Optimal integration of independent observations from Poisson sources

Abstract

The optimal integration of information from independent Poisson sources (such as neurons) was analyzed in the context of a two-interval, forced-choice detection task. When the mean count of the Poisson distribution is above 1, the benefit of integration is closely approximated by the predictions based on the square-root law of the Gaussian model. When the mean count falls far below 1, however, the benefit of integration clearly exceeds the predictions based on the square-root law.