Modeling bistable perception with a network of chaotic neurons

When an ambiguous stimulus is observed, our percep- tion undergoes dynamical changes between two states, a situation extensively explored in association with the Necker cube. Such phenomenon refers to bistable per- ception. Here, we present a model neural network composed of forced FitzHugh-Nagumo neurons, im- plemented also experimentally in an electronic circuit. We show, that under a particular coupling configu- ration, the neural network exhibit bistability between two configurations of clusters. Each cluster composed of two neurons undergoes independent chaotic spiking dynamics. As an appropriate external perturbation is applied to the system, the network undergoes changes in the clusters configuration, involving different neu- rons at each time. We hypothesize that the winning cluster of neurons, responsible for perception, is that exhibiting higher mean frequency. The clusters fea- tures may contribute to an increase of local field po- tential in the neural network.