A Bayesian model unifies multisensory spatial localization with the physiological properties of the superior colliculus
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Updated: Jul 14, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
Benjamin Rowland1, Terrence Stanford, Barry Stein
1Wake Forest University School of Medicine, Neurobiology and Anatomy, Medical Center Blvd, Winston-Salem, NC 27157, USA. browland@wfubmc.edu
Abstract:
"Multisensory integration" refers to the phenomenon by which information from different senses is integrated in order to interpret and guide responses to external events. Here, we advance two specific hypotheses: (1) the process of multisensory integration in spatial localization is statistically optimal, and (2) the optimality of the processes guiding this localization results from the implementation of Bayes' rule. We explicitly test the predictions of an optimal (Bayesian) model for the behavior of animals trained and tested in a spatial localization task, and find that the model correctly predicts behavioral patterns which are at times counterintuitive. The model also predicts the receptive field properties of superior colliculus neurons that are involved in these behaviors, and sheds new light on the computational responsibilities different circuits have in effecting these behaviors. Thus, the Bayesian model appears to represent not only a yardstick for the optimality of a behavior, but also a descriptor of the underlying neural processes.
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