Reading population codes: a neural implementation of ideal observers

Many sensory and motor variables are encoded in the nervous system by the a ctivities of large populations of neurons with bell-shaped tuning curves. E xtracting information from these population codes is difficult because of t he 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 approximatio n to ML can be implemented in a biologically plausible model of cortical ci rcuitry. Our results apply to a wide range of nonlinear activation function s, suggesting that cortical areas may, in general, function as ideal observ ers of activity in preceding areas.