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Skellam process with resetting: a neural spike train model.

Reza Ramezan1, Paul Marriott2, Shojaeddin Chenouri2

  • 1Department of Mathematics, California State University, Fullerton, 800 N. State College Blvd., Fullerton, CA 92831, U.S.A.

Statistics in Medicine
|September 28, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new Skellam process with resetting for modeling neural spike trains. This biologically plausible model offers a flexible framework for analyzing neural information coding and interspike interval distributions.

Keywords:
ISI distributionmixture of gamma distributionsneural spike trainsrecordsretinal ganglion cellsskellam processskellam process with resetting

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Area of Science:

  • Computational neuroscience
  • Mathematical modeling of neural systems

Background:

  • Neural spike trains are fundamental to information processing in the brain.
  • Existing models may not fully capture the complexity of neural firing patterns.
  • Understanding neural coding requires accurate and flexible statistical models.

Purpose of the Study:

  • To introduce and evaluate a novel Skellam process with resetting for neural spike train analysis.
  • To demonstrate the biological plausibility and flexibility of the proposed model.
  • To analyze the information coding capabilities and interspike interval distributions of the model.

Main Methods:

  • Development of the Skellam process with resetting.
  • Mathematical derivation of the interspike interval distribution as a mixture of gamma distributions.
  • Simulation studies to assess model performance.
  • Analysis of real neural data from retinal ganglion and lateral geniculate nucleus cells.

Main Results:

  • The Skellam process with resetting provides a biologically plausible model for neural spike trains.
  • The model's interspike interval distribution is a mixture of gamma distributions, offering significant flexibility.
  • The model effectively codes information using three potentially time-varying functions.
  • Performance evaluation through simulations and real neural data analysis.

Conclusions:

  • The Skellam process with resetting is a powerful and flexible tool for modeling neural spike trains.
  • This model advances the understanding of neural information coding and statistical properties of neural firing.
  • The model's flexibility allows it to encompass a wide range of existing neural models.