Replica symmetric evaluation of the information transfer in a two-layer network in presence of continuous and discrete stimuli

Abstract

In a previous report we have evaluated analytically the mutual information between the firing rates of N independent units and a set of continuous+discrete stimuli, for finite N and in the limit of large noise. Here, we extend the analysis to the case of two interconnected populations, where input units are linked to output ones via gaussian weights and a threshold linear transfer function. We evaluate the information carried by M output units about continuous +discrete correlates by means of saddle point equations under the assumption of replica symmetry. Within this limitation, we analyze the dependence of the information on the ratio M/N, on the selectivity of the input units and on the level of the output noise. We show analytically, and confirm numerically, that in the limit of a linear transfer function and of a small ratio between output and input noise, the output information approaches asymptotically the information carried in input. Finally, we show that the information loss in output does not depend much on the structure of the stimulus, but mainly on the position of the threshold nonlinearity, and on the ratio between input and output noise.

revised version of cond-mat/0107587, added simulation results and one figure; Phys. Rev. E, in press

References in corpus (4)

• Mutual Information of Population Codes and Distance Measures in Probability Space
• A theoretical model of neuronal population coding of stimuli with both continuous and discrete dimensions
• Attractor Modulation and Proliferation in 1+$\infty$ Dimensional Neural Networks
• A perturbative approach to non-linearities in the information carried by a two layer neural network

Cited by in corpus (3)

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• A replica free evaluation of the neuronal population information with mixed continuous and discrete stimuli: from the linear to the asymptotic regime