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Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

Richard Naud1, Wulfram Gerstner

  • 1School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne-EPFL, Lausanne, Switzerland.

Plos Computational Biology
|October 12, 2012
PubMed
Summary

This study analyzes neural responses using Peri-Stimulus-Time-Histograms (PSTHs), revealing how neuron refractoriness and adaptation influence temporal coding. A new quasi-renewal equation accurately models firing rates and stimulus decoding in spiking neurons.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Theoretical Neuroscience

Background:

  • Neuronal responses to stimuli, often visualized as Peri-Stimulus-Time-Histograms (PSTHs), contain complex temporal information potentially related to neural coding.
  • Understanding these temporal dynamics is crucial for deciphering how neurons process information, especially considering factors like refractoriness and adaptation.

Purpose of the Study:

  • To analyze the encoding and decoding of PSTHs in spiking neurons with arbitrary refractoriness and adaptation.
  • To develop and validate a mathematical framework for describing neuronal firing rates and stimulus reconstruction from population activity.

Main Methods:

  • Utilized the spike response model (generalized linear neuron model) to capture precise spike timing effects due to refractoriness and adaptation.
  • Derived a 'quasi-renewal equation' to model the firing rate of adapting neurons.
  • Investigated stimulus decoding from population PSTHs, considering varying levels of neuronal activity and adaptation.

Main Results:

  • The quasi-renewal equation provides an excellent description of firing rates for adapting neurons, outperforming other rate equations in certain contexts.
  • Decoding stimuli from population PSTHs depends non-linearly on past activity when refractory effects are significant.
  • Simple accumulator models suffice for decoding under low activity and weak adaptation.

Conclusions:

  • The derived quasi-renewal equation offers a robust model for neuronal firing rates, particularly for neurons exhibiting adaptation.
  • Decoding neural population activity is complex and becomes non-linear with strong refractory periods, impacting information retrieval.
  • Findings are applicable to mean-field analyses of neural networks and general point processes with self-inhibition.