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

TL;DR

This paper models stimulus-response learning using coupled Kuramoto phase oscillators, demonstrating how neural synchronization can replicate behavioral learning data through detailed simulations and stability analysis.

Contribution

It introduces neural-oscillator models based on Kuramoto equations to simulate stimulus-response learning, aligning neural dynamics with behavioral experiments.

Findings

Oscillator models match behavioral data from experiments.

Stability analysis reveals conditions for learning responses.

Neural synchronization explains stimulus-response conditioning.

Abstract

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.