Representation and propagation of uncertainty information in cortical populations

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 DESCRIPTION (provided by applicant): Understanding the mechanisms used by the brain to represent, and compute with, uncertain information marks a fundamental quest in systems neuroscience. Organisms often make decisions based on observations that are inherently uncertain due to noisy sensors and ambiguity in the world. To optimally perform such tasks, it is necessary for the brain to represent and utilize knowledge of sensory uncertainty. Behavioral studies have demonstrated that in certain tasks, humans perform close to optimally, implying that the brain represents and utilize uncertainty on a trial-by-trial basis. The theoretical framework of probabilistic population coding (PPC) postulates that the brain encodes sensory information in the pattern of population activity by representing a "likelihood function" over the stimulus. Although PPCs have been used to construct implementations of several Bayesian computations using neurally plausible operations, there has been no population-level physiological evidence that this is the coding scheme used by the brain. The proposed project combines electrophysiology (multi-neuronal recordings) and computational neuroscience with the goal to elucidate how the brain represents and computes with sensory uncertainty during visual decision-making. The project will use a simple orientation classification task previously designed in our laboratories. Optimal performance on this task requires the observer to utilize sensory uncertainty on trial-by- trial basis, and human and monkeys have been shown to perform near optimally. In Aim 1, population recordings in primary visual cortex will be combined with behavioral measurements to determine whether sensory uncertainty is encoded in this area, specifically, as likelihood functions in accordance to PPC. In Aim 2, simultaneous recordings from prefrontal cortex will be used to test whether there exists functional correlation between V1 and prefrontal cortex due to shared uncertainty information encoding. Taken together, the proposed work will be the most comprehensive test to date of Bayesian computation in perceptual decision- making, with a particular focus on PPC as the leading encoding framework to be tested. Understanding how the brain implements sensory uncertainty promises to usher in a new generation of neural networks, with possible applications to developing neuroprosthetic devices with improved decoding from cortical populations.