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Related Experiment Videos

Integrate-and-fire neurons driven by correlated stochastic input.

Emilio Salinas1, Terrence J Sejnowski

  • 1Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1010, USA. esalinas@wfubmc.edu

Neural Computation
|August 20, 2002
PubMed
Summary

Temporal correlations in synaptic inputs significantly impact neuronal firing. Increased input correlation time generally elevates firing rate and spike-train variability, with effects modulated by input mean and variance.

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Area of Science:

  • Computational Neuroscience
  • Neuronal Dynamics

Background:

  • Neurons process information through synaptic inputs, and correlations within these inputs are known to influence neuronal responses.
  • Analytical models incorporating input correlations are complex, limiting understanding of their precise effects on neural output.

Purpose of the Study:

  • To investigate the influence of temporal correlations in synaptic input on the firing characteristics of model neurons.
  • To derive analytical expressions for neuronal firing rate and spike-train variability under correlated input conditions.

Main Methods:

  • Utilized simple integrate-and-fire neuron models driven by a correlated binary input representing total current.
  • Derived analytical expressions for average firing rate and coefficient of variation based on input mean, variance, and correlation time.

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  • Validated analytical results through computer simulations.

    • Main Results:

    • Increased input correlation time generally leads to higher average firing rates and increased spike-train variability (coefficient of variation).
    • The magnitude of these changes is dependent on the relative values of input mean and variance.
    • Firing rate saturates at a finite limit with increasing correlation time, while coefficient of variation typically diverges.

    Conclusions:

    • Temporal correlations in synaptic inputs play a crucial role in shaping both the intensity (firing rate) and variability of neuronal spike trains.
    • The interplay between input correlations, mean, and variance differentially affects firing rate and variability, offering insights into neural coding mechanisms.