Reading out population codes with a matched filter
van Rossum, Mark
Turrigiano, Gina G.
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We study the optimal way to decode information present in a population code. Using a matched filter, the performance in Gaussian additive noise is as good as the theoretical maximum. The scheme can be applied when correlations among the neurons in the population are present. We show how the read out of the matched filter can be implemented in a neurophysiological realistic manner. The method seems advantageous for computations in layered networks.