Representational accuracy of stochastic neural populations

Fisher information is used to analyze the accuracy with which a neural popu lation encodes D stimulus features. It turns out that the form of response variability has a major impact on the encoding capacity and therefore plays an important role in the selection of an appropriate neural model. In part icular, in the presence of baseline firing, the reconstruction error rapidl y increases with D in the case of Poissonian noise but not for additive noi se. The existence of limited-range correlations of the type found in cortic al tissue yields a saturation of the Fisher information content as a functi on of the population size only for an additive noise model. We also show th at random variability in the correlation coefficient within a neural popula tion, as found empirically, considerably improves the average encoding qual ity. Finally, the representational accuracy of populations with inhomogeneo us tuning properties, either with variability in the tuning widths or fragm ented into specialized subpopulations, is superior to the case of identical and radially symmetric tuning curves usually considered in the literature.