Phase-Oscillator Computations as Neural Models of Stimulus-Response Conditioning and Response Selection

Details Phase-Oscillator Computations as Neural Models of Stimulus-Response Conditioning and Response Selection Phase-Oscillator Computations as Neural Models of Stimulus-Response Conditioning and Response Selection

2010-10-15

arxiv.org/abs/1010.3063v3

Journal of Mathematical Psychology, Volume 56, Issue 2, April 2012, Pages 95-117

Biology

Quantitative Biology

Neurons and Cognition

Scientific paper

The activity of collections of synchronizing neurons can be represented by weakly coupled nonlinear phase oscillators satisfying Kuramoto's equations. In this article, we build such neural-oscillator models, partly based on neurophysiological evidence, to represent approximately the learning behavior predicted and confirmed in three experiments by well-known stochastic learning models of behavioral stimulus-response theory. We use three Kuramoto oscillators to model a continuum of responses, and we provide detailed numerical simulations and analysis of the three-oscillator Kuramoto problem, including an analysis of the stability points for different coupling conditions. We show that the oscillator simulation data are well-matched to the behavioral data of the three experiments.

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