurons by slow stretch (duration: 2-10 s) of the Achilles tendon in the decerebrate cat preparation. The firing pattern evoked by stretch was measured by injecting a steady current to depolarize the cell to threshold for firing. The effective synaptic current (I(N), the net synaptic current reaching the soma of the cell) evoked by stretch was measured during voltage clamp. Hyperpolarized holding potentials were used to minimize the activation of the dendritic PIC and thus estimate stretch-evoked I(N) for a passive dendritic tree (I(N,PASS)). Depolarized holding potentials that approximated the average membrane potential during rhythmic firing allowed strong activation of the dendritic PIC and thus resulted in marked enhancement of the total stretch-evoked I(N) (I(N,TOT)). The net effect of the dendritic PIC on the generation of rhythmic firing was assessed by plotting stretch-evoked firing (strong PIC activation) versus stretch-evoked I(N,PASS) (minimal PIC activation). The gain of this input-output function for the neuron (I-O(N)) was found to be ~2.7 times as high as for the standard injected frequency current (F-I) function in low-input conductance neurons. However, about halfway through the stretch, firing rate tended to become constant, resulting in a sharp saturation in I-O(N) that was not present in F-I. In addition, the gain of I-O(N) decreased sharply with increasing input conductance, resulting in much lower stretch-evoked firing rates in high-input conductance cells. All three of these phenomena (high initial gain, saturation, and differences in low- and high-input conductance cells) were also readily apparent in the differences between

Glucose rapidly induces different forms of excitatory synaptic plasticity in hypothalamic POMC neurons.

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.

Glucose rapidly induces different forms of excitatory synaptic plasticity in hypothalamic POMC neurons.

Science.gov (United States)

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.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

Science.gov (United States)

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.

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...

Synaptic electronics: materials, devices and applications.

Science.gov (United States)

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)

Banach Synaptic Algebras

Science.gov (United States)

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.

Lectures on Dynamics of Stochastic Systems

CERN Document Server

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

Streptavidin-conjugated CdSe/ZnS quantum dots impaired synaptic plasticity and spatial memory process

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.

Streptavidin-conjugated CdSe/ZnS quantum dots impaired synaptic plasticity and spatial memory process

Science.gov (United States)

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.

Streptavidin-conjugated CdSe/ZnS quantum dots impaired synaptic plasticity and spatial memory process

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.

The development of the deterministic nonlinear PDEs in particle physics to stochastic case

Science.gov (United States)

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.

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...

EDITORIAL: Synaptic electronics Synaptic electronics

Science.gov (United States)

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

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.

Synaptic transmission modulates while non-synaptic processes govern the transition from pre-ictal to seizure activity in vitro

OpenAIRE

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...

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, formingtrans-complexes spanning the synaptic cleft orcis-complexes attached to the same synaptic membrane. SAMs play key roles in cell adhesion and in organizing protein interaction networks; they can also provide mechanisms of recognition, generate scaffolds onto which partners can dock, and likely take part in signaling processes as well. SAMs are regulated through a portfolio of different mechanisms that affect their protein levels, precise localization, stability, and the availability of their partners at synapses. Interaction of SAMs with their partners can further be strengthened or weakened through alternative splicing, competing protein partners, ectodomain shedding, or astrocytically secreted factors. Given that numerous SAMs appear altered by synaptic activity, in vivo, these molecules may be used to dynamically scale up or scale down synaptic communication. Many SAMs, including neurexins, neuroligins, cadherins, and contactins, are now implicated in neuropsychiatric and neurodevelopmental diseases, such as autism spectrum disorder, schizophrenia, and bipolar disorder and studying their molecular mechanisms holds promise for developing novel therapeutics.

Short-term Synaptic Depression in the Feedforward Inhibitory Circuit in the Dorsal Lateral Geniculate Nucleus.

Science.gov (United States)

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.

Glucose Rapidly Induces Different Forms of Excitatory Synaptic Plasticity in Hypothalamic POMC Neurons

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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

Stochastic modeling of thermal fatigue crack growth

CERN Document Server

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...

Distinct roles of synaptic and extrasynaptic GABAA receptors in striatal inhibition dynamics

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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

SRC Inhibition Reduces NR2B Surface Expression and Synaptic Plasticity in the Amygdala

Science.gov (United States)

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…

Enhanced excitatory input to MCH neurons during developmental period of high food intake is mediated by GABA

Science.gov (United States)

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

Developmental profiles of the intrinsic properties and synaptic function of auditory neurons in preterm and term baboon neonates.

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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.

Spectrotemporal dynamics of auditory cortical synaptic receptive field plasticity.

Science.gov (United States)

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.

Deciphering resting microglial morphology and process motility from a synaptic prospect

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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.

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film.

Science.gov (United States)

Chang, C T; Zeng, F; Li, X J; Dong, W S; Lu, S H; Gao, S; Pan, F

2016-01-07

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.

A Markovian event-based framework for stochastic spiking neural networks.

Science.gov (United States)

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

On the adaptivity gap of stochastic orienteering

NARCIS (Netherlands)

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

NARCIS (Netherlands)

N. Bansal (Nikhil); V. Nagarajan

2013-01-01

htmlabstractThe input to the stochastic orienteering problem consists of a budget B and metric (V,d) where each vertex v has a job with deterministic reward and random processing time (drawn from a known distribution). The processing times are independent across vertices. The goal is to obtain a

Active fault diagnosis based on stochastic tests

DEFF Research Database (Denmark)

Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2008-01-01

The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...

The coincident activation of lemniscal and paralemniscal inputs can drive synaptic plasticity in layer 2/3 pyramidal neurons of the mouse somatosensory cortex in vivo

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

Collective stochastic coherence in recurrent neuronal networks

Science.gov (United States)

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.

Stochastic Watershed Models for Risk Based Decision Making

Science.gov (United States)

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

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

Physiological and morphological characterization of organotypic cocultures of the chick forebrain area MNH and its main input area DMA/DMP.

Science.gov (United States)

Endepols, H; Jungnickel, J; Braun, K

2001-01-01

Cocultures of the learning-relevant forebrain region mediorostral neostriatum and hyperstriatum ventrale (MNH) and its main glutamatergic input area nucleus dorsomedialis anterior thalami/posterior thalami were morphologically and physiologically characterized. Synaptic contacts of thalamic fibers were light- and electron-microscopically detected on MNH neurons by applying the fluorescence tracer DiI-C18(3) into the thalamus part of the coculture. Most thalamic synapses on MNH neurons were symmetric and located on dendritic shafts, but no correlation between Gray-type ultrastructure and dendritic localization was found. Using intracellular current clamp recordings, we found that the electrophysiological properties, such as input resistance, time constant, action potential threshold, amplitude, and duration of MNH neurons, remain stable for over 30 days in vitro. Pharmacological blockade experiments revealed glutamate as the main neurotransmitter of thalamic synapses on MNH neurons, which were also found on inhibitory neurons. High frequency stimulation of thalamic inputs evoked synaptic potentiation in 22% of MNH neurons. The results indicate that DMA/DMP-MNH cocultures, which can be maintained under stable conditions for at least 4 weeks, provide an attractive in vitro model for investigating synaptic plasticity in the avian brain.

Inhibitory Gating of Basolateral Amygdala Inputs to the Prefrontal Cortex.

Science.gov (United States)

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

Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis

Science.gov (United States)

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

Survival motor neuron protein in motor neurons determines synaptic integrity in spinal muscular atrophy.

Science.gov (United States)

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.

Calcium Input Frequency, Duration and Amplitude Differentially Modulate the Relative Activation of Calcineurin and CaMKII

Science.gov (United States)

Li, Lu; Stefan, Melanie I.; Le Novère, Nicolas

2012-01-01

NMDA receptor dependent long-term potentiation (LTP) and long-term depression (LTD) are two prominent forms of synaptic plasticity, both of which are triggered by post-synaptic calcium elevation. To understand how calcium selectively stimulates two opposing processes, we developed a detailed computational model and performed simulations with different calcium input frequencies, amplitudes, and durations. We show that with a total amount of calcium ions kept constant, high frequencies of calcium pulses stimulate calmodulin more efficiently. Calcium input activates both calcineurin and Ca2+/calmodulin-dependent protein kinase II (CaMKII) at all frequencies, but increased frequencies shift the relative activation from calcineurin to CaMKII. Irrespective of amplitude and duration of the inputs, the total amount of calcium ions injected adjusts the sensitivity of the system to calcium input frequencies. At a given frequency, the quantity of CaMKII activated is proportional to the total amount of calcium. Thus, an input of a small amount of calcium at high frequencies can induce the same activation of CaMKII as a larger amount, at lower frequencies. Finally, the extent of activation of CaMKII signals with high calcium frequency is further controlled by other factors, including the availability of calmodulin, and by the potency of phosphatase inhibitors. PMID:22962589

Gating of Long-Term Potentiation by Nicotinic Acetylcholine Receptors at the Cerebellum Input Stage

NARCIS (Netherlands)

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

Input-Timing-Dependent Plasticity in the Hippocampal CA2 Region and Its Potential Role in Social Memory.

Science.gov (United States)

Leroy, Felix; Brann, David H; Meira, Torcato; Siegelbaum, Steven A

2017-08-30

Input-timing-dependent plasticity (ITDP) is a circuit-based synaptic learning rule by which paired activation of entorhinal cortical (EC) and Schaffer collateral (SC) inputs to hippocampal CA1 pyramidal neurons (PNs) produces a long-term enhancement of SC excitation. We now find that paired stimulation of EC and SC inputs also induces ITDP of SC excitation of CA2 PNs. However, whereas CA1 ITDP results from long-term depression of feedforward inhibition (iLTD) as a result of activation of CB1 endocannabinoid receptors on cholecystokinin-expressing interneurons, CA2 ITDP results from iLTD through activation of δ-opioid receptors on parvalbumin-expressing interneurons. Furthermore, whereas CA1 ITDP has been previously linked to enhanced specificity of contextual memory, we find that CA2 ITDP is associated with enhanced social memory. Thus, ITDP may provide a general synaptic learning rule for distinct forms of hippocampal-dependent memory mediated by distinct hippocampal regions. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

Somatostatin-positive interneurons in the dentate gyrus of mice provide local- and long-range septal synaptic inhibition.

Science.gov (United States)

Yuan, Mei; Meyer, Thomas; Benkowitz, Christoph; Savanthrapadian, Shakuntala; Ansel-Bollepalli, Laura; Foggetti, Angelica; Wulff, Peer; Alcami, Pepe; Elgueta, Claudio; Bartos, Marlene

2017-04-03

Somatostatin-expressing-interneurons (SOMIs) in the dentate gyrus (DG) control formation of granule cell (GC) assemblies during memory acquisition. Hilar-perforant-path-associated interneurons (HIPP cells) have been considered to be synonymous for DG-SOMIs. Deviating from this assumption, we show two functionally contrasting DG-SOMI-types. The classical feedback-inhibitory HIPPs distribute axon fibers in the molecular layer. They are engaged by converging GC-inputs and provide dendritic inhibition to the DG circuitry. In contrast, SOMIs with axon in the hilus, termed hilar interneurons (HILs), provide perisomatic inhibition onto GABAergic cells in the DG and project to the medial septum. Repetitive activation of glutamatergic inputs onto HIPP cells induces long-lasting-depression (LTD) of synaptic transmission but long-term-potentiation (LTP) of synaptic signals in HIL cells. Thus, LTD in HIPPs may assist flow of spatial information from the entorhinal cortex to the DG, whereas LTP in HILs may facilitate the temporal coordination of GCs with activity patterns governed by the medial septum.

Stabilization of (state, input)-disturbed CSTRs through the port-Hamiltonian systems approach

OpenAIRE

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...

Evaluating Kuala Lumpur stock exchange oriented bank performance with stochastic frontiers

International Nuclear Information System (INIS)

Baten, M. A.; Maznah, M. K.; Razamin, R.; Jastini, M. J.

2014-01-01

Banks play an essential role in the economic development and banks need to be efficient; otherwise, they may create blockage in the process of development in any country. The efficiency of banks in Malaysia is important and should receive greater attention. This study formulated an appropriate stochastic frontier model to investigate the efficiency of banks which are traded on Kuala Lumpur Stock Exchange (KLSE) market during the period 2005–2009. All data were analyzed to obtain the maximum likelihood method to estimate the parameters of stochastic production. Unlike the earlier studies which use balance sheet and income statements data, this study used market data as the input and output variables. It was observed that banks listed in KLSE exhibited a commendable overall efficiency level of 96.2% during 2005–2009 hence suggesting minimal input waste of 3.8%. Among the banks, the COMS (Cimb Group Holdings) bank is found to be highly efficient with a score of 0.9715 and BIMB (Bimb Holdings) bank is noted to have the lowest efficiency with a score of 0.9582. The results also show that Cobb-Douglas stochastic frontier model with truncated normal distributional assumption is preferable than Translog stochastic frontier model

Evaluating Kuala Lumpur stock exchange oriented bank performance with stochastic frontiers

Energy Technology Data Exchange (ETDEWEB)

Baten, M. A.; Maznah, M. K.; Razamin, R.; Jastini, M. J. [School of Quantitative Sciences, Universiti Utara Malaysia 06010, Sintok, Kedah (Malaysia)

2014-12-04

Banks play an essential role in the economic development and banks need to be efficient; otherwise, they may create blockage in the process of development in any country. The efficiency of banks in Malaysia is important and should receive greater attention. This study formulated an appropriate stochastic frontier model to investigate the efficiency of banks which are traded on Kuala Lumpur Stock Exchange (KLSE) market during the period 2005–2009. All data were analyzed to obtain the maximum likelihood method to estimate the parameters of stochastic production. Unlike the earlier studies which use balance sheet and income statements data, this study used market data as the input and output variables. It was observed that banks listed in KLSE exhibited a commendable overall efficiency level of 96.2% during 2005–2009 hence suggesting minimal input waste of 3.8%. Among the banks, the COMS (Cimb Group Holdings) bank is found to be highly efficient with a score of 0.9715 and BIMB (Bimb Holdings) bank is noted to have the lowest efficiency with a score of 0.9582. The results also show that Cobb-Douglas stochastic frontier model with truncated normal distributional assumption is preferable than Translog stochastic frontier model.

Binocular Rivalry in a Competitive Neural Network with Synaptic Depression

KAUST Repository

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.

Morphine treatment enhances glutamatergic input onto neurons of the nucleus accumbens via both disinhibitory and stimulating effect.

Science.gov (United States)

Yuan, Kejing; Sheng, Huan; Song, Jiaojiao; Yang, Li; Cui, Dongyang; Ma, Qianqian; Zhang, Wen; Lai, Bin; Chen, Ming; Zheng, Ping

2017-11-01

Drug addiction is a chronic brain disorder characterized by the compulsive repeated use of drugs. The reinforcing effect of repeated use of drugs on reward plays an important role in morphine-induced addictive behaviors. The nucleus accumbens (NAc) is an important site where morphine treatment produces its reinforcing effect on reward. However, how morphine treatment produces its reinforcing effect on reward in the NAc remains to be clarified. In the present study, we studied the influence of morphine treatment on the effects of DA and observed whether morphine treatment could directly change glutamatergic synaptic transmission in the NAc. We also explored the functional significance of morphine-induced potentiation of glutamatergic synaptic transmission in the NAc at behavioral level. Our results show that (1) morphine treatment removes the inhibitory effect of DA on glutamatergic input onto NAc neurons; (2) morphine treatment potentiates glutamatergic input onto NAc neurons, especially the one from the basolateral amygdala (BLA) to the NAc; (3) blockade of glutamatergic synaptic transmission in the NAc or ablation of projection neurons from BLA to NAc significantly decreases morphine treatment-induced increase in locomotor activity. These results suggest that morphine treatment enhances glutamatergic input onto neurons of the NAc via both disinhibitory and stimulating effect and therefore increases locomotor activity. © 2016 Society for the Study of Addiction.

On the adaptivity gap of stochastic orienteering

NARCIS (Netherlands)

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

NARCIS (Netherlands)

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

Enhanced excitatory input to melanin concentrating hormone neurons during developmental period of high food intake is mediated by GABA.

Science.gov (United States)

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.

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.

Retinal input to efferent target amacrine cells in the avian retina

Science.gov (United States)

Lindstrom, Sarah H.; Azizi, Nason; Weller, Cynthia; Wilson, Martin

2012-01-01

The bird visual system includes a substantial projection, of unknown function, from a midbrain nucleus to the contralateral retina. Every centrifugal, or efferent, neuron originating in the midbrain nucleus makes synaptic contact with the soma of a single, unique amacrine cell, the target cell (TC). By labeling efferent neurons in the midbrain we have been able to identify their terminals in retinal slices and make patch clamp recordings from TCs. TCs generate Na+ based action potentials triggered by spontaneous EPSPs originating from multiple classes of presynaptic neurons. Exogenously applied glutamate elicited inward currents having the mixed pharmacology of NMDA, kainate and inward rectifying AMPA receptors. Exogenously applied GABA elicited currents entirely suppressed by GABAzine, and therefore mediated by GABAA receptors. Immunohistochemistry showed the vesicular glutamate transporter, vGluT2, to be present in the characteristic synaptic boutons of efferent terminals, whereas the GABA synthetic enzyme, GAD, was present in much smaller processes of intrinsic retinal neurons. Extracellular recording showed that exogenously applied GABA was directly excitatory to TCs and, consistent with this, NKCC, the Cl− transporter often associated with excitatory GABAergic synapses, was identified in TCs by antibody staining. The presence of excitatory retinal input to TCs implies that TCs are not merely slaves to their midbrain input; instead, their output reflects local retinal activity and descending input from the midbrain. PMID:20650017

Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness

Science.gov (United States)

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

Hyperactivity of newborn Pten knock-out neurons results from increased excitatory synaptic drive.

Science.gov (United States)

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.

QUALITATIVE DATA AND ERROR MEASUREMENT IN INPUT-OUTPUT-ANALYSIS

NARCIS (Netherlands)

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

Synaptic consolidation across multiple timescales

Directory of Open Access Journals (Sweden)

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

Testing the robustness of deterministic models of optimal dynamic pricing and lot-sizing for deteriorating items under stochastic conditions

DEFF Research Database (Denmark)

Ghoreishi, Maryam

2018-01-01

Many models within the field of optimal dynamic pricing and lot-sizing models for deteriorating items assume everything is deterministic and develop a differential equation as the core of analysis. Two prominent examples are the papers by Rajan et al. (Manag Sci 38:240–262, 1992) and Abad (Manag......, we will try to expose the model by Abad (1996) and Rajan et al. (1992) to stochastic inputs; however, designing these stochastic inputs such that they as closely as possible are aligned with the assumptions of those papers. We do our investigation through a numerical test where we test the robustness...... of the numerical results reported in Rajan et al. (1992) and Abad (1996) in a simulation model. Our numerical results seem to confirm that the results stated in these papers are indeed robust when being imposed to stochastic inputs....

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 ...

Brain Injury-Induced Synaptic Reorganization in Hilar Inhibitory Neurons Is Differentially Suppressed by Rapamycin.

Science.gov (United States)

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.

proBDNF Negatively Regulates Neuronal Remodeling, Synaptic Transmission, and Synaptic Plasticity in Hippocampus

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 remodeling, synaptic growth and the storage of long-term memory in Aplysia.

Science.gov (United States)

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.

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.

Stochastic Averaging and Stochastic Extremum Seeking

CERN Document Server

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...

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.

Stochastic Modeling of Radioactive Material Releases

International Nuclear Information System (INIS)

Andrus, Jason; Pope, Chad

2015-01-01

Nonreactor nuclear facilities operated under the approval authority of the U.S. Department of Energy use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge or exceed dose evaluation guidelines. Unmitigated design basis events that sufficiently challenge dose evaluation guidelines or exceed the guidelines for members of the public or workers, merit selection of safety structures, systems, or components or other controls to prevent or mitigate the hazard. Idaho State University, in collaboration with Idaho National Laboratory, has developed a portable and simple to use software application called SODA (Stochastic Objective Decision-Aide) that stochastically calculates the radiation dose associated with hypothetical radiological material release scenarios. Rather than producing a point estimate of the dose, SODA produces a dose distribution result to allow a deeper understanding of the dose potential. SODA allows users to select the distribution type and parameter values for all of the input variables used to perform the dose calculation. SODA then randomly samples each distribution input variable and calculates the overall resulting dose distribution. In cases where an input variable distribution is unknown, a traditional single point value can be used. SODA was developed using the MATLAB coding framework. The software application has a graphical user input. SODA can be installed on both Windows and Mac computers and does not require MATLAB to function. SODA provides improved risk understanding leading to better informed decision making associated with establishing nuclear facility material-at-risk limits and safety structure, system, or component selection. It is important to note that SODA does not replace or compete with codes such as MACCS or RSAC, rather it is viewed as an easy to use supplemental tool to help improve risk understanding and support better informed decisions. The work was

Stochastic Modeling of Radioactive Material Releases

Energy Technology Data Exchange (ETDEWEB)

Andrus, Jason [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pope, Chad [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

Nonreactor nuclear facilities operated under the approval authority of the U.S. Department of Energy use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge or exceed dose evaluation guidelines. Unmitigated design basis events that sufficiently challenge dose evaluation guidelines or exceed the guidelines for members of the public or workers, merit selection of safety structures, systems, or components or other controls to prevent or mitigate the hazard. Idaho State University, in collaboration with Idaho National Laboratory, has developed a portable and simple to use software application called SODA (Stochastic Objective Decision-Aide) that stochastically calculates the radiation dose associated with hypothetical radiological material release scenarios. Rather than producing a point estimate of the dose, SODA produces a dose distribution result to allow a deeper understanding of the dose potential. SODA allows users to select the distribution type and parameter values for all of the input variables used to perform the dose calculation. SODA then randomly samples each distribution input variable and calculates the overall resulting dose distribution. In cases where an input variable distribution is unknown, a traditional single point value can be used. SODA was developed using the MATLAB coding framework. The software application has a graphical user input. SODA can be installed on both Windows and Mac computers and does not require MATLAB to function. SODA provides improved risk understanding leading to better informed decision making associated with establishing nuclear facility material-at-risk limits and safety structure, system, or component selection. It is important to note that SODA does not replace or compete with codes such as MACCS or RSAC, rather it is viewed as an easy to use supplemental tool to help improve risk understanding and support better informed decisions. The work was

Characterizing synaptic protein development in human visual cortex enables alignment of synaptic age with rat visual cortex

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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.

Modulation of synaptic plasticity by stress hormone associates with plastic alteration of synaptic NMDA receptor in the adult hippocampus.

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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.

Distributed Fault Detection for a Class of Nonlinear Stochastic Systems

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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.

Synaptic plasticity in drug reward circuitry.

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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.

A Ca2+-based computational model for NDMA receptor-dependent synaptic plasticity at individual post-synaptic spines in the hippocampus

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Owen Rackham

2010-07-01

Full Text Available Associative synaptic plasticity is synapse specific and requires coincident activity in presynaptic and postsynaptic neurons to activate NMDA receptors (NMDARs. The resultant Ca2+ influx is the critical trigger for the induction of synaptic plasticity. Given its centrality for the induction of synaptic plasticity, a model for NMDAR activation incorporating the timing of presynaptic glutamate release and postsynaptic depolarization by back-propagating action potentials could potentially predict the pre- and post-synaptic spike patterns required to induce synaptic plasticity. We have developed such a model by incorporating currently available data on the timecourse and amplitude of the postsynaptic membrane potential within individual spines. We couple this with data on the kinetics of synaptic NMDARs and then use the model to predict the continuous spine [Ca2+] in response to regular or irregular pre- and post-synaptic spike patterns. We then incorporate experimental data from synaptic plasticity induction protocols by regular activity patterns to couple the predicted local peak [Ca2+] to changes in synaptic strength. We find that our model accurately describes [Ca2+] in dendritic spines resulting from NMDAR activation during presynaptic and postsynaptic activity when compared to previous experimental observations. The model also replicates the experimentally determined plasticity outcome of regular and irregular spike patterns when applied to a single synapse. This model could therefore be used to predict the induction of synaptic plasticity under a variety of experimental conditions and spike patterns.

Notes on stochastic (bio)-logic gates: computing with allosteric cooperativity.

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Agliari, Elena; Altavilla, Matteo; Barra, Adriano; Dello Schiavo, Lorenzo; Katz, Evgeny

2015-05-15

Recent experimental breakthroughs have finally allowed to implement in-vitro reaction kinetics (the so called enzyme based logic) which code for two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the stochastic OR (and NOR). This accomplishment, together with the already-known single-input gates (performing as YES and NOT), provides a logic base and paves the way to the development of powerful biotechnological devices. However, as biochemical systems are always affected by the presence of noise (e.g. thermal), standard logic is not the correct theoretical reference framework, rather we show that statistical mechanics can work for this scope: here we formulate a complete statistical mechanical description of the Monod-Wyman-Changeaux allosteric model for both single and double ligand systems, with the purpose of exploring their practical capabilities to express noisy logical operators and/or perform stochastic logical operations. Mixing statistical mechanics with logics, and testing quantitatively the resulting findings on the available biochemical data, we successfully revise the concept of cooperativity (and anti-cooperativity) for allosteric systems, with particular emphasis on its computational capabilities, the related ranges and scaling of the involved parameters and its differences with classical cooperativity (and anti-cooperativity).

Synaptic Effects of Electric Fields

Science.gov (United States)

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

Theoretical and experimental study of stochastic effects on polarization rotation in a vectorial bistable laser

International Nuclear Information System (INIS)

Singh, Kamal P.; Ropars, Guy; Brunel, Marc; Le Floch, Albert

2006-01-01

We investigate the two-dimensional optical rotor of a weakly modulated vectorial bistable laser submitted to a single or multiple stochastic perturbations. In the Langevin-type equation of the rotor the role of an even or odd input forcing function on the system dynamics is isolated. Through these two inputs of optical and magnetic natures we verify that the stochastic resonance exists only when the periodic modulation acts on the even parity optical input. When two mutually correlated noises are simultaneously submitted to the input functions of opposite parities, we find a critical regime of the noise interplay whereby one stable state becomes noise-free. In this case, the residence time of the light vector in the noise-free state diverges which leads to a collapse of the output signal-to-noise ratio. But, in this critical regime also obtained when one noise drives both the even and odd functions, if the system symmetry is broken through an independent lever control, we can recover the switching cycle due to a new response mechanism, namely, the dual stochastic response, with a specific output signal-to-noise ratio expression. Both the theoretical analysis and the experiment show that the signal-to-noise ratio now displays a robust behavior for a large range of the input noise amplitude, and a plateau with respect to the input signal amplitude. Furthermore, we isolate an original signature of this synchronization mechanism in the residence-time distribution leading to a broadband forcing frequency range. These noise interplay effects in a double well potential are of generic nature and could be found in other nonlinear systems

Synaptic Plasticity and Excitation-Inhibition Balance in the Dentate Gyrus: Insights from In Vivo Recordings in Neuroligin-1, Neuroligin-2, and Collybistin Knockouts.

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Jedlicka, Peter; Muellerleile, Julia; Schwarzacher, Stephan W

2018-01-01

The hippocampal dentate gyrus plays a role in spatial learning and memory and is thought to encode differences between similar environments. The integrity of excitatory and inhibitory transmission and a fine balance between them is essential for efficient processing of information. Therefore, identification and functional characterization of crucial molecular players at excitatory and inhibitory inputs is critical for understanding the dentate gyrus function. In this minireview, we discuss recent studies unraveling molecular mechanisms of excitatory/inhibitory synaptic transmission, long-term synaptic plasticity, and dentate granule cell excitability in the hippocampus of live animals. We focus on the role of three major postsynaptic proteins localized at excitatory (neuroligin-1) and inhibitory synapses (neuroligin-2 and collybistin). In vivo recordings of field potentials have the advantage of characterizing the effects of the loss of these proteins on the input-output function of granule cells embedded in a network with intact connectivity. The lack of neuroligin-1 leads to deficient synaptic plasticity and reduced excitation but normal granule cell output, suggesting unaltered excitation-inhibition ratio. In contrast, the lack of neuroligin-2 and collybistin reduces inhibition resulting in a shift towards excitation of the dentate circuitry.

Synaptic Plasticity and Excitation-Inhibition Balance in the Dentate Gyrus: Insights from In Vivo Recordings in Neuroligin-1, Neuroligin-2, and Collybistin Knockouts

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Peter Jedlicka

2018-01-01

Full Text Available The hippocampal dentate gyrus plays a role in spatial learning and memory and is thought to encode differences between similar environments. The integrity of excitatory and inhibitory transmission and a fine balance between them is essential for efficient processing of information. Therefore, identification and functional characterization of crucial molecular players at excitatory and inhibitory inputs is critical for understanding the dentate gyrus function. In this minireview, we discuss recent studies unraveling molecular mechanisms of excitatory/inhibitory synaptic transmission, long-term synaptic plasticity, and dentate granule cell excitability in the hippocampus of live animals. We focus on the role of three major postsynaptic proteins localized at excitatory (neuroligin-1 and inhibitory synapses (neuroligin-2 and collybistin. In vivo recordings of field potentials have the advantage of characterizing the effects of the loss of these proteins on the input-output function of granule cells embedded in a network with intact connectivity. The lack of neuroligin-1 leads to deficient synaptic plasticity and reduced excitation but normal granule cell output, suggesting unaltered excitation-inhibition ratio. In contrast, the lack of neuroligin-2 and collybistin reduces inhibition resulting in a shift towards excitation of the dentate circuitry.

Underlying Mechanisms of Cooperativity, Input Specificity, and Associativity of Long-Term Potentiation Through a Positive Feedback of Local Protein Synthesis

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Lijie Hao

2018-05-01

Full Text Available Long-term potentiation (LTP is a specific form of activity-dependent synaptic plasticity that is a leading mechanism of learning and memory in mammals. The properties of cooperativity, input specificity, and associativity are essential for LTP; however, the underlying mechanisms are unclear. Here, based on experimentally observed phenomena, we introduce a computational model of synaptic plasticity in a pyramidal cell to explore the mechanisms responsible for the cooperativity, input specificity, and associativity of LTP. The model is based on molecular processes involved in synaptic plasticity and integrates gene expression involved in the regulation of neuronal activity. In the model, we introduce a local positive feedback loop of protein synthesis at each synapse, which is essential for bimodal response and synapse specificity. Bifurcation analysis of the local positive feedback loop of brain-derived neurotrophic factor (BDNF signaling illustrates the existence of bistability, which is the basis of LTP induction. The local bifurcation diagram provides guidance for the realization of LTP, and the projection of whole system trajectories onto the two-parameter bifurcation diagram confirms the predictions obtained from bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic components are required to achieve the three properties of LTP. This study provides insights into the mechanisms underlying the cooperativity, input specificity, and associativity of LTP, and the further construction of neural networks for learning and memory.

Synaptic Correlates of Low-Level Perception in V1.

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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

Endogenous fields enhanced stochastic resonance in a randomly coupled neuronal network

International Nuclear Information System (INIS)

Deng, Bin; Wang, Lin; Wang, Jiang; Wei, Xi-le; Yu, Hai-tao

2014-01-01

Highlights: • We study effects of endogenous fields on stochastic resonance in a neural network. • Stochastic resonance can be notably enhanced by endogenous field feedback. • Endogenous field feedback delay plays a vital role in stochastic resonance. • The parameters of low-passed filter play a subtle role in SR. - Abstract: Endogenous field, evoked by structured neuronal network activity in vivo, is correlated with many vital neuronal processes. In this paper, the effects of endogenous fields on stochastic resonance (SR) in a randomly connected neuronal network are investigated. The network consists of excitatory and inhibitory neurons and the axonal conduction delays between neurons are also considered. Numerical results elucidate that endogenous field feedback results in more rhythmic macroscope activation of the network for proper time delay and feedback coefficient. The response of the network to the weak periodic stimulation can be notably enhanced by endogenous field feedback. Moreover, the endogenous field feedback delay plays a vital role in SR. We reveal that appropriately tuned delays of the feedback can either induce the enhancement of SR, appearing at every integer multiple of the weak input signal’s oscillation period, or the depression of SR, appearing at every integer multiple of half the weak input signal’s oscillation period for the same feedback coefficient. Interestingly, the parameters of low-passed filter which is used in obtaining the endogenous field feedback signal play a subtle role in SR

Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar

DEFF Research Database (Denmark)

Löwe, Roland; Thorndahl, Søren; Mikkelsen, Peter Steen

2014-01-01

We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures are conside......We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures...

On the accuracy of mode-superposition analysis of linear systems under stochastic agencies

International Nuclear Information System (INIS)

Bellomo, M.; Di Paola, M.; La Mendola, L.; Muscolino, G.

1987-01-01

This paper deals with the response of linear structures using modal reduction. The MAM (mode acceleration method) correction is extended to stochastic analysis in the stationary case. In this framework the response of the given structure must be described in a probabilistic sense and the spectral moments of the nodal response must be computed in order to obtain a full description of the vibratory stochastic phenomenon. In the deterministic analysis the response is substantially made up of two terms, one of which accounts for the dynamic response due to the lower modes while the second accounts for the contribution due to the higher modes. In stochastic analysis the nodal spectral moments are made up of three terms; the first accounts for the spectral moments of the dynamic response due to the lower modes, the second accounts for the spectral moments of input and the third accounts for the cross-spectral moments between the input and the nodal output. The analysis is applied to a 35-storey building subjected to wind multivariate environments. (orig./HP)

Proteolytic Remodeling of Perineuronal Nets: Effects on Synaptic Plasticity and Neuronal Population Dynamics

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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.

DEA-Risk Efficiency and Stochastic Dominance Efficiency of Stock Indices

OpenAIRE

Martin Branda; Miloš Kopa

2012-01-01

In this article, the authors deal with the efficiency of world stock indices. Basically, they compare three approaches: mean-risk, data envelopment analysis (DEA), and stochastic dominance (SD) efficiency. In the DEA methodology, efficiency is defined as a weighted sum of outputs compared to a weighted sum of inputs when optimal weights are used. In DEA-risk efficiency, several risk measures and functionals which quantify the risk of the indices (var, VaR, CVaR, etc.) as DEA inputs are used. ...

Influence of Signal Stationarity on Digital Stochastic Measurement Implementation

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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.

Age-dependent modulation of synaptic plasticity and insulin mimetic effect of lipoic acid on a mouse model of Alzheimer's disease.

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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.

Secreted factors as synaptic organizers.

Science.gov (United States)

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.

MPTP-meditated hippocampal dopamine deprivation modulates synaptic transmission and activity-dependent synaptic plasticity

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

Spontaneous Vesicle Recycling in the Synaptic Bouton

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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.

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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.

Characterizing synaptic protein development in human visual cortex enables alignment of synaptic age with rat visual cortex

Science.gov (United States)

Pinto, Joshua G. A.; Jones, David G.; Williams, C. Kate; Murphy, Kathryn M.

2015-01-01

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 (V1) 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 four proteins and include a stage during early development (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. PMID:25729353

Bottom-up and Top-down Input Augment the Variability of Cortical Neurons

Science.gov (United States)

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

Spinal motoneuron synaptic plasticity after axotomy in the absence of inducible nitric oxide synthase

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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

Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition.

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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.

Inorganic proton conducting electrolyte coupled oxide-based dendritic transistors for synaptic electronics.

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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.

Self-organised criticality via retro-synaptic signals

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Hernandez-Urbina, Victor; Herrmann, J. Michael

2016-12-01

The brain is a complex system par excellence. In the last decade the observation of neuronal avalanches in neocortical circuits suggested the presence of self-organised criticality in brain networks. The occurrence of this type of dynamics implies several benefits to neural computation. However, the mechanisms that give rise to critical behaviour in these systems, and how they interact with other neuronal processes such as synaptic plasticity are not fully understood. In this paper, we present a long-term plasticity rule based on retro-synaptic signals that allows the system to reach a critical state in which clusters of activity are distributed as a power-law, among other observables. Our synaptic plasticity rule coexists with other synaptic mechanisms such as spike-timing-dependent plasticity, which implies that the resulting synaptic modulation captures not only the temporal correlations between spiking times of pre- and post-synaptic units, which has been suggested as requirement for learning and memory in neural systems, but also drives the system to a state of optimal neural information processing.

Localization of Presynaptic Plasticity Mechanisms Enables Functional Independence of Synaptic and Ectopic Transmission in the Cerebellum

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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.

Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

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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.

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 Collocation Applications in Computational Electromagnetics

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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.

Effects of dopamine and glutamate on synaptic plasticity: a computational modeling approach for drug abuse as comorbidity in mood disorders.

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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

Probabilistic DHP adaptive critic for nonlinear stochastic control systems.

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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.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

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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.

Science.gov (United States)

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.

Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

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...

Weak-periodic stochastic resonance in a parallel array of static nonlinearities.

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Yumei Ma

Full Text Available This paper studies the output-input signal-to-noise ratio (SNR gain of an uncoupled parallel array of static, yet arbitrary, nonlinear elements for transmitting a weak periodic signal in additive white noise. In the small-signal limit, an explicit expression for the SNR gain is derived. It serves to prove that the SNR gain is always a monotonically increasing function of the array size for any given nonlinearity and noisy environment. It also determines the SNR gain maximized by the locally optimal nonlinearity as the upper bound of the SNR gain achieved by an array of static nonlinear elements. With locally optimal nonlinearity, it is demonstrated that stochastic resonance cannot occur, i.e. adding internal noise into the array never improves the SNR gain. However, in an array of suboptimal but easily implemented threshold nonlinearities, we show the feasibility of situations where stochastic resonance occurs, and also the possibility of the SNR gain exceeding unity for a wide range of input noise distributions.

Lateral regulation of synaptic transmission by astrocytes.

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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.

Effects of 17beta-estradiol on glutamate synaptic transmission and neuronal excitability in the rat medial vestibular nuclei.

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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.

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

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

2014-01-01

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

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.

INCLUDING RISK IN ECONOMIC FEASIBILITY ANALYSIS:A STOCHASTIC SIMULATION MODEL FOR BLUEBERRY INVESTMENT DECISIONS IN CHILE

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GERMÁN LOBOS

2015-12-01

Full Text Available ABSTRACT The traditional method of net present value (NPV to analyze the economic profitability of an investment (based on a deterministic approach does not adequately represent the implicit risk associated with different but correlated input variables. Using a stochastic simulation approach for evaluating the profitability of blueberry (Vaccinium corymbosum L. production in Chile, the objective of this study is to illustrate the complexity of including risk in economic feasibility analysis when the project is subject to several but correlated risks. The results of the simulation analysis suggest that the non-inclusion of the intratemporal correlation between input variables underestimate the risk associated with investment decisions. The methodological contribution of this study illustrates the complexity of the interrelationships between uncertain variables and their impact on the convenience of carrying out this type of business in Chile. The steps for the analysis of economic viability were: First, adjusted probability distributions for stochastic input variables (SIV were simulated and validated. Second, the random values of SIV were used to calculate random values of variables such as production, revenues, costs, depreciation, taxes and net cash flows. Third, the complete stochastic model was simulated with 10,000 iterations using random values for SIV. This result gave information to estimate the probability distributions of the stochastic output variables (SOV such as the net present value, internal rate of return, value at risk, average cost of production, contribution margin and return on capital. Fourth, the complete stochastic model simulation results were used to analyze alternative scenarios and provide the results to decision makers in the form of probabilities, probability distributions, and for the SOV probabilistic forecasts. The main conclusion shown that this project is a profitable alternative investment in fruit trees in

Attractor neural networks with resource-efficient synaptic connectivity

Science.gov (United States)

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.

Involvement of neurotrophin-3 (NT-3) in the functional elimination of synaptic contacts during neuromuscular development.

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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.

Exogenous ciliary neurotrophic factor (CNTF) reduces synaptic depression during repetitive stimulation.

Science.gov (United States)

Garcia, Neus; Santafé, Manel M; Tomàs, Marta; Priego, Mercedes; Obis, Teresa; Lanuza, Maria A; Besalduch, Nuria; Tomàs, Josep

2012-09-01

It has been shown that ciliary neurotrophic factor (CNTF) has trophic and maintenance effects on several types of peripheral and central neurons, glia, and cells outside the nervous system. Both CNTF and its receptor, CNTF-Rα, are expressed in the muscle. We use confocal immunocytochemistry to show that the trophic cytokine and its receptor are present in the pre- and post-synaptic sites of the neuromuscular junctions (NMJs). Applied CNTF (7.5-200 ng/ml, 60 min-3 h) does not acutely affect spontaneous potentials (size or frequency) or quantal content of the evoked acetylcholine release from post-natal (in weak or strong axonal inputs on dually innervated end plates or in the most mature singly innervated synapses at P6) or adult (P30) NMJ of Levator auris longus muscle of the mice. However, CNTF reduces roughly 50% the depression produced by repetitive stimulation (40 Hz, 2 min) on the adult NMJs. Our findings indicate that, unlike neurotrophins, exogenous CNTF does not acutely modulate transmitter release locally at the mammalian neuromuscular synapse but can protect mature end plates from activity-induced synaptic depression. © 2012 Peripheral Nerve Society.

Non-Gaussian Stochastic Radiation Transfer in Finite Planar Media with Quadratic Scattering

International Nuclear Information System (INIS)

Sallah, M.

2016-01-01

The stochastic radiation transfer is considered in a participating planar finite continuously fluctuating medium characterized by non-Gaussian variability. The problem is considered for diffuse-reflecting boundaries with quadratic Rayleigh scattering. Random variable transformation (RVT) technique is used to get the complete average for the solution functions that are represented by the probability-density function (PDF) of the solution process. RVT algorithm applies a simple integral transformation to the input stochastic process (the extinction function of the medium). This linear transformation enables us to rewrite the stochastic transport equations in terms of the optical random variable (x) and the optical random thickness (L). Then the radiation transfer equation is solved deterministically to get a closed form for the solution as a function of x and L. So, the solution is used to obtain the PDF of the solution functions applying the RVT technique among the input random variable (L) and the output process (the solution functions). The obtained averages of the solution functions are used to get the complete analytical averages for some interesting physical quantities, namely, reflectivity, transmissivity and partial heat fluxes at the medium boundaries. Numerical results are represented graphically for different non-Gaussian probability distribution functions that compared with the corresponding Gaussian PDF.

A stochastic multiscale framework for modeling flow through random heterogeneous porous media

International Nuclear Information System (INIS)

Ganapathysubramanian, B.; Zabaras, N.

2009-01-01

Flow through porous media is ubiquitous, occurring from large geological scales down to the microscopic scales. Several critical engineering phenomena like contaminant spread, nuclear waste disposal and oil recovery rely on accurate analysis and prediction of these multiscale phenomena. Such analysis is complicated by inherent uncertainties as well as the limited information available to characterize the system. Any realistic modeling of these transport phenomena has to resolve two key issues: (i) the multi-length scale variations in permeability that these systems exhibit, and (ii) the inherently limited information available to quantify these property variations that necessitates posing these phenomena as stochastic processes. A stochastic variational multiscale formulation is developed to incorporate uncertain multiscale features. A stochastic analogue to a mixed multiscale finite element framework is used to formulate the physical stochastic multiscale process. Recent developments in linear and non-linear model reduction techniques are used to convert the limited information available about the permeability variation into a viable stochastic input model. An adaptive sparse grid collocation strategy is used to efficiently solve the resulting stochastic partial differential equations (SPDEs). The framework is applied to analyze flow through random heterogeneous media when only limited statistics about the permeability variation are given

Forebrain deletion of αGDI in adult mice worsens the pre-synaptic deficit at cortico-lateral amygdala synaptic connections.

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Veronica Bianchi

Full Text Available The GDI1 gene encodes αGDI, which retrieves inactive GDP-bound RAB from membranes to form a cytosolic pool awaiting vesicular release. Mutations in GDI1 are responsible for X-linked Intellectual Disability. Characterization of the Gdi1-null mice has revealed alterations in the total number and distribution of hippocampal and cortical synaptic vesicles, hippocampal short-term synaptic plasticity and specific short-term memory deficits in adult mice, which are possibly caused by alterations of different synaptic vesicle recycling pathways controlled by several RAB GTPases. However, interpretation of these studies is complicated by the complete ablation of Gdi1 in all cells in the brain throughout development. In this study, we generated conditionally gene-targeted mice in which the knockout of Gdi1 is restricted to the forebrain, hippocampus, cortex and amygdala and occurs only during postnatal development. Adult mutant mice reproduce the short-term memory deficit previously reported in Gdi1-null mice. Surprisingly, the delayed ablation of Gdi1 worsens the pre-synaptic phenotype at cortico-amygdala synaptic connections compared to Gdi1-null mice. These results suggest a pivotal role of αGDI via specific RAB GTPases acting specifically in forebrain regions at the pre-synaptic sites involved in memory formation.

Synapse geometry and receptor dynamics modulate synaptic strength.

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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.

Stochastic Analysis 2010

CERN Document Server

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

E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.

Science.gov (United States)

Trapp, Philip; Echeveste, Rodrigo; Gros, Claudius

2018-06-12

Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.

Synaptic vesicle distribution by conveyor belt.

Science.gov (United States)

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 © 2012 Elsevier Inc. All rights reserved.

On stabilization of linear systems with stochastic disturbances and input saturation

NARCIS (Netherlands)

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

Subcellular Location of PKA Controls Striatal Plasticity: Stochastic Simulations in Spiny Dendrites

Science.gov (United States)

Oliveira, Rodrigo F.; Kim, MyungSook; Blackwell, Kim T.

2012-01-01

Dopamine release in the striatum has been implicated in various forms of reward dependent learning. Dopamine leads to production of cAMP and activation of protein kinase A (PKA), which are involved in striatal synaptic plasticity and learning. PKA and its protein targets are not diffusely located throughout the neuron, but are confined to various subcellular compartments by anchoring molecules such as A-Kinase Anchoring Proteins (AKAPs). Experiments have shown that blocking the interaction of PKA with AKAPs disrupts its subcellular location and prevents LTP in the hippocampus and striatum; however, these experiments have not revealed whether the critical function of anchoring is to locate PKA near the cAMP that activates it or near its targets, such as AMPA receptors located in the post-synaptic density. We have developed a large scale stochastic reaction-diffusion model of signaling pathways in a medium spiny projection neuron dendrite with spines, based on published biochemical measurements, to investigate this question and to evaluate whether dopamine signaling exhibits spatial specificity post-synaptically. The model was stimulated with dopamine pulses mimicking those recorded in response to reward. Simulations show that PKA colocalization with adenylate cyclase, either in the spine head or in the dendrite, leads to greater phosphorylation of DARPP-32 Thr34 and AMPA receptor GluA1 Ser845 than when PKA is anchored away from adenylate cyclase. Simulations further demonstrate that though cAMP exhibits a strong spatial gradient, diffusible DARPP-32 facilitates the spread of PKA activity, suggesting that additional inactivation mechanisms are required to produce spatial specificity of PKA activity. PMID:22346744

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)

A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

Science.gov (United States)

Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

2017-10-01

Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal

Stochastic cooling equipment at the ISR

CERN Multimedia

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.

Generation Expansion Planning with Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

DEFF Research Database (Denmark)

Zhan, Yiduo; Zheng, Qipeng; Wang, Jianhui

2016-01-01

, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming......Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined...

Synaptic organization and division of labor in the exceptionally polymorphic ant Pheidole rhea.

Science.gov (United States)

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.

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.

Synchronization of a Class of Memristive Stochastic Bidirectional Associative Memory Neural Networks with Mixed Time-Varying Delays via Sampled-Data Control

Directory of Open Access Journals (Sweden)

Manman Yuan

2018-01-01

Full Text Available The paper addresses the issue of synchronization of memristive bidirectional associative memory neural networks (MBAMNNs with mixed time-varying delays and stochastic perturbation via a sampled-data controller. First, we propose a new model of MBAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying distributed delays and discrete delays. Second, we design a new method of sampled-data control for the stochastic MBAMNNs. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the methods are carefully designed to confirm the synchronization processes are suitable for the feather of the memristor. Third, sufficient criteria guaranteeing the synchronization of the systems are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

Loss of GABAergic inputs in APP/PS1 mouse model of Alzheimer's disease

Directory of Open Access Journals (Sweden)

Tutu Oyelami

2014-04-01

Full Text Available Alzheimer's disease (AD is characterized by symptoms which include seizures, sleep disruption, loss of memory as well as anxiety in patients. Of particular importance is the possibility of preventing the progressive loss of neuronal projections in the disease. Transgenic mice overexpressing EOFAD mutant PS1 (L166P and mutant APP (APP KM670/671NL Swedish (APP/PS1 develop a very early and robust Amyloid pathology and display synaptic plasticity impairments and cognitive dysfunction. Here we investigated GABAergic neurotransmission, using multi-electrode array (MEA technology and pharmacological manipulation to quantify the effect of GABA Blockers on field excitatory postsynaptic potentials (fEPSP, and immunostaining of GABAergic neurons. Using MEA technology we confirm impaired LTP induction by high frequency stimulation in APPPS1 hippocampal CA1 region that was associated with reduced alteration of the pair pulse ratio after LTP induction. Synaptic dysfunction was also observed under manipulation of external Calcium concentration and input-output curve. Electrophysiological recordings from brain slice of CA1 hippocampus area, in the presence of GABAergic receptors blockers cocktails further demonstrated significant reduction in the GABAergic inputs in APP/PS1 mice. Moreover, immunostaining of GAD65 a specific marker for GABAergic neurons revealed reduction of the GABAergic inputs in CA1 area of the hippocampus. These results might be linked to increased seizure sensitivity, premature death and cognitive dysfunction in this animal model of AD. Further in depth analysis of GABAergic dysfunction in APP/PS1 mice is required and may open new perspectives for AD therapy by restoring GABAergic function.

Rich spectrum of neural field dynamics in the presence of short-term synaptic depression

Science.gov (United States)

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.

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.

Phase transitions and self-organized criticality in networks of stochastic spiking neurons.

Science.gov (United States)

Brochini, Ludmila; de Andrade Costa, Ariadne; Abadi, Miguel; Roque, Antônio C; Stolfi, Jorge; Kinouchi, Osame

2016-11-07

Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains - a form of short-term plasticity probably located at the axon initial segment (AIS) - instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.

Glutamatergic synaptic plasticity in the mesocorticolimbic system in addiction

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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.

Glutamatergic synaptic plasticity in the mesocorticolimbic system in addiction

Science.gov (United States)

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

Compliance-Free, Digital SET and Analog RESET Synaptic Characteristics of Sub-Tantalum Oxide Based Neuromorphic Device.

Science.gov (United States)

Abbas, Yawar; Jeon, Yu-Rim; Sokolov, Andrey Sergeevich; Kim, Sohyeon; Ku, Boncheol; Choi, Changhwan

2018-01-19

A two terminal semiconducting device like a memristor is indispensable to emulate the function of synapse in the working memory. The analog switching characteristics of memristor play a vital role in the emulation of biological synapses. The application of consecutive voltage sweeps or pulses (action potentials) changes the conductivity of the memristor which is considered as the fundamental cause of the synaptic plasticity. In this study, a neuromorphic device using an in-situ growth of sub-tantalum oxide switching layer is fabricated, which exhibits the digital SET and analog RESET switching with an electroforming process without any compliance current (compliance free). The process of electroforming and SET is observed at the positive sweeps of +2.4 V and +0.86 V, respectively, while multilevel RESET is observed with the consecutive negative sweeps in the range of 0 V to -1.2 V. The movement of oxygen vacancies and gradual change in the anatomy of the filament is attributed to digital SET and analog RESET switching characteristics. For the Ti/Ta 2 O 3-x /Pt neuromorphic device, the Ti top and Pt bottom electrodes are considered as counterparts of the pre-synaptic input terminal and a post-synaptic output terminal, respectively.

Agrin and synaptic laminin are required to maintain adult neuromuscular junctions.

Directory of Open Access Journals (Sweden)

Melanie A Samuel

Full Text Available As synapses form and mature the synaptic partners produce organizing molecules that regulate each other's differentiation and ensure precise apposition of pre- and post-synaptic specializations. At the skeletal neuromuscular junction (NMJ, these molecules include agrin, a nerve-derived organizer of postsynaptic differentiation, and synaptic laminins, muscle-derived organizers of presynaptic differentiation. Both become concentrated in the synaptic cleft as the NMJ develops and are retained in adulthood. Here, we used mutant mice to ask whether these organizers are also required for synaptic maintenance. Deletion of agrin from a subset of adult motor neurons resulted in the loss of acetylcholine receptors and other components of the postsynaptic apparatus and synaptic cleft. Nerve terminals also atrophied and eventually withdrew from muscle fibers. On the other hand, mice lacking the presynaptic organizer laminin-α4 retained most of the synaptic cleft components but exhibited synaptic alterations reminiscent of those observed in aged animals. Although we detected no marked decrease in laminin or agrin levels at aged NMJs, we observed alterations in the distribution and organization of these synaptic cleft components suggesting that such changes could contribute to age-related synaptic disassembly. Together, these results demonstrate that pre- and post-synaptic organizers actively function to maintain the structure and function of adult NMJs.

Molecular mechanisms of synaptic remodeling in alcoholism.

Science.gov (United States)

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.

Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

Energy Technology Data Exchange (ETDEWEB)

Wang, Hong [Pacific Northwest National Laboratory (PNNL); Aziz, H M Abdul [ORNL; Young, Stan [National Renewable Energy Laboratory (NREL); Patil, Sagar [Pacific Northwest National Laboratory (PNNL)

2017-10-01

Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.

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)

Stochastic Neural Field Theory and the System-Size Expansion

KAUST Repository

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.

Synaptic Contacts Enhance Cell-to-Cell Tau Pathology Propagation

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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.

Synaptic Contacts Enhance Cell-to-Cell Tau Pathology Propagation.

Science.gov (United States)

Calafate, Sara; Buist, Arjan; Miskiewicz, Katarzyna; Vijayan, Vinoy; Daneels, Guy; de Strooper, Bart; de Wit, Joris; Verstreken, Patrik; Moechars, Diederik

2015-05-26

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. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Stochastic higher order finite element elasto-plastic analysis of the necking phenomenon

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Strąkowski Michał

2017-01-01

Full Text Available The principal goal of this work is to investigate an application of the stochastic perturbation technique of the 10th order in coupled thermo-elasto-plastic analysis of tension of the steel elastic bar exposed to fire with thermally dependent material characteristics. An ambient temperature, calculated from the fire curve after ISO 834-1, equivalent to the fire exposure of the steel structure is treated here as the input Gaussian random variable. It is uniquely defined by the constant mean value at outer surfaces of this element, where material parameters of the steel as Young modulus, yield strength, heat conductivity, capacity and thermal elongation are considered all as highly temperature-dependent. Computational implementation known as the Stochastic Finite Element Method is carried out with the use of the FEM system ABAQUS and computer algebra system MAPLE. It uses both polynomial and non-polynomial local response functions of stresses and displacements. The basic probabilistic characteristics of time-dependent structural response are determined (expectations, coefficients of variation, skewness and kurtosis and verified with classical Monte-Carlo simulation scheme and semi-analytical technique for input coefficient of variation not larger than 0.20. Finally, probabilistic convergence of all three methods versus increasing input uncertainty level is investigated.

Lateralized odor preference training in rat pups reveals an enhanced network response in anterior piriform cortex to olfactory input that parallels extended memory.

Science.gov (United States)

Fontaine, Christine J; Harley, Carolyn W; Yuan, Qi

2013-09-18

The present study examines synaptic plasticity in the anterior piriform cortex (aPC) using ex vivo slices from rat pups given lateralized odor preference training. In the early odor preference learning model, a brief 10 min training session yields 24 h memory, while four daily sessions yield 48 h memory. Odor preference memory can be lateralized through naris occlusion as the anterior commissure is not yet functional. AMPA receptor-mediated postsynaptic responses in the aPC to lateral olfactory tract input, shown to be enhanced at 24 h, are no longer enhanced 48 h after a single training session. Following four spaced lateralized trials, the AMPA receptor-mediated fEPSP is enhanced in the trained aPC at 48 h. Calcium imaging of aPC pyramidal cells within 48 h revealed decreased firing thresholds in the pyramidal cell network. Thus multiday odor preference training induced increased odor input responsiveness in previously weakly activated aPC cells. These results support the hypothesis that increased synaptic strength in olfactory input networks mediates odor preference memory. The increase in aPC network activation parallels behavioral memory.

Stochastic Optimization Model to STudy the Operational Impacts of High Wind Penetrations in Ireland

DEFF Research Database (Denmark)

Meibom, Peter; Barth, R.; Hasche, B.

2011-01-01

A stochastic mixed integer linear optimization scheduling model minimizing system operation costs and treating load and wind power production as stochastic inputs is presented. The schedules are updated in a rolling manner as more up-to-date information becomes available. This is a fundamental...... change relative to day-ahead unit commitment approaches. The need for reserves dependent on forecast horizon and share of wind power has been estimated with a statistical model combining load and wind power forecast errors with scenarios of forced outages. The model is used to study operational impacts...

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.

Defective Glycinergic Synaptic Transmission in Zebrafish Motility Mutants

OpenAIRE

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 ...

Stochastic long term modelling of a drainage system with estimation of return period uncertainty

DEFF Research Database (Denmark)

Thorndahl, Søren

2009-01-01

Long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems are associated with large uncertainties. Especially on rainfall inputs, parameters, and assessment of return periods. This paper proposes a Monte Carlo based methodology for stochastic prediction of...

Calcium current homeostasis and synaptic deficits in hippocampal neurons from Kelch-like 1 knockout mice

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.

Neuro-inspired computing using resistive synaptic devices

CERN Document Server

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...

Synaptic Correlates of Working Memory Capacity.

Science.gov (United States)

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.

Manipulating potential wells in Logical Stochastic Resonance to obtain XOR logic

International Nuclear Information System (INIS)

Storni, Remo; Ando, Hiroyasu; Aihara, Kazuyuki; Murali, K.; Sinha, Sudeshna

2012-01-01

Logical Stochastic Resonance (LSR) is the application of Stochastic Resonance to logic computation, namely the phenomenon where a nonlinear system driven by weak signals representing logic inputs, under optimal noise, can yield logic outputs. We extend the existing results, obtained in the context of bistable systems, to multi-stable dynamical systems, allowing us to obtain XOR logic, in addition to the AND (NAND) and OR (NOR) logic observed in earlier studies. This strategy widens the scope of LSR from the application point of view, as XOR forms the basis of ubiquitous bit-by-bit addition, and conceptually, showing the ability to yield non-monotonic input–output logic associations. -- Highlights: ► We generalize Logical Stochastic Resonance from bistable to multi-stable systems. ► We propose a tristable dynamical system formed of piecewise linear functions. ► The system can correctly reproduce XOR logic behavior using the LSR principle. ► The system yields different logic behavior without the need to change the dynamics.

Optogenetic activation of serotonergic terminals facilitates GABAergic inhibitory input to orexin/hypocretin neurons

OpenAIRE

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...

Altered balance of glutamatergic/GABAergic synaptic input and associated changes in dendrite morphology after BDNF expression in BDNF-deficient hippocampal neurons

OpenAIRE

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...

Depression as a Glial-Based Synaptic Dysfunction

Directory of Open Access Journals (Sweden)

Daniel eRial

2016-01-01

Full Text Available Recent studies combining pharmacological, behavioral, electrophysiological and molecular approaches indicate that depression results from maladaptive neuroplastic processing occurring in defined frontolimbic circuits responsible for emotional processing such as the prefrontal cortex, hippocampus, amygdala and ventral striatum. However, the exact mechanisms controlling synaptic plasticity that are disrupted to trigger depressive conditions have not been elucidated. Since glial cells (astrocytes and microglia tightly and dynamically interact with synapses, engaging a bi-directional communication critical for the processing of synaptic information, we now revisit the role of glial cells in the etiology of depression focusing on a dysfunction of the ‘quad-partite’ synapse. This interest is supported by the observations that depressive-like conditions are associated with a decreased density and hypofunction of astrocytes and with an increase microglia ‘activation’ in frontolimbic regions, which is expected to contribute for the synaptic dysfunction present in depression. Furthermore, the traditional culprits of depression (glucocorticoids, biogenic amines, BDNF affect glia functioning, whereas antidepressant treatments (SSRIs, electroshock, deep brain stimulation recover glia functioning. In this context of a quad-partite synapse, systems modulating glia-synapse bidirectional communication - such as the purinergic neuromodulation system operated by ATP and adenosine - emerge as promising candidates to re-normalize synaptic function by combining direct synaptic effects with an ability to also control astrocyte and microglia function. This proposed triple action of purines to control aberrant synaptic function illustrates the rationale to consider the interference with glia dysfunction as a mechanism of action driving the design of future pharmacological tools to manage depression.

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.

Population density equations for stochastic processes with memory kernels

Science.gov (United States)

Lai, Yi Ming; de Kamps, Marc

2017-06-01

We present a method for solving population density equations (PDEs)-a mean-field technique describing homogeneous populations of uncoupled neurons—where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly. We can independently vary the choice of the deterministic model and the model for the stochastic process, leading to a highly modular numerical solution strategy. We demonstrate this by replacing the master equation implicit in many formulations of the PDE formalism by a generalization called the generalized Montroll-Weiss equation—a recent result from random network theory—describing a random walker subject to transitions realized by a non-Markovian process. We demonstrate the method for leaky- and quadratic-integrate and fire neurons subject to spike trains with Poisson and gamma-distributed interspike intervals. We are able to model jump responses for both models accurately to both excitatory and inhibitory input under the assumption that all inputs are generated by one renewal process.

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

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.

Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Science.gov (United States)

Haberlandt, U.; Radtke, I.

2014-01-01

Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the

Synchronization of map-based neurons with memory and synaptic delay

Energy Technology Data Exchange (ETDEWEB)

Sausedo-Solorio, J.M. [Universidad Autónoma del Estado de Hidalgo, Carretera Pachuca-Tulancingo Km. 4.5, 42074 Pachuca, Hidalgo (Mexico); Pisarchik, A.N., E-mail: apisarch@cio.mx [Centro de Investigaciones en Optica, Loma del Bosque 115, Lomas del Campestre, 37150 Leon, Guanajuato (Mexico); Centre for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcon, Madrid (Spain)

2014-06-13

Synchronization of two synaptically coupled neurons with memory and synaptic delay is studied using the Rulkov map, one of the simplest neuron models which displays specific features inherent to bursting dynamics. We demonstrate a transition from lag to anticipated synchronization as the relationship between the memory duration and the synaptic delay time changes. The neuron maps synchronize either with anticipation, if the memory is longer than the synaptic delay time, or with lag otherwise. The mean anticipation time is equal to the difference between the memory and synaptic delay independently of the coupling strength. Frequency entrainment and phase-locking phenomena as well as a transition from regular spikes to chaos are demonstrated with respect to the coupling strength. - Highlights: • We study synchronization of neurons with memory and synaptic delay in the map model. • Neurons synchronize either with anticipation or with lag depending on delay time. • Mean anticipation time is equal to the difference between memory and synaptic delay. • Frequency entrainment and phase locking are studied with respect to the coupling.

Modeling and simulation of high dimensional stochastic multiscale PDE systems at the exascale

Energy Technology Data Exchange (ETDEWEB)

Zabaras, Nicolas J. [Cornell Univ., Ithaca, NY (United States)

2016-11-08

Predictive Modeling of multiscale and Multiphysics systems requires accurate data driven characterization of the input uncertainties, and understanding of how they propagate across scales and alter the final solution. This project develops a rigorous mathematical framework and scalable uncertainty quantification algorithms to efficiently construct realistic low dimensional input models, and surrogate low complexity systems for the analysis, design, and control of physical systems represented by multiscale stochastic PDEs. The work can be applied to many areas including physical and biological processes, from climate modeling to systems biology.

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...

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.

New insights into the stochastic ray production frontier

DEFF Research Database (Denmark)

Henningsen, Arne; Bělín, Matěj; Henningsen, Géraldine

The stochastic ray production frontier was developed as an alternative to the traditional output distance function to model production processes with multiple inputs and multiple outputs. Its main advantage over the traditional approach is that it can be used when some output quantities of some o...... important than the existing criticisms: taking logarithms of the polar coordinate angles, non-invariance to units of measurement, and ordering of the outputs. We also give some practical advice on how to address the newly raised issues....

New insights into the stochastic ray production frontier

DEFF Research Database (Denmark)

Henningsen, Arne; Bělín, Matěj; Henningsen, Geraldine

2017-01-01

The stochastic ray production frontier was developed as an alternative to the traditional output distance function to model production processes with multiple inputs and multiple outputs. Its main advantage over the traditional approach is that it can be used when some output quantities of some o...... important than the existing criticisms: taking logarithms of the polar coordinate angles, non-invariance to units of measurement, and ordering of the outputs. We also give some practical advice on how to address the newly raised issues....

A Stochastic and Holistic Method to Support Decision-Making in Early Building Design

DEFF Research Database (Denmark)

Østergaard, Torben; Maagaard, Steffen; Jensen, Rasmus Lund

2015-01-01

preferable input domains for the most influential parameters. To enable computationally fast simulations, we combined calculations of energy demand and thermal comfort based on ISO 13790 (CEN 2008) with a regression model for daylight factor. We constructed scoring functions for the three outputs and applied...... to collect the 10 % best performing simulations. From this collection, histograms were used to identify favourable and adverse input spans for a selection of the most sensitive parameters. Subsequently, two runs of each 3000 simulations were performed – one using the favourable input spans and the other...... using the adverse spans. The results showed that the distribution related to favourable input spans was shifted significantly towards higher holistic scores. The authors conclude that the use of a stochastic, holistic method can guide decision-making by identifying favourable input regions, and thereby...

BDNF-induced local protein synthesis and synaptic plasticity.

Science.gov (United States)

Leal, Graciano; Comprido, Diogo; Duarte, Carlos B

2014-01-01

Brain-derived neurotrophic factor (BDNF) is an important regulator of synaptic transmission and long-term potentiation (LTP) in the hippocampus and in other brain regions, playing a role in the formation of certain forms of memory. The effects of BDNF in LTP are mediated by TrkB (tropomyosin-related kinase B) receptors, which are known to be coupled to the activation of the Ras/ERK, phosphatidylinositol 3-kinase/Akt and phospholipase C-γ (PLC-γ) pathways. The role of BDNF in LTP is best studied in the hippocampus, where the neurotrophin acts at pre- and post-synaptic levels. Recent studies have shown that BDNF regulates the transport of mRNAs along dendrites and their translation at the synapse, by modulating the initiation and elongation phases of protein synthesis, and by acting on specific miRNAs. Furthermore, the effect of BDNF on transcription regulation may further contribute to long-term changes in the synaptic proteome. In this review we discuss the recent progress in understanding the mechanisms contributing to the short- and long-term regulation of the synaptic proteome by BDNF, and the role in synaptic plasticity, which is likely to influence learning and memory formation. This article is part of the Special Issue entitled 'BDNF Regulation of Synaptic Structure, Function, and Plasticity'. Copyright © 2013 Elsevier Ltd. All rights reserved.

PET measures of pre- and post-synaptic cardiac beta adrenergic function

Energy Technology Data Exchange (ETDEWEB)

Link, Jeanne M.; Stratton, John R.; Levy, Wayne; Poole, Jeanne E.; Shoner, Steven C.; Stuetzle, Werner; Caldwell, James H. E-mail: jcald@u.washington.edu

2003-11-01

Positron Emission Tomography was used to measure global and regional cardiac {beta}-adrenergic function in 19 normal subjects and 9 congestive heart failure patients. [{sup 11}C]-meta-hydroxyephedrine was used to image norepinephrine transporter function as an indicator of pre-synaptic function and [{sup 11}C]-CGP12177 was used to measure cell surface {beta}-receptor density as an indicator of post-synaptic function. Pre-synaptic, but not post-synaptic, function was significantly different between normals and CHF patients. Pre-synaptic function was well matched to post-synaptic function in the normal hearts but significantly different and poorly matched in the CHF patients studied. This imaging technique can help us understand regional sympathetic function in cardiac disease.

Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population

Energy Technology Data Exchange (ETDEWEB)

Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao, E-mail: htyu@tju.edu.cn [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Lu, Meili [School of Informational Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China); Che, Yanqiu [School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China)

2015-01-15

This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

A simple parameter can switch between different weak-noise-induced phenomena in a simple neuron model

Science.gov (United States)

Yamakou, Marius E.; Jost, Jürgen

2017-10-01

In recent years, several, apparently quite different, weak-noise-induced resonance phenomena have been discovered. Here, we show that at least two of them, self-induced stochastic resonance (SISR) and inverse stochastic resonance (ISR), can be related by a simple parameter switch in one of the simplest models, the FitzHugh-Nagumo (FHN) neuron model. We consider a FHN model with a unique fixed point perturbed by synaptic noise. Depending on the stability of this fixed point and whether it is located to either the left or right of the fold point of the critical manifold, two distinct weak-noise-induced phenomena, either SISR or ISR, may emerge. SISR is more robust to parametric perturbations than ISR, and the coherent spike train generated by SISR is more robust than that generated deterministically. ISR also depends on the location of initial conditions and on the time-scale separation parameter of the model equation. Our results could also explain why real biological neurons having similar physiological features and synaptic inputs may encode very different information.

Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population.

Science.gov (United States)

Wei, Xile; Zhang, Danhong; Lu, Meili; Wang, Jiang; Yu, Haitao; Che, Yanqiu

2015-01-01

This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population

International Nuclear Information System (INIS)

Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao; Lu, Meili; Che, Yanqiu

2015-01-01

This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance

ATM protein is located on presynaptic vesicles and its deficit leads to failures in synaptic plasticity.

Science.gov (United States)

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.

A presynaptic role for PKA in synaptic tagging and memory.

Science.gov (United States)

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.

Persistence and extinction of a single-species population system in a polluted environment with random perturbations and impulsive toxicant input

International Nuclear Information System (INIS)

Liu Meng; Wang Ke

2012-01-01

Highlights: ► Random population model with pulse toxicant input in polluted environments. ► Threshold between persistence and extinction is obtained. ► Different random noises have different effects on the persistence of the population. ► Impulsive period plays a key role in determining persistence of the population. ► Simulation figures support the analytical findings. - Abstract: Taking both white noises and colored noises into account, a stochastic single-species model with Markov switching and impulsive toxicant input in a polluted environment is proposed and investigated. Sufficient conditions for extinction, non-persistence in the mean, weak persistence and stochastic permanence are established. The threshold between weak persistence and extinction is obtained. Some simulation figures are introduced to illustrate the main results.

Evaluating the role of reentrant output-to-input feedback in simultaneous pattern processing

Energy Technology Data Exchange (ETDEWEB)

Achler, Tsvi [Los Alamos National Laboratory

2010-01-01

Simultaneous Pattern Processing (SPP) is defined as the ability to identify simultaneous-intermixed patterns without isolating them individually (e.g. without separating each pattern in space and processing it individually). Enhanced SPP ability is beneficial for many real-life applications such as scene understanding, separating simultaneous voices, and identifying odorant or taste mixes. The first part of this work identifies how SPP scenarios are problematic to models which train synaptic connections or implement lateral inhibition and quantifies how subtle difficulties lead to complex combinatorial issues. The second part of this work proposes and tests an algorithm motivated by Ubiquitous re-entrant 'output to input' connections found throughout sensory processing regions of the brain. Through these connections the model proposes a dynamic gain mechanism that can provide functionality normally achieved through variable synaptic connections. The re-entrant structure combined with enhanced perfonnance suggests the brain may utilize this configuration for SPP flexibility.

Incorporation of Stochastic Policyholder Behavior in Analytical Pricing of GMABs and GMDBs

Directory of Open Access Journals (Sweden)

Marcos Escobar

2016-11-01

Full Text Available Variable annuities represent certain unit-linked life insurance products offering different types of protection commonly referred to as guaranteed minimum benefits (GMXBs. They are designed for the increasing demand of the customers for private pension provision. In this paper we analytically price variable annuities with guaranteed minimum repayments at maturity and in case of the insured’s death. If the contract is prematurely surrendered, the policyholder is entitled to the current value of the fund account reduced by the prevailing surrender fee. The financial market and the mortality model are affine linear. For the surrender model, a Cox process is deployed whose intensity is given by a deterministic function (s-curve with stochastic inputs from the financial market. So, the policyholders’ surrender behavior depends on the performance of the financial market and is stochastic. The presented pricing scheme incorporates the stochastic surrender behavior of the policyholders and is only based on suitable closed-form approximations.

GABA regulates synaptic integration of newly generated neurons in the adult brain

Science.gov (United States)

Ge, Shaoyu; Goh, Eyleen L. K.; Sailor, Kurt A.; Kitabatake, Yasuji; Ming, Guo-Li; Song, Hongjun

2006-02-01

Adult neurogenesis, the birth and integration of new neurons from adult neural stem cells, is a striking form of structural plasticity and highlights the regenerative capacity of the adult mammalian brain. Accumulating evidence suggests that neuronal activity regulates adult neurogenesis and that new neurons contribute to specific brain functions. The mechanism that regulates the integration of newly generated neurons into the pre-existing functional circuitry in the adult brain is unknown. Here we show that newborn granule cells in the dentate gyrus of the adult hippocampus are tonically activated by ambient GABA (γ-aminobutyric acid) before being sequentially innervated by GABA- and glutamate-mediated synaptic inputs. GABA, the major inhibitory neurotransmitter in the adult brain, initially exerts an excitatory action on newborn neurons owing to their high cytoplasmic chloride ion content. Conversion of GABA-induced depolarization (excitation) into hyperpolarization (inhibition) in newborn neurons leads to marked defects in their synapse formation and dendritic development in vivo. Our study identifies an essential role for GABA in the synaptic integration of newly generated neurons in the adult brain, and suggests an unexpected mechanism for activity-dependent regulation of adult neurogenesis, in which newborn neurons may sense neuronal network activity through tonic and phasic GABA activation.

Noncausal stochastic calculus

CERN Document Server

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 ...

Synaptic plasticity and sensory-motor improvement following fibrin sealant dorsal root reimplantation and mononuclear cell therapy

Science.gov (United States)

Benitez, Suzana U.; Barbizan, Roberta; Spejo, Aline B.; Ferreira, Rui S.; Barraviera, Benedito; Góes, Alfredo M.; de Oliveira, Alexandre L. R.

2014-01-01

Root lesions may affect both dorsal and ventral roots. However, due to the possibility of generating further inflammation and neuropathic pain, surgical procedures do not prioritize the repair of the afferent component. The loss of such sensorial input directly disturbs the spinal circuits thus affecting the functionality of the injuried limb. The present study evaluated the motor and sensory improvement following dorsal root reimplantation with fibrin sealant (FS) plus bone marrow mononuclear cells (MC) after dorsal rhizotomy. MC were used to enhance the repair process. We also analyzed changes in the glial response and synaptic circuits within the spinal cord. Female Lewis rats (6–8 weeks old) were divided in three groups: rhizotomy (RZ group), rhizotomy repaired with FS (RZ+FS group) and rhizotomy repaired with FS and MC (RZ+FS+MC group). The behavioral tests electronic von-Frey and Walking track test were carried out. For immunohistochemistry we used markers to detect different synapse profiles as well as glial reaction. The behavioral results showed a significant decrease in sensory and motor function after lesion. The reimplantation decreased glial reaction and improved synaptic plasticity of afferent inputs. Cell therapy further enhanced the rewiring process. In addition, both reimplanted groups presented twice as much motor control compared to the non-treated group. In conclusion, the reimplantation with FS and MC is efficient and may be considered an approach to improve sensory-motor recovery following dorsal rhizotomy. PMID:25249946

Target-specific M1 inputs to infragranular S1 pyramidal neurons

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Fanselow, Erika E.; Simons, Daniel J.

2016-01-01

The functional role of input from the primary motor cortex (M1) to primary somatosensory cortex (S1) is unclear; one key to understanding this pathway may lie in elucidating the cell-type specific microcircuits that connect S1 and M1. Recently, we discovered that a subset of pyramidal neurons in the infragranular layers of S1 receive especially strong input from M1 (Kinnischtzke AK, Simons DJ, Fanselow EE. Cereb Cortex 24: 2237–2248, 2014), suggesting that M1 may affect specific classes of pyramidal neurons differently. Here, using combined optogenetic and retrograde labeling approaches in the mouse, we examined the strengths of M1 inputs to five classes of infragranular S1 neurons categorized by their projections to particular cortical and subcortical targets. We found that the magnitude of M1 synaptic input to S1 pyramidal neurons varies greatly depending on the projection target of the postsynaptic neuron. Of the populations examined, M1-projecting corticocortical neurons in L6 received the strongest M1 inputs, whereas ventral posterior medial nucleus-projecting corticothalamic neurons, also located in L6, received the weakest. Each population also possessed distinct intrinsic properties. The results suggest that M1 differentially engages specific classes of S1 projection neurons, thereby regulating the motor-related influence S1 exerts over subcortical structures. PMID:27334960

Two Classes of Secreted Synaptic Organizers in the Central Nervous System.

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Yuzaki, Michisuke

2018-02-10

Research in the last two decades has identified many synaptic organizers in the central nervous system that directly regulate the assembly of pre- and/or postsynaptic molecules, such as synaptic vesicles, active zone proteins, and neurotransmitter receptors. They are classified into secreted factors and cell adhesion molecules, such as neurexins and neuroligins. Certain secreted factors are termed extracellular scaffolding proteins (ESPs) because they are components of the synaptic extracellular matrix and serve as a scaffold at the synaptic cleft. These include Lgi1, Cbln1, neuronal pentraxins, Hevin, thrombospondins, and glypicans. Diffusible secreted factors, such as Wnts, fibroblast growth factors, and semaphorins, tend to act from a distance. In contrast, ESPs remain at the synaptic cleft and often help synaptic adhesion and/or accumulation of postsynaptic receptors. Many fundamental questions remain about when, how, and why various synaptic organizers establish and modify the vast numbers of connections during development and throughout life.

Electric Dipole Theory of Chemical Synaptic Transmission

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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

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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.

Science.gov (United States)

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.

Synaptic transmission block by presynaptic injection of oligomeric amyloid beta

Science.gov (United States)

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

Identification of the structure parameters using short-time non-stationary stochastic excitation

Science.gov (United States)

Jarczewska, Kamila; Koszela, Piotr; Śniady, PaweŁ; Korzec, Aleksandra

2011-07-01

In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input-output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

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

Directory of Open Access Journals (Sweden)

Johannes eBill

2014-12-01

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

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

Science.gov (United States)

Bill, Johannes; Legenstein, Robert

2014-01-01

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

Translational control by eIF2α phosphorylation regulates vulnerability to the synaptic and behavioral effects of cocaine

Science.gov (United States)

Huang, Wei; Placzek, Andon N; Viana Di Prisco, Gonzalo; Khatiwada, Sanjeev; Sidrauski, Carmela; Krnjević, Krešimir; Walter, Peter; Dani, John A; Costa-Mattioli, Mauro

2016-01-01

Adolescents are especially prone to drug addiction, but the underlying biological basis of their increased vulnerability remains unknown. We reveal that translational control by phosphorylation of the translation initiation factor eIF2α (p-eIF2α) accounts for adolescent hypersensitivity to cocaine. In adolescent (but not adult) mice, a low dose of cocaine reduced p-eIF2α in the ventral tegmental area (VTA), potentiated synaptic inputs to VTA dopaminergic neurons, and induced drug-reinforced behavior. Like adolescents, adult mice with reduced p-eIF2α-mediated translational control were more susceptible to cocaine-induced synaptic potentiation and behavior. Conversely, like adults, adolescent mice with increased p-eIF2α became more resistant to cocaine's effects. Accordingly, metabotropic glutamate receptor-mediated long-term depression (mGluR-LTD)—whose disruption is postulated to increase vulnerability to drug addiction—was impaired in both adolescent mice and adult mice with reduced p-eIF2α mediated translation. Thus, during addiction, cocaine hijacks translational control by p-eIF2α, initiating synaptic potentiation and addiction-related behaviors. These insights may hold promise for new treatments for addiction. DOI: http://dx.doi.org/10.7554/eLife.12052.001 PMID:26928234

Phosphodiesterase Inhibition to Target the Synaptic Dysfunction in Alzheimer's Disease

Science.gov (United States)

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.

Sensitivity analysis of complex models: Coping with dynamic and static inputs

International Nuclear Information System (INIS)

Anstett-Collin, F.; Goffart, J.; Mara, T.; Denis-Vidal, L.

2015-01-01

In this paper, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the one of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen–Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient sampling-based strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a building energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house. - Highlights: • Sensitivity analysis of models with uncertain static and dynamic inputs is performed. • Karhunen–Loève (KL) decomposition of the spatio/temporal inputs is performed. • The influence of the dynamic inputs is studied through the modes of the KL expansion. • The proposed approach is applied to a building energy model. • Impact of weather data and material properties on performance of real house is given

Myopic (HD-PTP, PTPN23) selectively regulates synaptic neuropeptide release.

Science.gov (United States)

Bulgari, Dinara; Jha, Anupma; Deitcher, David L; Levitan, Edwin S

2018-02-13

Neurotransmission is mediated by synaptic exocytosis of neuropeptide-containing dense-core vesicles (DCVs) and small-molecule transmitter-containing small synaptic vesicles (SSVs). Exocytosis of both vesicle types depends on Ca 2+ and shared secretory proteins. Here, we show that increasing or decreasing expression of Myopic (mop, HD-PTP, PTPN23), a Bro1 domain-containing pseudophosphatase implicated in neuronal development and neuropeptide gene expression, increases synaptic neuropeptide stores at the Drosophila neuromuscular junction (NMJ). This occurs without altering DCV content or transport, but synaptic DCV number and age are increased. The effect on synaptic neuropeptide stores is accounted for by inhibition of activity-induced Ca 2+ -dependent neuropeptide release. cAMP-evoked Ca 2+ -independent synaptic neuropeptide release also requires optimal Myopic expression, showing that Myopic affects the DCV secretory machinery shared by cAMP and Ca 2+ pathways. Presynaptic Myopic is abundant at early endosomes, but interaction with the endosomal sorting complex required for transport III (ESCRT III) protein (CHMP4/Shrub) that mediates Myopic's effect on neuron pruning is not required for control of neuropeptide release. Remarkably, in contrast to the effect on DCVs, Myopic does not affect release from SSVs. Therefore, Myopic selectively regulates synaptic DCV exocytosis that mediates peptidergic transmission at the NMJ.

Strain-dependent variations in spatial learning and in hippocampal synaptic plasticity in the dentate gyrus of freely behaving rats

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.

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

Development of the Stochastic Lung Model for Asthma

International Nuclear Information System (INIS)

Dobos, E.; Borbely-Kiss, I.; Kertesz, Zs.; Balashazy, I.

2005-01-01

Complete text of publication follows. The Stochastic Lung Model is a state-of-the-art tool for the investigation of the health impact of atmospheric aerosols. This model has already been tested and applied to calculate the deposition fractions of aerosols in different regions of the human respiratory tract. The health effects of inhaled aerosols may strongly depend on the distribution of deposition within the respiratory tract. In the current study three Asthma Models have been incorporated into the Stochastic Lung Deposition Code. A common new feature of these models is that the breathing cycle may be asymmetric. It means that the inspiration time, the expiration time and the two breath hold times are independent. And the code can simulate the mucus blockage, too. The main characteristics of the models are the followings: a) ASTHMA MODEL I: One input bronchial asthma factor is applied for the whole tracheobronchial region. The code multiplies all tracheobroncial diameters with this single value. b) ASTHMA MODEL II: Bronchial asthma factors have to be given for each bronchial generation as input data (21 values). The program multiplies the diameter of bronchi with these factors. c) ASTHMA MODEL III: Here, only the range of bronchial asthma factors are presented as input data and the code selects randomly the exact factors in pre-described airway generations. In this case the stochastic character appears in the Asthma Model, as well. As an example, Figure 1 shows the deposition fractions in the tracheobronchial and acinar regions of the human lung in the case of healthy and asthmatic adults at sitting breathing conditions as a function of particle size computed by Asthma Model I where the bronchial asthma factor was 30%. These models have been tested and compared for different types of asthma at various breathing conditions and in a wide range of particle sizes. The distribution of deposition in the characteristic regions of the respiratory tract have been computed

Numerical resolution of N-dimensional Fokker-Plank stochastic equations

International Nuclear Information System (INIS)

Garcia-Olivares, A.; Muoz, A.

1992-01-01

This document describes the use of a library of programs able to solve stochastic Fokker-Planck equations in a N-dimensional space. the input data are essentially: (i) the initial distribution of the stochastic variable, (ii) the drift and fluctuation coefficients as a function of the state (which can be obtained from the transition probabilities between neighboring states) and (iii) some parameters controlling the run. A last version of the library accepts sources and sinks defined in the states space. The output is the temporal evolution of the probability distribution in the space defined by a N-dimensional grid. Some applications and readings in Synergetics, Self-Organization, transport phenomena, Ecology and other fields are suggested. If the probability distribution is interpreted as a distribution of particles then the codes can be used to solve the N-dimensional problem of advection-diffusion. (author) 21 fig. 16 ref

Numerical Resolution of N-dimensional Fokker-Planck stochastic equations

International Nuclear Information System (INIS)

Garcia-Olivares, R. A.; Munoz Roldan, A.

1992-01-01

This document describes the use of a library of programs able to solve stochastic Fokker-Planck equations in a N-dimensional space. The input data are essentially: (i) the initial distribution of the stochastic variable, (ii) the drift and fluctuation coefficients as a function of the state (which can be obtained from the transition probabilities between neighboring states) and (iii) some parameters controlling the run. A last version of the library accepts sources and sinks defined in the states space. The output is the temporal evolution of the probability distribution in the space defined by a N-dimensional grid. Some applications and readings in Synergetic, Self-Organization, transport phenomena, Ecology and other fields are suggested. If the probability distribution is interpreted as a distribution of particles then the codes can be used to solve the N-dimensional problem of advection-diffusion. (Author) 16 refs

Using Stochastic Dynamic Programming to Support Water Resources Management in the Ziya River Basin, China

DEFF Research Database (Denmark)

Davidsen, Claus; Cardenal, Silvio Javier Pereira; Liu, Suxia

2015-01-01

of stochastic dynamic programming, to optimize water resources management in the Ziya River basin. Natural runoff from the upper basin was estimated with a rainfall-runoff model autocalibrated using in situ measured discharge. The runoff serial correlation was described by a Markov chain and used as input...

Fragile X Mental Retardation Protein Regulates Activity-Dependent Membrane Trafficking and Trans-Synaptic Signaling Mediating Synaptic Remodeling

Science.gov (United States)

Sears, James C.; Broadie, Kendal

2018-01-01

Fragile X syndrome (FXS) is the leading monogenic cause of autism and intellectual disability. The disease arises through loss of fragile X mental retardation protein (FMRP), which normally exhibits peak expression levels in early-use critical periods, and is required for activity-dependent synaptic remodeling during this transient developmental window. FMRP canonically binds mRNA to repress protein translation, with targets that regulate cytoskeleton dynamics, membrane trafficking, and trans-synaptic signaling. We focus here on recent advances emerging in these three areas from the Drosophila disease model. In the well-characterized central brain mushroom body (MB) olfactory learning/memory circuit, FMRP is required for activity-dependent synaptic remodeling of projection neurons innervating the MB calyx, with function tightly restricted to an early-use critical period. FMRP loss is phenocopied by conditional removal of FMRP only during this critical period, and rescued by FMRP conditional expression only during this critical period. Consistent with FXS hyperexcitation, FMRP loss defects are phenocopied by heightened sensory experience and targeted optogenetic hyperexcitation during this critical period. FMRP binds mRNA encoding Drosophila ESCRTIII core component Shrub (human CHMP4 homolog) to restrict Shrub translation in an activity-dependent mechanism only during this same critical period. Shrub mediates endosomal membrane trafficking, and perturbing Shrub expression is known to interfere with neuronal process pruning. Consistently, FMRP loss and Shrub overexpression targeted to projection neurons similarly causes endosomal membrane trafficking defects within synaptic boutons, and genetic reduction of Shrub strikingly rescues Drosophila FXS model defects. In parallel work on the well-characterized giant fiber (GF) circuit, FMRP limits iontophoretic dye loading into central interneurons, demonstrating an FMRP role controlling core neuronal properties through the

Contribution to the stochastically studies of space-time dependable hydrological processes

International Nuclear Information System (INIS)

Kjaevski, Ivancho

2002-12-01

meteorological processes, directly, which enable incorrect mathematical models to determinate hydro meteorological processes. There are three basic ways to create models for hydro meteorological processes until now: deterministic, stochastic or combination of deterministic-stochastic approaches. In this Doctoral dissertation, stochastic approach is been studied in modeling of hydro meteorological processes. Advantage of the stochastic approach is more less parameters of the technique than the deterministic approach. Some of the stochastic techniques are transformation the precipitation in run-off, prognosis of future water flow and water quality, estimate of effect to the environment etc. The newest mathematical-statistical method, ARIMA and ARMAX models, will be described and explained in the following text. These methods are applied in studying and modeling of hydro meteorological processes. The following hydro meteorological processes are analyzed in this Doctoral dissertation, which have most frequently measuring and they have largest amount of information: i) Amount of year and month precipitation; ii) Average year and average month air temperature; iii) Average year and average month water flow; Almost all spice-time hydro meteorological processes including the processes with little time intervals (decade, day or hour) could be analyzed and modeled with same or similar techniques. This doctoral dissertation is divided in five chapters. The most applied mathematic-statistic para metrical and no parameter's methods for testing the homogeneity and consistency of hydro meteorological processes, which are caused by natural or artificial influence are presented in the flat and second chapter. The mathematical algorithm for creating one-dimensional ARIMA stochastic model, and transfer function model (TFM), known as (ARMAX) model are presented in the third chapter. The TFM model is applied to define the stochastic relation between one or more input and one output series

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

International Nuclear Information System (INIS)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

2016-01-01

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

Energy Technology Data Exchange (ETDEWEB)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin, E-mail: dengbin@tju.edu.cn; Chan, Wai-lok [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2016-06-15

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

A Simple Stochastic Differential Equation with Discontinuous Drift

DEFF Research Database (Denmark)

Simonsen, Maria; Leth, John-Josef; Schiøler, Henrik

2013-01-01

In this paper we study solutions to stochastic differential equations (SDEs) with discontinuous drift. We apply two approaches: The Euler-Maruyama method and the Fokker-Planck equation and show that a candidate density function based on the Euler-Maruyama method approximates a candidate density...... function based on the stationary Fokker-Planck equation. Furthermore, we introduce a smooth function which approximates the discontinuous drift and apply the Euler-Maruyama method and the Fokker-Planck equation with this input. The point of departure for this work is a particular SDE with discontinuous...

Efficient stochastic EMC/EMI analysis using HDMR-generated surrogate models

KAUST Repository

Yücel, Abdulkadir C.

2011-08-01

Stochastic methods have been used extensively to quantify effects due to uncertainty in system parameters (e.g. material, geometrical, and electrical constants) and/or excitation on observables pertinent to electromagnetic compatibility and interference (EMC/EMI) analysis (e.g. voltages across mission-critical circuit elements) [1]. In recent years, stochastic collocation (SC) methods, especially those leveraging generalized polynomial chaos (gPC) expansions, have received significant attention [2, 3]. SC-gPC methods probe surrogate models (i.e. compact polynomial input-output representations) to statistically characterize observables. They are nonintrusive, that is they use existing deterministic simulators, and often cost only a fraction of direct Monte-Carlo (MC) methods. Unfortunately, SC-gPC-generated surrogate models often lack accuracy (i) when the number of uncertain/random system variables is large and/or (ii) when the observables exhibit rapid variations. © 2011 IEEE.

Considering inventory distributions in a stochastic periodic inventory routing system

Science.gov (United States)

Yadollahi, Ehsan; Aghezzaf, El-Houssaine

2017-07-01

Dealing with the stochasticity of parameters is one of the critical issues in business and industry nowadays. Supply chain planners have difficulties in forecasting stochastic parameters of a distribution system. Demand rates of customers during their lead time are one of these parameters. In addition, holding a huge level of inventory at the retailers is costly and inefficient. To cover the uncertainty of forecasting demand rates, researchers have proposed the usage of safety stock to avoid stock-out. However, finding the precise level of safety stock depends on forecasting the statistical distribution of demand rates and their variations in different settings among the planning horizon. In this paper the demand rate distributions and its parameters are taken into account for each time period in a stochastic periodic IRP. An analysis of the achieved statistical distribution of the inventory and safety stock level is provided to measure the effects of input parameters on the output indicators. Different values for coefficient of variation are applied to the customers' demand rate in the optimization model. The outcome of the deterministic equivalent model of SPIRP is simulated in form of an illustrative case.

Synaptic membrane rafts: traffic lights for local neurotrophin signaling?

Science.gov (United States)

Zonta, Barbara; Minichiello, Liliana

2013-10-18

Lipid rafts, cholesterol and lipid rich microdomains, are believed to play important roles as platforms for the partitioning of transmembrane and synaptic proteins involved in synaptic signaling, plasticity, and maintenance. There is increasing evidence of a physical interaction between post-synaptic densities and post-synaptic lipid rafts. Localization of proteins within lipid rafts is highly regulated, and therefore lipid rafts may function as traffic lights modulating and fine-tuning neuronal signaling. The tyrosine kinase neurotrophin receptors (Trk) and the low-affinity p75 neurotrophin receptor (p75(NTR)) are enriched in neuronal lipid rafts together with the intermediates of downstream signaling pathways, suggesting a possible role of rafts in neurotrophin signaling. Moreover, neurotrophins and their receptors are involved in the regulation of cholesterol metabolism. Cholesterol is an important component of lipid rafts and its depletion leads to gradual loss of synapses, underscoring the importance of lipid rafts for proper neuronal function. Here, we review and discuss the idea that translocation of neurotrophin receptors in synaptic rafts may account for the selectivity of their transduced signals.

Synaptic membrane rafts: traffic lights for local neurotrophin signalling?

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Barbara eZonta

2013-10-01

Full Text Available Lipid rafts, cholesterol and lipid rich microdomains, are believed to play important roles as platforms for the partitioning of transmembrane and synaptic proteins involved in synaptic signalling, plasticity and maintenance. There is increasing evidence of a physical interaction between post-synaptic densities and post-synaptic lipid rafts. Localization of proteins within lipid rafts is highly regulated, and therefore lipid rafts may function as traffic lights modulating and fine-tuning neuronal signalling. The tyrosine kinase neurotrophin receptors (Trk and the low-affinity p75 neurotrophin receptor (p75NTR are enriched in neuronal lipid rafts together with the intermediates of downstream signalling pathways, suggesting a possible role of rafts in neurotrophin signalling. Moreover, neurotrophins and their receptors are involved in the regulation of cholesterol metabolism. Cholesterol is an important component of lipid rafts and its depletion leads to gradual loss of synapses, underscoring the importance of lipid rafts for proper neuronal function. Here, we review and discuss the idea that translocation of neurotrophin receptors in synaptic rafts may account for the selectivity of their transduced signals.

Communication: Limitations of the stochastic quasi-steady-state approximation in open biochemical reaction networks

Science.gov (United States)

Thomas, Philipp; Straube, Arthur V.; Grima, Ramon

2011-11-01

It is commonly believed that, whenever timescale separation holds, the predictions of reduced chemical master equations obtained using the stochastic quasi-steady-state approximation are in very good agreement with the predictions of the full master equations. We use the linear noise approximation to obtain a simple formula for the relative error between the predictions of the two master equations for the Michaelis-Menten reaction with substrate input. The reduced approach is predicted to overestimate the variance of the substrate concentration fluctuations by as much as 30%. The theoretical results are validated by stochastic simulations using experimental parameter values for enzymes involved in proteolysis, gluconeogenesis, and fermentation.

Information transfer with rate-modulated Poisson processes: a simple model for nonstationary stochastic resonance.

Science.gov (United States)

Goychuk, I

2001-08-01

Stochastic resonance in a simple model of information transfer is studied for sensory neurons and ensembles of ion channels. An exact expression for the information gain is obtained for the Poisson process with the signal-modulated spiking rate. This result allows one to generalize the conventional stochastic resonance (SR) problem (with periodic input signal) to the arbitrary signals of finite duration (nonstationary SR). Moreover, in the case of a periodic signal, the rate of information gain is compared with the conventional signal-to-noise ratio. The paper establishes the general nonequivalence between both measures notwithstanding their apparent similarity in the limit of weak signals.

Electrophysiological characterization of male goldfish (Carassius auratus ventral preoptic area neurons receiving olfactory inputs

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Wudu E. Lado

2014-06-01

Full Text Available Chemical communication via sex pheromones is critical for successful reproduction but the underlying neural mechanisms are not well-understood. The goldfish is a tractable model because sex pheromones have been well-characterized in this species. We used male goldfish forebrain explants in vitro and performed whole-cell current clamp recordings from single neurons in the ventral preoptic area (vPOA to characterize their membrane properties and synaptic inputs from the olfactory bulbs (OB. Principle component and cluster analyses based on intrinsic membrane properties of vPOA neurons (N = 107 revealed five (I-V distinct cell groups. These cells displayed differences in their input resistance (Rinput: I II = IV > III = V. Evidence from electrical stimulation of the OB and application of receptor antagonists suggests that vPOA neurons receive monosynaptic glutamatergic inputs via the medial olfactory tract, with connectivity varying among neuronal groups [I (24%, II (40%, III (0%, IV (34% and V (2%].

Stochastic approach to the derivation of emission limits for wastewater treatment plants.

Science.gov (United States)

Stransky, D; Kabelkova, I; Bares, V

2009-01-01

Stochastic approach to the derivation of WWTP emission limits meeting probabilistically defined environmental quality standards (EQS) is presented. The stochastic model is based on the mixing equation with input data defined by probability density distributions and solved by Monte Carlo simulations. The approach was tested on a study catchment for total phosphorus (P(tot)). The model assumes input variables independency which was proved for the dry-weather situation. Discharges and P(tot) concentrations both in the study creek and WWTP effluent follow log-normal probability distribution. Variation coefficients of P(tot) concentrations differ considerably along the stream (c(v)=0.415-0.884). The selected value of the variation coefficient (c(v)=0.420) affects the derived mean value (C(mean)=0.13 mg/l) of the P(tot) EQS (C(90)=0.2 mg/l). Even after supposed improvement of water quality upstream of the WWTP to the level of the P(tot) EQS, the WWTP emission limits calculated would be lower than the values of the best available technology (BAT). Thus, minimum dilution ratios for the meaningful application of the combined approach to the derivation of P(tot) emission limits for Czech streams are discussed.

Risk-sensitive control of stochastic hybrid systems on infinite time horizon

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Runolfsson Thordur

1999-01-01

Full Text Available A risk-sensitive optimal control problem is considered for a hybrid system that consists of continuous time diffusion process that depends on a discrete valued mode variable that is modeled as a Markov chain. Optimality conditions are presented and conditions for the existence of optimal controls are derived. It is shown that the optimal risk-sensitive control problem is equivalent to the upper value of an associated stochastic differential game, and insight into the contributions of the noise input and mode variable to the risk sensitivity of the cost functional is given. Furthermore, it is shown that due to the mode variable risk sensitivity, the equivalence relationship that has been observed between risk-sensitive and H ∞ control in the nonhybrid case does not hold for stochastic hybrid systems.

STOCHASTIC FLOWS OF MAPPINGS

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.

Deep brain stimulation of the amygdala alleviates fear conditioning-induced alterations in synaptic plasticity in the cortical-amygdala pathway and fear memory.

Science.gov (United States)

Sui, Li; Huang, SiJia; Peng, BinBin; Ren, Jie; Tian, FuYing; Wang, Yan

2014-07-01

Deep brain stimulation (DBS) of the amygdala has been demonstrated to modulate hyperactivity of the amygdala, which is responsible for the symptoms of post-traumatic stress disorder (PTSD), and thus might be used for the treatment of PTSD. However, the underlying mechanism of DBS of the amygdala in the modulation of the amygdala is unclear. The present study investigated the effects of DBS of the amygdala on synaptic transmission and synaptic plasticity at cortical inputs to the amygdala, which is critical for the formation and storage of auditory fear memories, and fear memories. The results demonstrated that auditory fear conditioning increased single-pulse-evoked field excitatory postsynaptic potentials in the cortical-amygdala pathway. Furthermore, auditory fear conditioning decreased the induction of paired-pulse facilitation and long-term potentiation, two neurophysiological models for studying short-term and long-term synaptic plasticity, respectively, in the cortical-amygdala pathway. In addition, all these auditory fear conditioning-induced changes could be reversed by DBS of the amygdala. DBS of the amygdala also rescued auditory fear conditioning-induced enhancement of long-term retention of fear memory. These findings suggested that DBS of the amygdala alleviating fear conditioning-induced alterations in synaptic plasticity in the cortical-amygdala pathway and fear memory may underlie the neuromodulatory role of DBS of the amygdala in activities of the amygdala.

Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks

International Nuclear Information System (INIS)

Sun, Z.; Sen, A.K.; Longman, R.W.

2006-01-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used

Defective glycinergic synaptic transmission in zebrafish motility mutants

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Hiromi Hirata

2010-01-01

Full Text Available 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 transmission due to a lack of synaptic aggregation of GlyRs. Due to the consequent loss of reciprocal inhibition of motor circuits between the two sides of the spinal cord, motor neurons activate simultaneously on both sides resulting in bilateral contraction of axial muscles of beo mutants, eliciting the so-called ‘accordion’ phenotype. Similar defects in GlyR subunit genes have been observed in several mammals and are the basis for human hyperekplexia/startle disease. By contrast, zebrafish shocked (sho mutants have a defect in slc6a9, encoding GlyT1, a glycine transporter that is expressed by astroglial cells surrounding the glycinergic synapse in the hindbrain and spinal cord. GlyT1 mediates rapid uptake of glycine from the synaptic cleft, terminating synaptic transmission. In zebrafish sho mutants, there appears to be elevated extracellular glycine resulting in persistent inhibition of postsynaptic neurons and subsequent reduced motility, causing the ‘twitch once’ phenotype. We review current knowledge regarding zebrafish ‘accordion’ and ‘twitch once’ mutants, including beo and sho, and report the identification of a new α2 subunit that revises the phylogeny of zebrafish GlyRs.

Defective Glycinergic Synaptic Transmission in Zebrafish Motility Mutants

Science.gov (United States)

Hirata, Hiromi; Carta, Eloisa; Yamanaka, Iori; Harvey, Robert J.; Kuwada, John Y.

2009-01-01

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 transmission due to a lack of synaptic aggregation of GlyRs. Due to the consequent loss of reciprocal inhibition of motor circuits between the two sides of the spinal cord, motor neurons activate simultaneously on both sides resulting in bilateral contraction of axial muscles of beo mutants, eliciting the so-called ‘accordion’ phenotype. Similar defects in GlyR subunit genes have been observed in several mammals and are the basis for human hyperekplexia/startle disease. By contrast, zebrafish shocked (sho) mutants have a defect in slc6a9, encoding GlyT1, a glycine transporter that is expressed by astroglial cells surrounding the glycinergic synapse in the hindbrain and spinal cord. GlyT1 mediates rapid uptake of glycine from the synaptic cleft, terminating synaptic transmission. In zebrafish sho mutants, there appears to be elevated extracellular glycine resulting in persistent inhibition of postsynaptic neurons and subsequent reduced motility, causing the ‘twitch-once’ phenotype. We review current knowledge regarding zebrafish ‘accordion’ and ‘twitch-once’ mutants, including beo and sho, and report the identification of a new α2 subunit that revises the phylogeny of zebrafish GlyRs. PMID:20161699

Effect of mechanical tactile noise on amplitude of visual evoked potentials: multisensory stochastic resonance.

Science.gov (United States)

Méndez-Balbuena, Ignacio; Huidobro, Nayeli; Silva, Mayte; Flores, Amira; Trenado, Carlos; Quintanar, Luis; Arias-Carrión, Oscar; Kristeva, Rumyana; Manjarrez, Elias

2015-10-01

The present investigation documents the electrophysiological occurrence of multisensory stochastic resonance in the human visual pathway elicited by tactile noise. We define multisensory stochastic resonance of brain evoked potentials as the phenomenon in which an intermediate level of input noise of one sensory modality enhances the brain evoked response of another sensory modality. Here we examined this phenomenon in visual evoked potentials (VEPs) modulated by the addition of tactile noise. Specifically, we examined whether a particular level of mechanical Gaussian noise applied to the index finger can improve the amplitude of the VEP. We compared the amplitude of the positive P100 VEP component between zero noise (ZN), optimal noise (ON), and high mechanical noise (HN). The data disclosed an inverted U-like graph for all the subjects, thus demonstrating the occurrence of a multisensory stochastic resonance in the P100 VEP. Copyright © 2015 the American Physiological Society.

The Secreted Protein C1QL1 and Its Receptor BAI3 Control the Synaptic Connectivity of Excitatory Inputs Converging on Cerebellar Purkinje Cells

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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.

Stochastic processes

CERN Document Server

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

Semi-blind identification of wideband MIMO channels via stochastic sampling

OpenAIRE

Andrieu, Christophe; Piechocki, Robert J.; McGeehan, Joe P.; Armour, Simon M.

2003-01-01

In this paper we address the problem of wide-band multiple-input multiple-output (MIMO) channel (multidimensional time invariant FIR filter) identification using Markov chains Monte Carlo methods. Towards this end we develop a novel stochastic sampling technique that produces a sequence of multidimensional channel samples. The method is semi-blind in the sense that it uses a very short training sequence. In such a framework the problem is no longer analytically tractable; hence we resort to s...

A Modified Adaptive Stochastic Resonance for Detecting Faint Signal in Sensors

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Hengwei Li

2007-02-01

Full Text Available In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR. We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT. Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that th

Experience-Dependent Equilibration of AMPAR-Mediated Synaptic Transmission during the Critical Period

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Kyung-Seok Han

2017-01-01

Full Text Available Experience-dependent synapse refinement is essential for functional optimization of neural circuits. However, how sensory experience sculpts excitatory synaptic transmission is poorly understood. Here, we show that despite substantial remodeling of synaptic connectivity, AMPAR-mediated synaptic transmission remains at equilibrium during the critical period in the mouse primary visual cortex. The maintenance of this equilibrium requires neurogranin (Ng, a postsynaptic calmodulin-binding protein important for synaptic plasticity. With normal visual experience, loss of Ng decreased AMPAR-positive synapse numbers, prevented AMPAR-silent synapse maturation, and increased spine elimination. Importantly, visual deprivation halted synapse loss caused by loss of Ng, revealing that Ng coordinates experience-dependent AMPAR-silent synapse conversion to AMPAR-active synapses and synapse elimination. Loss of Ng also led to sensitized long-term synaptic depression (LTD and impaired visually guided behavior. Our synaptic interrogation reveals that experience-dependent coordination of AMPAR-silent synapse conversion and synapse elimination hinges upon Ng-dependent mechanisms for constructive synaptic refinement during the critical period.

Stochastic Frontier Production Analysis of Tobacco Growers in District Mardan, Pakistan

International Nuclear Information System (INIS)

Saddozai, K. N; Nasrullah, M.; Khan, N. P.

2015-01-01

The theme of this research was to analyze the stochastic frontier production of tobacco growers. This parametric approach was encompassed to investigate the technical efficiency of growers. The primary data was gleaned during 2014-15 from sampled population of three villages namely Takkar Kali, Garo Shah and Passand Kali of Takhtbhai Tehsil, Mardan district of Khyber Pakhtunkhwa province. The multi-stage sampling technique was utilized to obtain the desired sample size of 120 tobacco growers. The major findings of stochastic production frontier analysis indicate that all variables were statistically significant and have portrayed positive contribution to tobacco production except fertilizer which was found significant but has revealed inverse relation with tobacco production. The mean technical efficiency was estimated at 0.85 depicting that tobacco growers can further amplify efficiency by 15% with given level of inputs. The inefficiency model estimates demonstrate that only experience of tobacco growers in study area was significantly decreasing the inefficiency of the growers. The study has concluded that tobacco growers are operating in the second stage of production; therefore, tobacco production can still be enhanced. It is recommended that season long trainings for tobacco growers may be undertaken by the concerned authorities to enhance the crop management skills for rational use of input. (author)

On the precision of quasi steady state assumptions in stochastic dynamics

Science.gov (United States)

Agarwal, Animesh; Adams, Rhys; Castellani, Gastone C.; Shouval, Harel Z.

2012-07-01

Many biochemical networks have complex multidimensional dynamics and there is a long history of methods that have been used for dimensionality reduction for such reaction networks. Usually a deterministic mass action approach is used; however, in small volumes, there are significant fluctuations from the mean which the mass action approach cannot capture. In such cases stochastic simulation methods should be used. In this paper, we evaluate the applicability of one such dimensionality reduction method, the quasi-steady state approximation (QSSA) [L. Menten and M. Michaelis, "Die kinetik der invertinwirkung," Biochem. Z 49, 333369 (1913)] for dimensionality reduction in case of stochastic dynamics. First, the applicability of QSSA approach is evaluated for a canonical system of enzyme reactions. Application of QSSA to such a reaction system in a deterministic setting leads to Michaelis-Menten reduced kinetics which can be used to derive the equilibrium concentrations of the reaction species. In the case of stochastic simulations, however, the steady state is characterized by fluctuations around the mean equilibrium concentration. Our analysis shows that a QSSA based approach for dimensionality reduction captures well the mean of the distribution as obtained from a full dimensional simulation but fails to accurately capture the distribution around that mean. Moreover, the QSSA approximation is not unique. We have then extended the analysis to a simple bistable biochemical network model proposed to account for the stability of synaptic efficacies; the substrate of learning and memory [J. E. Lisman, "A mechanism of memory storage insensitive to molecular turnover: A bistable autophosphorylating kinase," Proc. Natl. Acad. Sci. U.S.A. 82, 3055-3057 (1985)], 10.1073/pnas.82.9.3055. Our analysis shows that a QSSA based dimensionality reduction method results in errors as big as two orders of magnitude in predicting the residence times in the two stable states.

Regulation of spike timing-dependent plasticity of olfactory inputs in mitral cells in the rat olfactory bulb.

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Teng-Fei Ma

Full Text Available The recent history of activity input onto granule cells (GCs in the main olfactory bulb can affect the strength of lateral inhibition, which functions to generate contrast enhancement. However, at the plasticity level, it is unknown whether and how the prior modification of lateral inhibition modulates the subsequent induction of long-lasting changes of the excitatory olfactory nerve (ON inputs to mitral cells (MCs. Here we found that the repetitive stimulation of two distinct excitatory inputs to the GCs induced a persistent modification of lateral inhibition in MCs in opposing directions. This bidirectional modification of inhibitory inputs differentially regulated the subsequent synaptic plasticity of the excitatory ON inputs to the MCs, which was induced by the repetitive pairing of excitatory postsynaptic potentials (EPSPs with postsynaptic bursts. The regulation of spike timing-dependent plasticity (STDP was achieved by the regulation of the inter-spike-interval (ISI of the postsynaptic bursts. This novel form of inhibition-dependent regulation of plasticity may contribute to the encoding or processing of olfactory information in the olfactory bulb.

Synaptic Homeostasis and Allostasis in the Dentate Gyrus Caused by Inflammatory and Neuropathic Pain Conditions

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Rui-Rui Wang

2018-01-01

Full Text Available It has been generally accepted that pain can cause imbalance between excitation and inhibition (homeostasis at the synaptic level. However, it remains poorly understood how this imbalance (allostasis develops in the CNS under different pain conditions. Here, we analyzed the changes in both excitatory and inhibitory synaptic transmission and modulation of the dentate gyrus (DG under two pain conditions with different etiology and duration. First, it was revealed that the functions of the input-output (I/O curves for evoked excitatory postsynaptic currents (eEPSCs following the perforant path (PP stimulation were gained under both acute inflammatory and chronic neuropathic pain conditions relative to the controls. However, the functions of I/O curves for the PP-evoked inhibitory postsynaptic currents (eIPSCs differed between the two conditions, namely it was greatly gained under inflammatory condition, but was reduced under neuropathic condition in reverse. Second, both the frequency and amplitude of miniature IPSCs (mIPSCs were increased under inflammatory condition, however a decrease in frequency of mIPSCs was observed under neuropathic condition. Finally, the spike discharge of the DG granule cells in response to current injection was significantly increased by neuropathic pain condition, however, no different change was found between inflammatory pain condition and the control. These results provide another line of evidence showing homeostatic and allostatic modulation of excitatory synaptic transmission by inhibitory controls under different pathological pain conditions, hence implicating use of different therapeutic approaches to maintain the homeostasis between excitation and inhibition while treating different conditions of pathological pain.

A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking

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Sushmita Lakshmi Allam

2012-10-01

Full Text Available Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

Science.gov (United States)

Béhuret, Sébastien; Deleuze, Charlotte; Bal, Thierry

2015-01-01

A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus. PMID:26733818

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

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Qiang Yu

Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons.

Science.gov (United States)

Béhuret, Sébastien; Deleuze, Charlotte; Bal, Thierry

2015-01-01

A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

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Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

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Sébastien eBéhuret

2015-12-01

Full Text Available A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

Cell-specific gain modulation by synaptically released zinc in cortical circuits of audition.

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Anderson, Charles T; Kumar, Manoj; Xiong, Shanshan; Tzounopoulos, Thanos

2017-09-09

In many excitatory synapses, mobile zinc is found within glutamatergic vesicles and is coreleased with glutamate. Ex vivo studies established that synaptically released (synaptic) zinc inhibits excitatory neurotransmission at lower frequencies of synaptic activity but enhances steady state synaptic responses during higher frequencies of activity. However, it remains unknown how synaptic zinc affects neuronal processing in vivo. Here, we imaged the sound-evoked neuronal activity of the primary auditory cortex in awake mice. We discovered that synaptic zinc enhanced the gain of sound-evoked responses in CaMKII-expressing principal neurons, but it reduced the gain of parvalbumin- and somatostatin-expressing interneurons. This modulation was sound intensity-dependent and, in part, NMDA receptor-independent. By establishing a previously unknown link between synaptic zinc and gain control of auditory cortical processing, our findings advance understanding about cortical synaptic mechanisms and create a new framework for approaching and interpreting the role of the auditory cortex in sound processing.

Stochastic narrow escape in molecular and cellular biology analysis and applications

CERN Document Server

Holcman, David

2015-01-01

This book covers recent developments in the non-standard asymptotics of the mathematical narrow escape problem in stochastic theory, as well as applications of the narrow escape problem in cell biology. The first part of the book concentrates on mathematical methods, including advanced asymptotic methods in partial equations, and is aimed primarily at applied mathematicians and theoretical physicists who are interested in biological applications. The second part of the book is intended for computational biologists, theoretical chemists, biochemists, biophysicists, and physiologists. It includes a summary of output formulas from the mathematical portion of the book and concentrates on their applications in modeling specific problems in theoretical molecular and cellular biology. Critical biological processes, such as synaptic plasticity and transmission, activation of genes by transcription factors, or double-strained DNA break repair, are controlled by diffusion in structures that have both large and small sp...

Spatiotemporal discrimination in neural networks with short-term synaptic plasticity

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Shlaer, Benjamin; Miller, Paul

2015-03-01

Cells in recurrently connected neural networks exhibit bistability, which allows for stimulus information to persist in a circuit even after stimulus offset, i.e. short-term memory. However, such a system does not have enough hysteresis to encode temporal information about the stimuli. The biophysically described phenomenon of synaptic depression decreases synaptic transmission strengths due to increased presynaptic activity. This short-term reduction in synaptic strengths can destabilize attractor states in excitatory recurrent neural networks, causing the network to move along stimulus dependent dynamical trajectories. Such a network can successfully separate amplitudes and durations of stimuli from the number of successive stimuli. Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression. Front. Comput. Neurosci. 7:59., and so provides a strong candidate network for the encoding of spatiotemporal information. Here we explicitly demonstrate the capability of a recurrent neural network with short-term synaptic depression to discriminate between the temporal sequences in which spatial stimuli are presented.

[Involvement of aquaporin-4 in synaptic plasticity, learning and memory].

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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.

Neuronal cytoskeleton in synaptic plasticity and regeneration.

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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.

Gating of long-term potentiation by nicotinic acetylcholine receptors at the cerebellum input stage.

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Francesca Prestori

Full Text Available The 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 capacity and impair cerebellar functioning, which suggests that neuromodulators are required to gate plasticity processes. Cholinergic systems innervating the cerebellum are thought to enhance procedural learning and memory. Here we show that a specific subtype of acetylcholine receptors, the α7-nAChRs, are distributed both in cerebellar mossy fibre terminals and granule cell dendrites and contribute substantially to synaptic regulation. Selective α7-nAChR activation enhances the postsynaptic calcium increase, allowing weak mossy fibre bursts, which would otherwise cause LTD, to generate robust LTP. The local microperfusion of α7-nAChR agonists could also lead to in vivo switching of LTD to LTP following sensory stimulation of the whisker pad. In the cerebellar flocculus, α7-nAChR pharmacological activation impaired vestibulo-ocular-reflex adaptation, probably because LTP was saturated, preventing the fine adjustment of synaptic weights. These results show that gating mechanisms mediated by specific subtypes of nicotinic receptors are required to control the LTD/LTP balance at the mossy fibre-granule cell relay in order to regulate cerebellar plasticity and behavioural adaptation.

Physiological and Morphological Characterization of Organotypic Cocultures of the Chick Forebrain Area MNH and its Main Input Area DMA/DMP

OpenAIRE

Endepols, Heike; Jungnickel, Julia; Braun, Katharina

2001-01-01

Cocultures of the learning-relevant forebrain region mediorostrai neostriatum and hyperstriatum ventrale (MNH) and its main glutamatergic input area nucleus dorsomedialis anterior thalami/posterior thalami were morphologically and physiologically characterized. Synaptic contacts of thalamic fibers were lightand electron-microscopically detected on MNH neurons by applying the fluorescence tracer DiI-C18(3) into the thalamus part of the coculture. Most thalamic syn...

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

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Hunter, J D; Milton, J G

2001-08-01

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

Synaptic learning rules and sparse coding in a model sensory system.

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Luca A Finelli

2008-04-01

Full Text Available Neural circuits exploit numerous strategies for encoding information. Although the functional significance of individual coding mechanisms has been investigated, ways in which multiple mechanisms interact and integrate are not well understood. The locust olfactory system, in which dense, transiently synchronized spike trains across ensembles of antenna lobe (AL neurons are transformed into a sparse representation in the mushroom body (MB; a region associated with memory, provides a well-studied preparation for investigating the interaction of multiple coding mechanisms. Recordings made in vivo from the insect MB demonstrated highly specific responses to odors in Kenyon cells (KCs. Typically, only a few KCs from the recorded population of neurons responded reliably when a specific odor was presented. Different odors induced responses in different KCs. Here, we explored with a biologically plausible model the possibility that a form of plasticity may control and tune synaptic weights of inputs to the mushroom body to ensure the specificity of KCs' responses to familiar or meaningful odors. We found that plasticity at the synapses between the AL and the MB efficiently regulated the delicate tuning necessary to selectively filter the intense AL oscillatory output and condense it to a sparse representation in the MB. Activity-dependent plasticity drove the observed specificity, reliability, and expected persistence of odor representations, suggesting a role for plasticity in information processing and making a testable prediction about synaptic plasticity at AL-MB synapses.

Synaptic Remodeling Generates Synchronous Oscillations in the Degenerated Outer Mouse Retina

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Wadood eHaq

2014-09-01

Full Text Available During neuronal degenerative diseases, neuronal microcircuits undergo severe structural alterations, leading to remodeling of synaptic connectivity. The functional consequences of such remodeling are mostly unknown. For instance, in mutant rd1 mouse retina, a common model for Retinitis Pigmentosa, rod bipolar cells (RBCs establish contacts with remnant cone photoreceptors (cones as a consequence of rod photoreceptor cell death and the resulting lack of presynaptic input. To assess the functional connectivity in the remodeled, light-insensitive outer rd1 retina, we recorded spontaneous population activity in retinal wholemounts using Ca2+ imaging and identified the participating cell types. Focusing on cones, RBCs and horizontal cells (HCs, we found that these cell types display spontaneous oscillatory activity and form synchronously active clusters. Overall activity was modulated by GABAergic inhibition from HCs. Many of the activity clusters comprised both cones and RBCs. Opposite to what is expected from the intact (wild-type cone-ON bipolar cell pathway, cone and RBC activity was positively correlated and, at least partially, mediated by glutamate transporters expressed on RBCs. Deletion of gap junctional coupling between cones reduced the number of clusters, indicating that electrical cone coupling plays a crucial role for generating the observed synchronized oscillations. In conclusion, degeneration-induced synaptic remodeling of the rd1 retina results in a complex self-sustained outer retinal oscillatory network, that complements (and potentially modulates the recently described inner retinal oscillatory network consisting of amacrine, bipolar and ganglion cells.

Synaptic theory of Replicator-like melioration

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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.

A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS

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Mehrez, Loujaine [University of Southern California; Ghanem, Roger [University of Southern California; McAuliffe, Colin [Altair Engineering, Inc.; Aitharaju, Venkat [General Motors; Rodgers, William [General Motors

2016-06-06

multiscale framework to construct stochastic macroscopic constitutive material models is proposed. A spectral projection approach, specifically polynomial chaos expansion, has been used to construct explicit functional relationships between the homogenized properties and input parameters from finer scales. A homogenization engine embedded in Multiscale Designer, software for composite materials, has been used for the upscaling process. The framework is demonstrated using non-crimp fabric composite materials by constructing probabilistic models of the homogenized properties of a non-crimp fabric laminate in terms of the input parameters together with the homogenized properties from finer scales.

Schaffer collateral inputs to CA1 excitatory and inhibitory neurons follow different connectivity rules.

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Kwon, Osung; Feng, Linqing; Druckmann, Shaul; Kim, Jinhyun

2018-05-04

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3-CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' Rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks. SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow

Ex vivo dissection of optogenetically activated mPFC and hippocampal inputs to neurons in the basolateral amygdala: implications for fear and emotional memory

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Cora eHübner

2014-03-01

Full Text Available Many lines of evidence suggest that a reciprocally interconnected network comprising the amygdala, ventral hippocampus (vHC, and medial prefrontal cortex (mPFC participates in different aspects of the acquisition and extinction of conditioned fear responses and fear behavior. This could at least in part be mediated by direct connections from mPFC or vHC to amygdala to control amygdala activity and output. However, currently the interactions between mPFC and vHC afferents and their specific targets in the amygdala are still poorly understood. Here, we use an ex-vivo optogenetic approach to dissect synaptic properties of inputs from mPFC and vHC to defined neuronal populations in the basal amygdala (BA, the area that we identify as a major target of these projections. We find that BA principal neurons (PNs and local BA interneurons (INs receive monosynaptic excitatory inputs from mPFC and vHC. In addition, both these inputs also recruit GABAergic feedforward inhibition in a substantial fraction of PNs, in some neurons this also comprises a slow GABAB-component. Amongst the innervated PNs we identify neurons that project back to subregions of the mPFC, indicating a loop between neurons in mPFC and BA, and a pathway from vHC to mPFC via BA. Interestingly, mPFC inputs also recruit feedforward inhibition in a fraction of INs, suggesting that these inputs can activate dis-inhibitory circuits in the BA. A general feature of both mPFC and vHC inputs to local INs is that excitatory inputs display faster rise and decay kinetics than in PNs, which would enable temporally precise signaling. However, mPFC and vHC inputs to both PNs and INs differ in their presynaptic release properties, in that vHC inputs are more depressing. In summary, our data describe novel wiring, and features of synaptic connections from mPFC and vHC to amygdala that could help to interpret functions of these interconnected brain areas at the network level.

Ex vivo dissection of optogenetically activated mPFC and hippocampal inputs to neurons in the basolateral amygdala: implications for fear and emotional memory

Science.gov (United States)

Hübner, Cora; Bosch, Daniel; Gall, Andrea; Lüthi, Andreas; Ehrlich, Ingrid

2014-01-01

Many lines of evidence suggest that a reciprocally interconnected network comprising the amygdala, ventral hippocampus (vHC), and medial prefrontal cortex (mPFC) participates in different aspects of the acquisition and extinction of conditioned fear responses and fear behavior. This could at least in part be mediated by direct connections from mPFC or vHC to amygdala to control amygdala activity and output. However, currently the interactions between mPFC and vHC afferents and their specific targets in the amygdala are still poorly understood. Here, we use an ex-vivo optogenetic approach to dissect synaptic properties of inputs from mPFC and vHC to defined neuronal populations in the basal amygdala (BA), the area that we identify as a major target of these projections. We find that BA principal neurons (PNs) and local BA interneurons (INs) receive monosynaptic excitatory inputs from mPFC and vHC. In addition, both these inputs also recruit GABAergic feedforward inhibition in a substantial fraction of PNs, in some neurons this also comprises a slow GABAB-component. Amongst the innervated PNs we identify neurons that project back to subregions of the mPFC, indicating a loop between neurons in mPFC and BA, and a pathway from vHC to mPFC via BA. Interestingly, mPFC inputs also recruit feedforward inhibition in a fraction of INs, suggesting that these inputs can activate dis-inhibitory circuits in the BA. A general feature of both mPFC and vHC inputs to local INs is that excitatory inputs display faster rise and decay kinetics than in PNs, which would enable temporally precise signaling. However, mPFC and vHC inputs to both PNs and INs differ in their presynaptic release properties, in that vHC inputs are more depressing. In summary, our data describe novel wiring, and features of synaptic connections from mPFC and vHC to amygdala that could help to interpret functions of these interconnected brain areas at the network level. PMID:24634648

Factors Influencing Short-term Synaptic Plasticity in the Avian Cochlear Nucleus Magnocellularis

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Jason Tait Sanchez Quinones

2015-01-01

Full Text Available Defined as reduced neural responses during high rates of activity, synaptic depression is a form of short-term plasticity important for the temporal filtering of sound. In the avian cochlear nucleus magnocellularis (NM, an auditory brainstem structure, mechanisms regulating short-term synaptic depression include pre-, post-, and extrasynaptic factors. Using varied paired-pulse stimulus intervals, we found that the time course of synaptic depression lasts up to four seconds at late-developing NM synapses. Synaptic depression was largely reliant on exogenous Ca 2+ -dependent probability of presynaptic neurotransmitter release, and to a lesser extent, on the desensitization of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptor (AMPA-R. Interestingly, although extrasynaptic glutamate clearance did not play a significant role in regulating synaptic depression, blocking glutamate clearance at early-developing synapses altered synaptic dynamics, changing responses from depression to facilitation. These results suggest a developmental shift in the relative reliance on pre-, post-, and extrasynaptic factors in regulating short-term synaptic plasticity in NM.

Shank synaptic scaffold proteins: keys to understanding the pathogenesis of autism and other synaptic disorders.

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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.

Synaptic vesicle dynamic changes in a model of fragile X.

Science.gov (United States)

Broek, Jantine A C; Lin, Zhanmin; de Gruiter, H Martijn; van 't Spijker, Heleen; Haasdijk, Elize D; Cox, David; Ozcan, Sureyya; van Cappellen, Gert W A; Houtsmuller, Adriaan B; Willemsen, Rob; de Zeeuw, Chris I; Bahn, Sabine

2016-01-01

Fragile X syndrome (FXS) is a single-gene disorder that is the most common heritable cause of intellectual disability and the most frequent monogenic cause of autism spectrum disorders (ASD). FXS is caused by an expansion of trinucleotide repeats in the promoter region of the fragile X mental retardation gene (Fmr1). This leads to a lack of fragile X mental retardation protein (FMRP), which regulates translation of a wide range of messenger RNAs (mRNAs). The extent of expression level alterations of synaptic proteins affected by FMRP loss and their consequences on synaptic dynamics in FXS has not been fully investigated. Here, we used an Fmr1 knockout (KO) mouse model to investigate the molecular mechanisms underlying FXS by monitoring protein expression changes using shotgun label-free liquid-chromatography mass spectrometry (LC-MS(E)) in brain tissue and synaptosome fractions. FXS-associated candidate proteins were validated using selected reaction monitoring (SRM) in synaptosome fractions for targeted protein quantification. Furthermore, functional alterations in synaptic release and dynamics were evaluated using live-cell imaging, and interpretation of synaptic dynamics differences was investigated using electron microscopy. Key findings relate to altered levels of proteins involved in GABA-signalling, especially in the cerebellum. Further exploration using microscopy studies found reduced synaptic vesicle unloading of hippocampal neurons and increased vesicle unloading in cerebellar neurons, which suggests a general decrease of synaptic transmission. Our findings suggest that FMRP is a regulator of synaptic vesicle dynamics, which supports the role of FMRP in presynaptic functions. Taken together, these studies provide novel insights into the molecular changes associated with FXS.

Modulation of extrasynaptic NMDA receptors by synaptic and tonic zinc.

Science.gov (United States)

Anderson, Charles T; Radford, Robert J; Zastrow, Melissa L; Zhang, Daniel Y; Apfel, Ulf-Peter; Lippard, Stephen J; Tzounopoulos, Thanos

2015-05-19

Many excitatory synapses contain high levels of mobile zinc within glutamatergic vesicles. Although synaptic zinc and glutamate are coreleased, it is controversial whether zinc diffuses away from the release site or whether it remains bound to presynaptic membranes or proteins after its release. To study zinc transmission and quantify zinc levels, we required a high-affinity rapid zinc chelator as well as an extracellular ratiometric fluorescent zinc sensor. We demonstrate that tricine, considered a preferred chelator for studying the role of synaptic zinc, is unable to efficiently prevent zinc from binding low-nanomolar zinc-binding sites, such as the high-affinity zinc-binding site found in NMDA receptors (NMDARs). Here, we used ZX1, which has a 1 nM zinc dissociation constant and second-order rate constant for binding zinc that is 200-fold higher than those for tricine and CaEDTA. We find that synaptic zinc is phasically released during action potentials. In response to short trains of presynaptic stimulation, synaptic zinc diffuses beyond the synaptic cleft where it inhibits extrasynaptic NMDARs. During higher rates of presynaptic stimulation, released glutamate activates additional extrasynaptic NMDARs that are not reached by synaptically released zinc, but which are inhibited by ambient, tonic levels of nonsynaptic zinc. By performing a ratiometric evaluation of extracellular zinc levels in the dorsal cochlear nucleus, we determined the tonic zinc levels to be low nanomolar. These results demonstrate a physiological role for endogenous synaptic as well as tonic zinc in inhibiting extrasynaptic NMDARs and thereby fine tuning neuronal excitability and signaling.

Modulation of extrasynaptic NMDA receptors by synaptic and tonic zinc

Science.gov (United States)

Anderson, Charles T.; Radford, Robert J.; Zastrow, Melissa L.; Zhang, Daniel Y.; Apfel, Ulf-Peter; Lippard, Stephen J.; Tzounopoulos, Thanos

2015-01-01

Many excitatory synapses contain high levels of mobile zinc within glutamatergic vesicles. Although synaptic zinc and glutamate are coreleased, it is controversial whether zinc diffuses away from the release site or whether it remains bound to presynaptic membranes or proteins after its release. To study zinc transmission and quantify zinc levels, we required a high-affinity rapid zinc chelator as well as an extracellular ratiometric fluorescent zinc sensor. We demonstrate that tricine, considered a preferred chelator for studying the role of synaptic zinc, is unable to efficiently prevent zinc from binding low-nanomolar zinc-binding sites, such as the high-affinity zinc-binding site found in NMDA receptors (NMDARs). Here, we used ZX1, which has a 1 nM zinc dissociation constant and second-order rate constant for binding zinc that is 200-fold higher than those for tricine and CaEDTA. We find that synaptic zinc is phasically released during action potentials. In response to short trains of presynaptic stimulation, synaptic zinc diffuses beyond the synaptic cleft where it inhibits extrasynaptic NMDARs. During higher rates of presynaptic stimulation, released glutamate activates additional extrasynaptic NMDARs that are not reached by synaptically released zinc, but which are inhibited by ambient, tonic levels of nonsynaptic zinc. By performing a ratiometric evaluation of extracellular zinc levels in the dorsal cochlear nucleus, we determined the tonic zinc levels to be low nanomolar. These results demonstrate a physiological role for endogenous synaptic as well as tonic zinc in inhibiting extrasynaptic NMDARs and thereby fine tuning neuronal excitability and signaling. PMID:25947151

Preferential distribution of nociceptive input to motoneurons with muscle units in the cranial portion of the upper trapezius muscle.

Science.gov (United States)

Dideriksen, Jakob L; Holobar, Ales; Falla, Deborah

2016-08-01

Pain is associated with changes in the neural drive to muscles. For the upper trapezius muscle, surface electromyography (EMG) recordings have indicated that acute noxious stimulation in either the cranial or the caudal region of the muscle leads to a relative decrease in muscle activity in the cranial region. It is, however, not known if this adaption reflects different recruitment thresholds of the upper trapezius motor units in the cranial and caudal region or a nonuniform nociceptive input to the motor units of both regions. This study investigated these potential mechanisms by direct motor unit identification. Motor unit activity was investigated with high-density surface EMG signals recorded from the upper trapezius muscle of 12 healthy volunteers during baseline, control (intramuscular injection of isotonic saline), and painful (hypertonic saline) conditions. The EMG was decomposed into individual motor unit spike trains. Motor unit discharge rates decreased significantly from control to pain conditions by 4.0 ± 3.6 pulses/s (pps) in the cranial region but not in the caudal region (1.4 ± 2.8 pps; not significant). These changes were compatible with variations in the synaptic input to the motoneurons of the two regions. These adjustments were observed, irrespective of the location of noxious stimulation. These results strongly indicate that the nociceptive synaptic input is distributed in a nonuniform way across regions of the upper trapezius muscle. Copyright © 2016 the American Physiological Society.

Pricing long-dated insurance contracts with stochastic interest rates and stochastic volatility

NARCIS (Netherlands)

van Haastrecht, A.; Lord, R.; Pelsser, A.; Schrager, D.

2009-01-01

We consider the pricing of long-dated insurance contracts under stochastic interest rates and stochastic volatility. In particular, we focus on the valuation of insurance options with long-term equity or foreign exchange exposures. Our modeling framework extends the stochastic volatility model of

Leucine-rich repeat-containing synaptic adhesion molecules as organizers of synaptic specificity and diversity.

Science.gov (United States)

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.

Long-term relationships between cholinergic tone, synchronous bursting and synaptic remodeling.

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Maya Kaufman

Full Text Available Cholinergic neuromodulation plays key roles in the regulation of neuronal excitability, network activity, arousal, and behavior. On longer time scales, cholinergic systems play essential roles in cortical development, maturation, and plasticity. Presumably, these processes are associated with substantial synaptic remodeling, yet to date, long-term relationships between cholinergic tone and synaptic remodeling remain largely unknown. Here we used automated microscopy combined with multielectrode array recordings to study long-term relationships between cholinergic tone, excitatory synapse remodeling, and network activity characteristics in networks of cortical neurons grown on multielectrode array substrates. Experimental elevations of cholinergic tone led to the abrupt suppression of episodic synchronous bursting activity (but not of general activity, followed by a gradual growth of excitatory synapses over hours. Subsequent blockage of cholinergic receptors led to an immediate restoration of synchronous bursting and the gradual reversal of synaptic growth. Neither synaptic growth nor downsizing was governed by multiplicative scaling rules. Instead, these occurred in a subset of synapses, irrespective of initial synaptic size. Synaptic growth seemed to depend on intrinsic network activity, but not on the degree to which bursting was suppressed. Intriguingly, sustained elevations of cholinergic tone were associated with a gradual recovery of synchronous bursting but not with a reversal of synaptic growth. These findings show that cholinergic tone can strongly affect synaptic remodeling and synchronous bursting activity, but do not support a strict coupling between the two. Finally, the reemergence of synchronous bursting in the presence of elevated cholinergic tone indicates that the capacity of cholinergic neuromodulation to indefinitely suppress synchronous bursting might be inherently limited.

Long-term Relationships between Cholinergic Tone, Synchronous Bursting and Synaptic Remodeling

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Kaufman, Maya; Corner, Michael A.; Ziv, Noam E.

2012-01-01

Cholinergic neuromodulation plays key roles in the regulation of neuronal excitability, network activity, arousal, and behavior. On longer time scales, cholinergic systems play essential roles in cortical development, maturation, and plasticity. Presumably, these processes are associated with substantial synaptic remodeling, yet to date, long-term relationships between cholinergic tone and synaptic remodeling remain largely unknown. Here we used automated microscopy combined with multielectrode array recordings to study long-term relationships between cholinergic tone, excitatory synapse remodeling, and network activity characteristics in networks of cortical neurons grown on multielectrode array substrates. Experimental elevations of cholinergic tone led to the abrupt suppression of episodic synchronous bursting activity (but not of general activity), followed by a gradual growth of excitatory synapses over hours. Subsequent blockage of cholinergic receptors led to an immediate restoration of synchronous bursting and the gradual reversal of synaptic growth. Neither synaptic growth nor downsizing was governed by multiplicative scaling rules. Instead, these occurred in a subset of synapses, irrespective of initial synaptic size. Synaptic growth seemed to depend on intrinsic network activity, but not on the degree to which bursting was suppressed. Intriguingly, sustained elevations of cholinergic tone were associated with a gradual recovery of synchronous bursting but not with a reversal of synaptic growth. These findings show that cholinergic tone can strongly affect synaptic remodeling and synchronous bursting activity, but do not support a strict coupling between the two. Finally, the reemergence of synchronous bursting in the presence of elevated cholinergic tone indicates that the capacity of cholinergic neuromodulation to indefinitely suppress synchronous bursting might be inherently limited. PMID:22911726

Long-term relationships between cholinergic tone, synchronous bursting and synaptic remodeling.

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Kaufman, Maya; Corner, Michael A; Ziv, Noam E

2012-01-01

Cholinergic neuromodulation plays key roles in the regulation of neuronal excitability, network activity, arousal, and behavior. On longer time scales, cholinergic systems play essential roles in cortical development, maturation, and plasticity. Presumably, these processes are associated with substantial synaptic remodeling, yet to date, long-term relationships between cholinergic tone and synaptic remodeling remain largely unknown. Here we used automated microscopy combined with multielectrode array recordings to study long-term relationships between cholinergic tone, excitatory synapse remodeling, and network activity characteristics in networks of cortical neurons grown on multielectrode array substrates. Experimental elevations of cholinergic tone led to the abrupt suppression of episodic synchronous bursting activity (but not of general activity), followed by a gradual growth of excitatory synapses over hours. Subsequent blockage of cholinergic receptors led to an immediate restoration of synchronous bursting and the gradual reversal of synaptic growth. Neither synaptic growth nor downsizing was governed by multiplicative scaling rules. Instead, these occurred in a subset of synapses, irrespective of initial synaptic size. Synaptic growth seemed to depend on intrinsic network activity, but not on the degree to which bursting was suppressed. Intriguingly, sustained elevations of cholinergic tone were associated with a gradual recovery of synchronous bursting but not with a reversal of synaptic growth. These findings show that cholinergic tone can strongly affect synaptic remodeling and synchronous bursting activity, but do not support a strict coupling between the two. Finally, the reemergence of synchronous bursting in the presence of elevated cholinergic tone indicates that the capacity of cholinergic neuromodulation to indefinitely suppress synchronous bursting might be inherently limited.

Can we observe open loop transfer functions in a stochastic feedback system ?

International Nuclear Information System (INIS)

Kishida, Kuniharu; Suda, Nobuhide.

1991-01-01

There are two kinds of problems concerning open loop and closed loop transfer functions in a feedback system. One is a problem even in the deterministic case, and the other is in the stochastic case. In the deterministic case it is guaranteed under a necessary and sufficient condition that total sum of degrees of sub-transfer functions coincides to the degree of the total system. In the stochastic case a systematic understanding of a physical state model, a theoretical innovation model and a data-oriented innovation model is indispensable for determination of open loop transfer functions from time series data. Undesirable factors appear in determination of open loop transfer functions, since a transfer function matrix from input noises to output variables has a redundancy factor of diagonal matrix. (author)

Sliding mode control-based linear functional observers for discrete-time stochastic systems

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Singh, Satnesh; Janardhanan, Sivaramakrishnan

2017-11-01

Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.

Internal additive noise effects in stochastic resonance using organic field effect transistor

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Suzuki, Yoshiharu; Asakawa, Naoki [Division of Molecular Science, Graduate School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu, Gunma 376-8515 (Japan); Matsubara, Kiyohiko [KOOROGI LLC, 6-1585-1-B Sakaino-cho, Kiryu, Gunma 376-0002 (Japan)

2016-08-29

Stochastic resonance phenomenon was observed in organic field effect transistor using poly(3-hexylthiophene), which enhances performance of signal transmission with application of noise. The enhancement of correlation coefficient between the input and output signals was low, and the variation of correlation coefficient was not remarkable with respect to the intensity of external noise, which was due to the existence of internal additive noise following the nonlinear threshold response. In other words, internal additive noise plays a positive role on the capability of approximately constant signal transmission regardless of noise intensity, which can be said “homeostatic” behavior or “noise robustness” against external noise. Furthermore, internal additive noise causes emergence of the stochastic resonance effect even on the threshold unit without internal additive noise on which the correlation coefficient usually decreases monotonically.

Drosophila-Cdh1 (Rap/Fzr) a regulatory subunit of APC/C is required for synaptic morphology, synaptic transmission and locomotion.

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Wise, Alexandria; Schatoff, Emma; Flores, Julian; Hua, Shao-Ying; Ueda, Atsushi; Wu, Chun-Fang; Venkatesh, Tadmiri

2013-11-01

The assembly of functional synapses requires the orchestration of the synthesis and degradation of a multitude of proteins. Protein degradation and modification by the conserved ubiquitination pathway has emerged as a key cellular regulatory mechanism during nervous system development and function (Kwabe and Brose, 2011). The anaphase promoting complex/cyclosome (APC/C) is a multi-subunit ubiquitin ligase complex primarily characterized for its role in the regulation of mitosis (Peters, 2002). In recent years, a role for APC/C in nervous system development and function has been rapidly emerging (Stegmuller and Bonni, 2005; Li et al., 2008). In the mammalian central nervous system the activator subunit, APC/C-Cdh1, has been shown to be a regulator of axon growth and dendrite morphogenesis (Konishi et al., 2004). In the Drosophila peripheral nervous system (PNS), APC2, a ligase subunit of the APC/C complex has been shown to regulate synaptic bouton size and activity (van Roessel et al., 2004). To investigate the role of APC/C-Cdh1 at the synapse we examined loss-of-function mutants of Rap/Fzr (Retina aberrant in pattern/Fizzy related), a Drosophila homolog of the mammalian Cdh1 during the development of the larval neuromuscular junction in Drosophila. Our cell biological, ultrastructural, electrophysiological, and behavioral data showed that rap/fzr loss-of-function mutations lead to changes in synaptic structure and function as well as locomotion defects. Data presented here show changes in size and morphology of synaptic boutons, and, muscle tissue organization. Electrophysiological experiments show that loss-of-function mutants exhibit increased frequency of spontaneous miniature synaptic potentials, indicating a higher rate of spontaneous synaptic vesicle fusion events. In addition, larval locomotion and peristaltic movement were also impaired. These findings suggest a role for Drosophila APC/C-Cdh1 mediated ubiquitination in regulating synaptic morphology

Numerical Methods for Stochastic Computations A Spectral Method Approach

CERN Document Server

Xiu, Dongbin

2010-01-01

The first graduate-level textbook to focus on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC). These fast, efficient, and accurate methods are an extension of the classical spectral methods of high-dimensional random spaces. Designed to simulate complex systems subject to random inputs, these methods are widely used in many areas of computer science and engineering. The book introduces polynomial approximation theory and probability theory; describes the basic theory of gPC meth

Statistical theory of synaptic connectivity in the neocortex

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Escobar, Gina

Learning and long-term memory rely on plasticity of neural circuits. In adult cerebral cortex plasticity can be mediated by modulation of existing synapses and structural reorganization of circuits through growth and retraction of dendritic spines. In the first part of this thesis, we describe a theoretical framework for the analysis of spine remodeling plasticity. New synaptic contacts appear in the neuropil where gaps between axonal and dendritic branches can be bridged by dendritic spines. Such sites are termed potential synapses. We derive expressions for the densities of potential synapses in the neuropil. We calculate the ratio of actual to potential synapses, called the connectivity fraction, and use it to find the number of structurally different circuits attainable with spine remodeling. These parameters are calculated in four systems: mouse occipital cortex, rat hippocampal area CA1, monkey primary visual (V1), and human temporal cortex. The neurogeometric results indicate that a dendritic spine can choose among an average of 4-7 potential targets in rodents, while in primates it can choose from 10-20 potential targets. The potential of the neuropil to undergo circuit remodeling is found to be highest in rat CA1 (4.9-6.0 nats/mum 3) and lowest in monkey V1 (0.9-1.0 nats/mum3). We evaluate the lower bound of neuron selectivity in the choice of synaptic partners and find that post-synaptic excitatory neurons in rodents make synaptic contacts with more than 21-30% of pre-synaptic axons encountered with new spine growth. Primate neurons appear to be more selective, making synaptic connections with more than 7-15% of encountered axons. Another plasticity mechanism is included in the second part of this work: long-term potentiation and depression of excitatory synaptic connections. Because synaptic strength is correlated with the size of the synapse, the former can be inferred from the distribution of spine head volumes. To this end we analyze and compare 166

Optogenetic Examination of Prefrontal-Amygdala Synaptic Development.

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Arruda-Carvalho, Maithe; Wu, Wan-Chen; Cummings, Kirstie A; Clem, Roger L

2017-03-15

A brain network comprising the medial prefrontal cortex (mPFC) and amygdala plays important roles in developmentally regulated cognitive and emotional processes. However, very little is known about the maturation of mPFC-amygdala circuitry. We conducted anatomical tracing of mPFC projections and optogenetic interrogation of their synaptic connections with neurons in the basolateral amygdala (BLA) at neonatal to adult developmental stages in mice. Results indicate that mPFC-BLA projections exhibit delayed emergence relative to other mPFC pathways and establish synaptic transmission with BLA excitatory and inhibitory neurons in late infancy, events that coincide with a massive increase in overall synaptic drive. During subsequent adolescence, mPFC-BLA circuits are further modified by excitatory synaptic strengthening as well as a transient surge in feedforward inhibition. The latter was correlated with increased spontaneous inhibitory currents in excitatory neurons, suggesting that mPFC-BLA circuit maturation culminates in a period of exuberant GABAergic transmission. These findings establish a time course for the onset and refinement of mPFC-BLA transmission and point to potential sensitive periods in the development of this critical network. SIGNIFICANCE STATEMENT Human mPFC-amygdala functional connectivity is developmentally regulated and figures prominently in numerous psychiatric disorders with a high incidence of adolescent onset. However, it remains unclear when synaptic connections between these structures emerge or how their properties change with age. Our work establishes developmental windows and cellular substrates for synapse maturation in this pathway involving both excitatory and inhibitory circuits. The engagement of these substrates by early life experience may support the ontogeny of fundamental behaviors but could also lead to inappropriate circuit refinement and psychopathology in adverse situations. Copyright © 2017 the authors 0270-6474/17/372976-10$15.00/0.

Synaptic characteristics with strong analog potentiation, depression, and short-term to long-term memory transition in a Pt/CeO2/Pt crossbar array structure

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Kim, Hyung Jun; Park, Daehoon; Yang, Paul; Beom, Keonwon; Kim, Min Ju; Shin, Chansun; Kang, Chi Jung; Yoon, Tae-Sik

2018-06-01

A crossbar array of Pt/CeO2/Pt memristors exhibited the synaptic characteristics such as analog, reversible, and strong resistance change with a ratio of ∼103, corresponding to wide dynamic range of synaptic weight modulation as potentiation and depression with respect to the voltage polarity. In addition, it presented timing-dependent responses such as paired-pulse facilitation and the short-term to long-term memory transition by increasing amplitude, width, and repetition number of voltage pulse and reducing the interval time between pulses. The memory loss with a time was fitted with a stretched exponential relaxation model, revealing the relation of memory stability with the input stimuli strength. The resistance change was further enhanced but its stability got worse as increasing measurement temperature, indicating that the resistance was changed as a result of voltage- and temperature-dependent electrical charging and discharging to alter the energy barrier for charge transport. These detailed synaptic characteristics demonstrated the potential of crossbar array of Pt/CeO2/Pt memristors as artificial synapses in highly connected neuron-synapse network.

Stochastic thermodynamics

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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

Synaptic vesicle exocytosis in hippocampal synaptosomes correlates directly with total mitochondrial volume

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Ivannikov, Maxim V.; Sugimori, Mutsuyuki; Llinás, Rodolfo R.

2012-01-01

Synaptic plasticity in many regions of the central nervous system leads to the continuous adjustment of synaptic strength, which is essential for learning and memory. In this study, we show by visualizing synaptic vesicle release in mouse hippocampal synaptosomes that presynaptic mitochondria and specifically, their capacities for ATP production are essential determinants of synaptic vesicle exocytosis and its magnitude. Total internal reflection microscopy of FM1-43 loaded hippocampal synaptosomes showed that inhibition of mitochondrial oxidative phosphorylation reduces evoked synaptic release. This reduction was accompanied by a substantial drop in synaptosomal ATP levels. However, cytosolic calcium influx was not affected. Structural characterization of stimulated hippocampal synaptosomes revealed that higher total presynaptic mitochondrial volumes were consistently associated with higher levels of exocytosis. Thus, synaptic vesicle release is linked to the presynaptic ability to regenerate ATP, which itself is a utility of mitochondrial density and activity. PMID:22772899

A dual theory of price and value in a meso-scale economic model with stochastic profit rate

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Greenblatt, R. E.

2014-12-01

The problem of commodity price determination in a market-based, capitalist economy has a long and contentious history. Neoclassical microeconomic theories are based typically on marginal utility assumptions, while classical macroeconomic theories tend to be value-based. In the current work, I study a simplified meso-scale model of a commodity capitalist economy. The production/exchange model is represented by a network whose nodes are firms, workers, capitalists, and markets, and whose directed edges represent physical or monetary flows. A pair of multivariate linear equations with stochastic input parameters represent physical (supply/demand) and monetary (income/expense) balance. The input parameters yield a non-degenerate profit rate distribution across firms. Labor time and price are found to be eigenvector solutions to the respective balance equations. A simple relation is derived relating the expected value of commodity price to commodity labor content. Results of Monte Carlo simulations are consistent with the stochastic price/labor content relation.

Glial processes at the Drosophila larval neuromuscular junction match synaptic growth.

Directory of Open Access Journals (Sweden)

Deidre L Brink

Full Text Available Glia are integral participants in synaptic physiology, remodeling and maturation from blowflies to humans, yet how glial structure is coordinated with synaptic growth is unknown. To investigate the dynamics of glial development at the Drosophila larval neuromuscular junction (NMJ, we developed a live imaging system to establish the relationship between glia, neuronal boutons, and the muscle subsynaptic reticulum. Using this system we observed processes from two classes of peripheral glia present at the NMJ. Processes from the subperineurial glia formed a blood-nerve barrier around the axon proximal to the first bouton. Processes from the perineurial glial extended beyond the end of the blood-nerve barrier into the NMJ where they contacted synapses and extended across non-synaptic muscle. Growth of the glial processes was coordinated with NMJ growth and synaptic activity. Increasing synaptic size through elevated temperature or the highwire mutation increased the extent of glial processes at the NMJ and conversely blocking synaptic activity and size decreased the presence and size of glial processes. We found that elevated temperature was required during embryogenesis in order to increase glial expansion at the nmj. Therefore, in our live imaging system, glial processes at the NMJ are likely indirectly regulated by synaptic changes to ensure the coordinated growth of all components of the tripartite larval NMJ.

Influence of Wilbraham-Gibbs Phenomenon on Digital Stochastic Measurement of EEG Signal Over an Interval

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Sovilj P.

2014-10-01

Full Text Available Measurement methods, based on the approach named Digital Stochastic Measurement, have been introduced, and several prototype and small-series commercial instruments have been developed based on these methods. These methods have been mostly investigated for various types of stationary signals, but also for non-stationary signals. This paper presents, analyzes and discusses digital stochastic measurement of electroencephalography (EEG signal in the time domain, emphasizing the problem of influence of the Wilbraham-Gibbs phenomenon. The increase of measurement error, related to the Wilbraham-Gibbs phenomenon, is found. If the EEG signal is measured and measurement interval is 20 ms wide, the average maximal error relative to the range of input signal is 16.84 %. If the measurement interval is extended to 2s, the average maximal error relative to the range of input signal is significantly lowered - down to 1.37 %. Absolute errors are compared with the error limit recommended by Organisation Internationale de Métrologie Légale (OIML and with the quantization steps of the advanced EEG instruments with 24-bit A/D conversion

Portfolio Optimization with Stochastic Dividends and Stochastic Volatility

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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…

A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnO_x–Al_2O_3 thin film structure

International Nuclear Information System (INIS)

Li, H. K.; Chen, T. P.; Liu, P.; Zhang, Q.; Hu, S. G.; Liu, Y.; Lee, P. S.

2016-01-01

In this work, a synaptic transistor based on the indium gallium zinc oxide (IGZO)–aluminum oxide (Al_2O_3) thin film structure, which uses ultraviolet (UV) light pulses as the pre-synaptic stimulus, has been demonstrated. The synaptic transistor exhibits the behavior of synaptic plasticity like the paired-pulse facilitation. In addition, it also shows the brain's memory behaviors including the transition from short-term memory to long-term memory and the Ebbinghaus forgetting curve. The synapse-like behavior and memory behaviors of the transistor are due to the trapping and detrapping processes of the holes, which are generated by the UV pulses, at the IGZO/Al_2O_3 interface and/or in the Al_2O_3 layer.