Title:
Revealing nonlinear computation by analyzing choices
Revealing nonlinear computation by analyzing choices
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Author(s)
Pitkow, Xaq
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Abstract
The sensory data about most natural task-relevant variables is confounded by task-irrelevant sensory variations, called nuisance variables. To be useful, the sensory signals that encode the relevant variables must be untangled from the nuisance variables through nonlinear recoding transformations, before the brain can use or decode them to drive behaviors. The information to be untangled is represented in the cortex by the activity of many neurons, forming a nonlinear population code. Here we provide a new theory about these nonlinear codes and their relationship to nuisance variables. This theory obeys fundamental mathematical limitations on information content that are inherited from the sensory periphery, producing redundant codes when there are many more cortical neurons than sensory neurons. The theory predicts a simple relationship between fluctuating neural activity and behavioral choices if the brain uses its nonlinear population codes optimally. When primates discriminate between rotations of natural images, neural responses in visual cortex follow this predicted pattern.
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Date Issued
2017-12-06
Extent
66:13 minutes
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Moving Image
Resource Subtype
Lecture