Nature Neuroscience 9, 690 - 696 (2006)
Published online: 16 April 2006; Corrected online: 01 June 2006 | doi:10.1038/nn1691
Optimal representation of sensory information by neural populationsMehrdad Jazayeri
& J Anthony Movshon
Center for Neural Science, 4 Washington Place, Room 809, New York University, New York, New York 10003, USA.
Correspondence should be addressed to J Anthony Movshon movshon@nyu.edu Sensory information is encoded by populations of neurons. The responses of individual neurons are inherently noisy, so the brain must interpret this information as reliably as possible. In most situations, the optimal strategy for decoding the population signal is to compute the likelihoods of the stimuli that are consistent with an observed neural response. But it has not been clear how the brain can directly compute likelihoods. Here we present a simple and biologically plausible model that can realize the likelihood function by computing a weighted sum of sensory neuron responses. The model provides the basis for an optimal decoding of sensory information. It explains a variety of psychophysical observations on detection, discrimination and identification, and it also directly predicts the relative contributions that different sensory neurons make to perceptual judgments.
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