Nonlinear dynamics of neuronal excitability, oscillations, and coincidence detection

John Rinzel, Gemma Huguet

Research output: Contribution to journalArticle

Abstract

We review some widely studied models and firing dynamics for neuronal systems, both at the single cell and network level, and dynamical systems techniques to study them. In particular, we focus on two topics in mathematical neuroscience that have attracted the attention of mathematicians for decades: single-cell excitability and bursting. We review the mathematical framework for three types of excitability and onset of repetitive firing behavior in single-neuron models and their relation with Hodgkin's classification in 1948 of repetitive firing properties. We discuss the mathematical dissection of bursting oscillations using fast/slow analysis and demonstrate the approach using single-cell and mean-field network models. Finally, we illustrate the properties of Type III excitability in which case repetitive firing for constant or slow inputs is absent. Rather, firing is in response only to rapid enough changes in the stimulus. Our case study involves neuronal computations for sound localization for which neurons in the auditory brain stem perform extraordinarily precise coincidence detection with submillisecond temporal resolution.

Original languageEnglish (US)
Pages (from-to)1464-1494
Number of pages31
JournalCommunications on Pure and Applied Mathematics
Volume66
Issue number9
DOIs
StatePublished - Sep 2013

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

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