Reading population codes: a neural implementation of ideal observers.
Many sensory and motor variables are encoded in the nervous system by the activities of large populations of neurons with bell-shaped tuning curves. Extracting information from these population codes is difficult because of the noise inherent in neuronal responses. In most cases of interest, maximum likelihood (ML) is the best read-out method and would be used by an ideal observer. Using simulations and analysis, we show that a close approximation to ML can be implemented in a biologically plausible model of cortical circuitry. Our results apply to a wide range of nonlinear activation functions, suggesting that cortical areas may, in general, function as ideal observers of activity in preceding areas.
Brain and Cognitive Science Department, University of Rochester, Rochester, New York 14627, USA.
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Deneve
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Latham
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Pouget
D001931:Brain Mapping; D003198:Computer Simulation; D016013:Likelihood Functions; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D016012:Poisson Distribution; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology*