title: Computational principles and models of multisensory integration.
abstract: Combining information from multiple senses creates robust percepts, speeds up responses, enhances learning, and improves detection, discrimination, and recognition. In this review, I discuss computational models and principles that provide insight into how this process of multisensory integration occurs at the behavioral and neural level. My initial focus is on drift-diffusion and Bayesian models that can predict behavior in multisensory contexts. I then highlight how recent neurophysiological and perturbation experiments provide evidence for a distributed redundant network for multisensory integration. I also emphasize studies which show that task-relevant variables in multisensory contexts are distributed in heterogeneous neural populations. Finally, I describe dimensionality reduction methods and recurrent neural network models that may help decipher heterogeneous neural populations involved in multisensory integration.
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. Electronic address: mouli@stanford.edu.
authors.forename: Chandramouli
authors.initials: C
authors.lastname: Chandrasekaran
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007858:Learning / Q000502:physiology; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology; D012677:Sensation / Q000502:physiology*