Maoz Shamir, Haim Sompolinsky
Population codes often rely on the tuning of the mean responses to the stimulus parameters. However, this information can be greatly sup- pressed by long range correlations. Here we study the efﬁciency of cod- ing information in the second order statistics of the population responses. We show that the Fisher Information of this system grows linearly with the size of the system. We propose a bilinear readout model for extract- ing information from correlation codes, and evaluate its performance in discrimination and estimation tasks. It is shown that the main source of information in this system is the stimulus dependence of the variances of the single neuron responses.