Optimal Prediction of Moving Sound Source Direction in the Owl

Weston Cox and Brian J Fischer

PLOS Computational Biology, 2015, vol. 11, issue 7, 1-20

Abstract: Capturing nature’s statistical structure in behavioral responses is at the core of the ability to function adaptively in the environment. Bayesian statistical inference describes how sensory and prior information can be combined optimally to guide behavior. An outstanding open question of how neural coding supports Bayesian inference includes how sensory cues are optimally integrated over time. Here we address what neural response properties allow a neural system to perform Bayesian prediction, i.e., predicting where a source will be in the near future given sensory information and prior assumptions. The work here shows that the population vector decoder will perform Bayesian prediction when the receptive fields of the neurons encode the target dynamics with shifting receptive fields. We test the model using the system that underlies sound localization in barn owls. Neurons in the owl’s midbrain show shifting receptive fields for moving sources that are consistent with the predictions of the model. We predict that neural populations can be specialized to represent the statistics of dynamic stimuli to allow for a vector read-out of Bayes-optimal predictions.Author Summary: Many behaviors require predictive movements. Predictive movements are especially important in prey capture where a predator must predict the future location of moving prey. How sensory information is transformed to motor commands for predictive behaviors is an important open question. Bayesian statistical inference provides a framework to define optimal prediction and Bayesian models of the brain have received experimental support. However, it remains unclear how neural systems can perform optimal prediction in time. Here we use a theoretical approach to specify how a population of neurons should respond to a moving stimulus to allow for a Bayesian prediction to be decoded from the neural responses. This provides a novel theoretical framework that predicts properties of neural responses that are observed in auditory and visual systems of multiple species.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004360

DOI: 10.1371/journal.pcbi.1004360

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