Transformations of sensory information in the brain suggest changing criteria for optimality

by Tyler S. Manning, Emma Alexander, Bruce G. Cumming, Gregory C. DeAngelis, Xin Huang, Emily A. Cooper Neurons throughout the brain modulate their firing rate lawfully in response to sensory input. Theories of neural computation posit that these modulations reflect the outcome of a constrained optimization in which neurons aim to robustly and efficiently represent sensory information. Our understanding of how this optimization varies across different areas in the brain, however, is still in its infancy. Here, we show that neural sensory responses transform along the dorsal stream of the visual system in a manner consistent with a transition from optimizing for information preservation towards optimizing for perceptual discrimination. Focusing on the representation of binocular disparities —the slight differences in the retinal images of the two eyes—we re-analyze measurements characterizing neuronal tuning curves in brain areas V1, V2, and MT (middle temporal) in the macaque monkey. We compare these to measurements of the statistics of binocular disparity typically encountered du ring natural behaviors using a Fisher Information framework. The differences in tuning curve characteristics across areas are consistent with a shift in optimization goals: V1 and V2 population-level responses are more consistent with maximizing the information encoded about naturally occurring bino cular disparities, while MT responses shift towards maximizing the ability to support disparity ...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research