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Related Concept Videos

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Updated: Sep 25, 2025

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

Alessandro Barri1, Gianluigi Mongillo2,3

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

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|April 26, 2022
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Summary
This summary is machine-generated.

Short-term synaptic plasticity enhances neuronal network computation by enabling new dynamical regimes. This study models repetitive synaptic transmission and its role in working memory, revealing co-existing stable states crucial for cognitive functions.

Keywords:
Network oscillationsQuantal modelShort-term synaptic plasticitySlow-fast dynamicsWorking memory

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Synaptic transmission is dynamically adjusted on a spike-by-spike basis, influencing neuronal network computations.
  • Short-term synaptic plasticity (STSP) enhances the dynamical repertoire of neuronal networks, suggesting augmented computational capabilities.

Purpose of the Study:

  • To present a quantitative framework for modeling repetitive synaptic transmission inspired by the quantal model.
  • To investigate the emergence of new dynamical regimes in neuronal networks due to STSP.
  • To explore the relevance of these dynamical regimes in working memory processes.

Main Methods:

  • Review of basic chemical synaptic transmission physiology.
  • Development of a general framework for modeling repetitive synaptic transmission based on the quantal model.
  • Discussion of methods for estimating model parameters from experimental recordings.

Main Results:

  • Demonstration that STSP introduces novel dynamical regimes in neuronal networks.
  • Observation of co-existing stable fixed points and stable limit cycles in networks with short-term synaptic facilitation.
  • Connection of these dynamical regimes to potential mechanisms underlying working memory.

Conclusions:

  • Short-term synaptic plasticity significantly expands the computational capacity of neuronal networks.
  • The dynamical regime characterized by co-existing stable states, facilitated by STSP, is a key candidate mechanism for working memory.
  • Further experimental validation of the synaptic theory of working memory is warranted.