title: Stochastic accumulation by cortical columns may explain the scalar property of multistable perception.
abstract: The timing of certain mental events is thought to reflect random walks performed by underlying neural dynamics. One class of such events--stochastic reversals of multistable perceptions--exhibits a unique scalar property: even though timing densities vary widely, higher moments stay in particular proportions to the mean. We show that stochastic accumulation of activity in a finite number of idealized cortical columns--realizing a generalized Ehrenfest urn model--may explain these observations. Modeling stochastic reversals as the first-passage time of a threshold number of active columns, we obtain higher moments of the first-passage time density. We derive analytical expressions for noninteracting columns and generalize the results to interacting columns in simulations. The scalar property of multistable perception is reproduced by a dynamic regime with a fixed, low threshold, in which the activation of a few additional columns suffices for a reversal.
authors.affiliation: Cognitive Biology, Center for Behavioral Brain Sciences, Otto von Guericke University, 39106 Magdeburg, Germany and Department of Technologies and Health, Istituto Superiore di Sanità, 00161 Roma, Italy.
authors.forename: Robin
authors.initials: R
authors.lastname: Cao
authors.affiliation: Cognitive Biology, Center for Behavioral Brain Sciences, Otto von Guericke University, 39106 Magdeburg, Germany.
authors.forename: Jochen
authors.initials: J
authors.lastname: Braun
authors.affiliation: Department of Technologies and Health, Istituto Superiore di Sanità, 00161 Roma, Italy.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Mattia
mesh_terms: D002540:Cerebral Cortex / Q000502:physiology; D003071:Cognition / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology; D010465:Perception / Q000502:physiology*; D013269:Stochastic Processes