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Abstract:
Population codes often rely on the tuning of the mean
responses to the stimulus parameters. However, this information
can be greatly suppressed by long range correlations. Here we
study the efficiency of coding 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 extracting
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.
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