Dynamical characteristics common to neuronal competition models

Asya Shpiro, Rodica Curtu, John Rinzel, Nava Rubin

Research output: Contribution to journalArticle

Abstract

Models implementing neuronal competition by reciprocally inhibitory populations are widely used to characterize bistable phenomena such as binocular rivalry. We find common dynamical behavior in several models of this general type, which differ in their architecture in the form of their gain functions, and in how they implement the slow process that underlies alternating dominance. We focus on examining the effect of the input strength on the rate (and existence) of oscillations. In spite of their differences, all considered models possess similar qualitative features, some of which we report here for the first time. Experimentally, dominance durations have been reported to decrease monotonically with increasing stimulus strength (such as Levelt's "Proposition IV"). The models predict this behavior; however, they also predict that at a lower range of input strength dominance durations increase with increasing stimulus strength. The nonmonotonic dependency of duration on stimulus strength is common to both deterministic and stochastic models. We conclude that additional experimental tests of Levelt's Proposition IV are needed to reconcile models and perception.

Original languageEnglish (US)
Pages (from-to)462-473
Number of pages12
JournalJournal of neurophysiology
Volume97
Issue number1
DOIs
StatePublished - Jan 2007

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology

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