Submitted on February 2, 2007
Accepted on July 2, 2007
Noise-induced alternations in an attractor network model of perceptual bi-stability
Ruben Moreno Bote1*, John Rinzel2, and Nava Rubin1
1 Center for Neural Science, New York University, New York, New York, United States
2 Dept. of Neural Science & Mathematics, New York University, New York, New York, United States
* To whom correspondence should be addressed. E-mail: rmoreno{at}cns.nyu.edu.
When a stimulus supports two distinct interpretations, perception alternates in an irregular manner between them. What causes the bi-stable perceptual switches remains an open question. Most existing models assume that switches are due to a slow fatiguing process, such as adaptation or synaptic depression. We develop a new, attractor-based framework in which alternations are induced by noise, and are absent without it. Our model goes beyond previous energy-based conceptualizations of perceptual bi-stability by constructing a neurally plausible attractor model that is implemented in both firing rate mean-field and spiking cell-based networks. The model accounts for known properties of bi-stable perceptual phenomena, most notably the increase in alternation rate with stimulation strength observed in binocular rivalry (Levelt 1968). Furthermore, it makes a novel prediction about the effect of changing stimulus strength on the activity levels of the dominant and suppressed neural populations, a prediction that could be tested with fMRI or electrophysiological recordings. The neural architecture derived from the energy-based model readily generalizes to several competing populations, providing a natural extension for multi-stability phenomena.
