Noise-induced alternations in an attractor network model of perceptual bistability

When a stimulus supports two distinct interpretations, perception alternates in an irregular manner between them. What causes the bistable switches remains an open question. Most existing models that switches arise from a slow fatiguing process, such as or synaptic depression. We develop a new, attractor-based in which alternations are induced by noise and are absent it. Our model goes beyond previous energy-based conceptualizations of perceptual bistability by constructing a neurally plausible model that is implemented in both firing rate mean-field and cell-based networks. The model accounts for known properties of perceptual phenomena, most notably the increase in alternation with stimulation strength observed in binocular rivalry. Furthermore, makes a novel prediction about the effect of changing stimulus strength the activity levels of the dominant and suppressed neural populations, prediction that could be tested with functional MRI or electrophysiological recordings. The neural architecture derived from the energy-based readily generalizes to several competing populations, providing a extension for multistability phenomena.