Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy

R Jolivet*, TJ Lewis, W Gerstner

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [ Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70-80 percent of the spikes in the full model ( with temporal precision of +/-2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons.

Original languageEnglish
Pages (from-to)959-976
Number of pages18
JournalJournal of Neurophysiology
Volume92
Issue number2
DOIs
Publication statusPublished - Aug 2004
Externally publishedYes

Keywords

  • ASYNCHRONOUS STATES
  • CORTICAL-NEURONS
  • NETWORKS
  • RELIABILITY
  • COMPUTATION
  • EXCITATION
  • REDUCTION
  • SYNCHRONY
  • DYNAMICS
  • HODGKIN

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