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J Neurophysiol (October 25, 2006). doi:10.1152/jn.00604.2006
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Submitted on June 9, 2006
Accepted on October 20, 2006

Dynamical characteristics common to neuronal competition models

Asya Shpiro1*, Rodica Curtu2, John Rinzel3, and Nava Rubin1

1 Center for Neural Science, New York University, New York, New York, United States
2 Department of Math Analysis and Probabilities, Transilvania University of Brasov, Brasov, Romania
3 Center for Neural Science, New York University, New York, New York, United States; Courant Institute of Mathematical Sciences, New York University, United States

* To whom correspondence should be addressed. E-mail: avs203{at}nyu.edu.

Models implementing neuronal competition via reciprocally inhibitory populations are widely used to characterize bi-stable 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 (e.g. Levelt 1968, "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 non-monotonic dependence of duration on stimulus strength is common to deterministic as well as stochastic models. We conclude that additional experimental tests of Levelt's Proposition IV are needed to reconcile models and perception.




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