Neural Comput - Efficient sensory encoding and Bayesian inference with heterogeneous neural populations.

Tópicos

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Resumo

The efficient coding hypothesis posits that sensory systems maximize information transmitted to the brain about the environment. We develop a precise and testable form of this hypothesis in the context of encoding a sensory variable with a population of noisy neurons, each characterized by a tuning curve. We parameterize the population with two continuous functions that control the density and amplitude of the tuning curves, assuming that the tuning widths vary inversely with the cell density. This parameterization allows us to solve, in closed form, for the information-maximizing allocation of tuning curves as a function of the prior probability distribution of sensory variables. For the optimal population, the cell density is proportional to the prior, such that more cells with narrower tuning are allocated to encode higher-probability stimuli and that each cell transmits an equal portion of the stimulus probability mass. We also compute the stimulus discrimination capabilities of a perceptual system that relies on this neural representation and find that the best achievable discrimination thresholds are inversely proportional to the sensory prior. We examine how the prior information that is implicitly encoded in the tuning curves of the optimal population may be used for perceptual inference and derive a novel decoder, the Bayesian population vector, that closely approximates a Bayesian least-squares estimator that has explicit access to the prior. Finally, we generalize these results to sigmoidal tuning curves, correlated neural variability, and a broader class of objective functions. These results provide a principled embedding of sensory prior information in neural populations and yield predictions that are readily testable with environmental, physiological, and perceptual data.

Resumo Limpo

effici code hypothesi posit sensori system maxim inform transmit brain environ develop precis testabl form hypothesi context encod sensori variabl popul noisi neuron character tune curv parameter popul two continu function control densiti amplitud tune curv assum tune width vari invers cell densiti parameter allow us solv close form informationmaxim alloc tune curv function prior probabl distribut sensori variabl optim popul cell densiti proport prior cell narrow tune alloc encod higherprob stimuli cell transmit equal portion stimulus probabl mass also comput stimulus discrimin capabl perceptu system reli neural represent find best achiev discrimin threshold invers proport sensori prior examin prior inform implicit encod tune curv optim popul may use perceptu infer deriv novel decod bayesian popul vector close approxim bayesian leastsquar estim explicit access prior final general result sigmoid tune curv correl neural variabl broader class object function result provid principl embed sensori prior inform neural popul yield predict readili testabl environment physiolog perceptu data

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