C. Diekman, M. Golubitsky and Y. Wang
Derived Patterns in Binocular Rivalry Networks
J. Math. Neuro.
(2013)
Binocular rivalry is the alternation in visual perception that can occur when the two eyes are presented with different images. Wilson (In Cortical mechanisms of vision, Jenkins, M. and Harris, L. (Eds.), Cambridge University Press (2009) 399–417) 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 Kov´acs, Papathomas, Yang, and Feh´er (PNAS 93:15508–15511). 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 motivated by the known structure of the primary visual cortex. The modified network models make expected the surprising outcomes observed in these experiments.