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Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models.

Jacob Østergaard1, Mark A Kramer2, Uri T Eden3

  • 1University of Copenhagen, 2100 Copenhagen, Denmark ostergaard@math.ku.dk.

Neural Computation
|October 25, 2017
PubMed
Summary
This summary is machine-generated.

Statistical and dynamical models of neural activity are typically separate. This study simulated Izhikevich neuron data and fit it with a generalized linear model (GLM), revealing how GLMs capture noise but not deterministic randomness.

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Area of Science:

  • Computational neuroscience
  • Mathematical modeling of neural systems

Background:

  • Neural activity is understood using statistical and dynamical models, which are typically applied separately.
  • Understanding the relationship between these modeling approaches is an active research area.

Purpose of the Study:

  • To examine the relationship between statistical and dynamical models of neural activity using simulation.
  • To assess how well a generalized linear model (GLM) captures features of a dynamical model (Izhikevich neuron) under varying noise levels.

Main Methods:

  • Generated spike train data from a simulated Izhikevich neuron model with noisy input current.
  • Fitted the simulated spike train data using a generalized linear model (GLM) incorporating past spiking influences.

Main Results:

  • The generalized linear model (GLM) successfully captured both deterministic features and noise-driven variability from the Izhikevich neuron simulations.
  • For near-deterministic spike trains, goodness-of-fit analyses indicated that the GLM did not statistically fit well, failing to capture the inherent randomness.

Conclusions:

  • The generalized linear model (GLM) captures essential features of simulated neural spike trains.
  • Statistical models like the GLM may not fully represent the underlying dynamics or randomness of neural systems, especially in near-deterministic conditions.