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
