Bistable perception modeled as competing stochastic integrations at two levels

• Published in: PLoS computational biology Vol. 5; no. 7; p. e1000430
• Main Authors: Gigante, Guido, Mattia, Maurizio, Braun, Jochen, Del Giudice, Paolo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2009; Public Library of Science (PLoS)
• Subjects: Algorithms; Competition; Computational Biology/Computational Neuroscience; Decision-making; Models, Neurological; Neurons; Neuroscience/Behavioral Neuroscience; Neuroscience/Cognitive Neuroscience; Neuroscience/Psychology; Neuroscience/Theoretical Neuroscience; Neurosciences; Perception - physiology; Photic Stimulation; Physics/Interdisciplinary Physics; Physiological aspects; Psychological aspects; Stochastic Processes; Studies; Time Factors; Vision, Binocular - physiology; Visual perception; Italy; Algorithms; Stochastic Processes; Time Factors; Vision, Binocular; Perception; Models, Neurological; Photic Stimulation
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000430

We propose a novel explanation for bistable perception, namely, the collective dynamics of multiple neural populations that are individually meta-stable. Distributed representations of sensory input and of perceptual state build gradually through noise-driven transitions in these populations, until the competition between alternative representations is resolved by a threshold mechanism. The perpetual repetition of this collective race to threshold renders perception bistable. This collective dynamics - which is largely uncoupled from the time-scales that govern individual populations or neurons - explains many hitherto puzzling observations about bistable perception: the wide range of mean alternation rates exhibited by bistable phenomena, the consistent variability of successive dominance periods, and the stabilizing effect of past perceptual states. It also predicts a number of previously unsuspected relationships between observable quantities characterizing bistable perception. We conclude that bistable perception reflects the collective nature of neural decision making rather than properties of individual populations or neurons.