Open Access Research

Derived Patterns in Binocular Rivalry Networks

Casey O Diekman1, Martin Golubitsky1* and Yunjiao Wang2

Author Affiliations

1 Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, 43210, USA

2 Department of Mathematics, Texas Southern University, Houston, TX, 77004, USA

For all author emails, please log on.

The Journal of Mathematical Neuroscience 2013, 3:6  doi:10.1186/2190-8567-3-6

Published: 8 May 2013

Abstract

Binocular rivalry is the alternation in visual perception that can occur when the two eyes are presented with different images. Wilson proposed a class of neuronal network models that generalize rivalry to multiple competing patterns. The networks are assumed to have learned several patterns, and rivalry is identified with time periodic states that have periods of dominance of different patterns. Here, we show that these networks can also support patterns that were not learned, which we call derived. This is important because there is evidence for perception of derived patterns in the binocular rivalry experiments of Kovács, Papathomas, Yang, and Fehér. We construct modified Wilson networks for these experiments and use symmetry breaking to make predictions regarding states that a subject might perceive. Specifically, we modify the networks to include lateral coupling, which is inspired by the known structure of the primary visual cortex. The modified network models make expected the surprising outcomes observed in these experiments.

Keywords:
Binocular rivalry; Interocular grouping; Coupled systems; Symmetry; Hopf bifurcation