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Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.

Abed Ghanbari1, Aleksey Malyshev2, Maxim Volgushev3

  • 1Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, United States of America.

Plos Computational Biology
|September 6, 2017
PubMed
Summary
This summary is machine-generated.

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Researchers developed novel computational models to estimate synaptic weights and short-term plasticity (STP) using only neural spike data. These methods accurately reconstruct dynamic synaptic changes, advancing our understanding of neural information processing.

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Synaptic Plasticity

Background:

  • Short-term synaptic plasticity (STP) dynamically modulates neuronal communication on millisecond to second timescales.
  • Traditional methods for studying STP rely on intracellular recordings, limiting in vivo applications.
  • STP influences postsynaptic spike train statistics, offering an alternative data source.

Purpose of the Study:

  • To develop and validate model-based approaches for estimating synaptic weights and STP from extracellular spike recordings alone.
  • To enable characterization of synaptic dynamics in complex neural circuits without invasive recordings.

Main Methods:

  • Extended a generalized linear model (GLM) to incorporate time-varying synaptic weights influenced by presynaptic spike history.

Related Experiment Videos

  • Implemented two STP models: one based on Tsodyks-Markram vesicle dynamics, and a second, biophysically unrestrained functional approach.
  • Validated models using simulated pre- and postsynaptic spike trains with known STP dynamics and potential confounding factors.

    • Main Results:

    • Both models accurately reconstructed time-varying synaptic weights from spike data across different STP types.
    • The models successfully captured postsynaptic response differences to presynaptic spikes following short versus long inter-spike intervals.
    • Model performance remained robust despite simulated spike-frequency adaptation, stochastic release, and other noise sources.

    Conclusions:

    • Model-based analysis of spike trains provides a powerful, non-invasive method for quantifying short-term synaptic plasticity.
    • These approaches can advance the study of neural circuit function and information processing in vivo.
    • The developed models offer a valuable tool for analyzing multi-electrode spike recordings to understand synaptic dynamics.