Neural Networks with Dynamic Synapses

Tsodyks, Misha; Pawelzik, Klaus; Markram, Henry

doi:10.1162/089976698300017502

Cited by 828 publications

(1,019 citation statements)

References 11 publications

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“…In Tsodyks et al (1998) the authors describe the short-term behavior of synapses with two coupled differential equations: the first describes the availability of vesicles and the second the probability of releasing the vesicles into the synaptic cleft. The arrival of spikes at the synapse leads to a calcium influx into the presynaptic terminal and, thus, to an increase in the release probability (synaptic facilitation).…”

Section: Short-term Plasticitymentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Short-term Plasticitymentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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“…The quantities D and F are scaled so that their resting values are equal to unity. This transformation preserves the mathematical structure of the model introduced in Tsodyks et al (1998). Equations (3) and (4) introduce the recovery-fromdepression time constant τ D , the facilitation time constant τ F and the incremental increase in transmitter release probability u on the arrival of a pre-synaptic spike.…”

Section: Fitting the Synaptic Dynamicsmentioning

confidence: 99%

“…These equations are equivalent to those given in Tsodyks et al (1998) and it should be noted that, as in their derivation, correlations between D and F have been neglected.…”

Section: Model Of the Pre-synaptic Drivementioning

confidence: 99%

“…The history-dependence of the synaptic transfer function (Thomson and Deuchars, 1994) can alter the efficacy of connections between subpopulations, dynamically re-wiring networks over timescales of tens of milliseconds. A number of phenomenological models (Tsodyks and Markram, 1997;Abbott et al, 1997;Tsodyks et al, 1998) have been developed that can be used to classify the dynamics of synapses as being: either depressing, reacting with decreasing strength to a closely spaced train of inputs; or facilitating, reacting to the same train with increasing strength. These depressing and facilitating effects are non-negligible-the post-synaptic potential can be as much as a factor of ten weaker or stronger after a short train of pre-synaptic spikes.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Synaptic Plasticity Orchestrates the Response of Pyramidal Cells and Interneurons to Population Bursts

et al. 2005

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Abstract. The synaptic drive from neuronal populations varies considerably over short time scales. Such changes in the pre-synaptic rate trigger many temporal processes absent under steady-state conditions. This paper examines the differential impact of pyramidal cell population bursts on post-synaptic pyramidal cells receiving depressing synapses, and on a class of interneuron that receives facilitating synapses. In experiment a significant shift of the order of one hundred milliseconds is seen between the response of these two cell classes to the same population burst. It is demonstrated here that such a temporal differentiation of the response can be explained by the synaptic and membrane properties without recourse to elaborate cortical wiring schemes. Experimental data is first used to construct models of the two types of dynamic synaptic response. A population-based approach is then followed to examine analytically the temporal synaptic filtering effects of the population burst for the two post-synaptic targets. The peak-to-peak delays seen in experiment can be captured by the model for experimentally realistic parameter ranges. It is further shown that the temporal separation of the response is communicated in the outgoing action potentials of the two post-synaptic cells: pyramidal cells fire at the beginning of the burst and the class of * To whom correspondence should be addressed. 324 Richardson et al.interneuron receiving facilitating synapses fires at the end of the burst. The functional role of such delays in the temporal organisation of activity in the cortical microcircuit is discussed.

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“…Many STP models have been developed with distinct scientific aims (Mallart and Martin 1967;Friesen 1975;Magleby and Zengel 1975;Krausz and Friesen 1977;Zengel and Magleby 1982;Yamada and Zucker 1992;Sen et al 1997;Tsodyks and Markram 1997;Varela et al 1997;Tsodyks et al 1998;Hunter and Milton 2001). In one approach, the model is built to explain the physiological mechanisms underlying synaptic transmission.…”

Section: Introductionmentioning

confidence: 99%

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

2008

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Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input-output data. Results show that the non-parametric models accurately and efficiently replicate the input-output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP.

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Neural Networks with Dynamic Synapses

Cited by 828 publications

References 11 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Short-Term Synaptic Plasticity Orchestrates the Response of Pyramidal Cells and Interneurons to Population Bursts

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

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