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Short-Term Synaptic Plasticity: Microscopic Modelling and (Some) Computational Implications

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Computational Modelling of the Brain

Part of the book series: Advances in Experimental Medicine and Biology ((CNNCSN,volume 1359))

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Abstract

Synaptic transmission is transiently adjusted on a spike-by-spike basis, with the adjustments persisting from hundreds of milliseconds up to seconds. Such a short-term plasticity has been suggested to significantly augment the computational capabilities of neuronal networks by enhancing their dynamical repertoire. In this chapter, after reviewing the basic physiology of chemical synaptic transmission, we present a general framework—inspired by the quantal model—to build simple, yet quantitatively accurate models of repetitive synaptic transmission. We also discuss different methods to obtain estimates of the model’s parameters from experimental recordings. Next, we show that, indeed, new dynamical regimes appear in the presence of short-term synaptic plasticity. In particular, model neuronal networks exhibit the co-existence of a stable fixed point and a stable limit cycle in the presence of short-term synaptic facilitation. It has been suggested that this dynamical regime is especially relevant in working memory processes. We provide, then, a short summary of the synaptic theory of working memory and discuss some of its specific predictions in the context of experiments. We conclude the chapter with a short outlook.

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Notes

  1. 1.

    Least-squares fitting can be seen as a mapping from the average experimental responses to the parameters. In non-mathematical terms, the mapping is ill conditioned when small changes in the experimental responses result in large changes of the estimate. Practically, this means that the estimate obtained is not reliable. In fact, repeating the experiment would obviously produce slightly different responses that, in turn, would produce vastly different parameter estimates.

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Author information

Authors and Affiliations

  1. Unit of Synapse and Circuit Dynamics, CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France

    Alessandro Barri

  2. Centre National de la Recherche Scientifique, CNRS UMR7210, Paris, France

    Gianluigi Mongillo

  3. Institut de la Vision, Sorbonne Université, Paris, France

    Gianluigi Mongillo

Authors
  1. Alessandro Barri
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  2. Gianluigi Mongillo
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Correspondence to Gianluigi Mongillo .

Editor information

Editors and Affiliations

  1. International School of Advanced Studies, Trieste, Italy

    Michele Giugliano

  2. Erasmus Medical Center, Rotterdam, Zuid-Holland, The Netherlands

    Mario Negrello

  3. Polytechnic University of Milan, Milano, Italy

    Daniele Linaro

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Barri, A., Mongillo, G. (2022). Short-Term Synaptic Plasticity: Microscopic Modelling and (Some) Computational Implications. In: Giugliano, M., Negrello, M., Linaro, D. (eds) Computational Modelling of the Brain. Advances in Experimental Medicine and Biology(), vol 1359. Springer, Cham. https://doi.org/10.1007/978-3-030-89439-9_5

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