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Variational Information Maximization for Population Coding
Type of publication: Idiap-RR
Citation: barber:population:04:85
Number: Idiap-RR-85-2004
Year: 2004
Month: 6
Institution: IDIAP
Address: Rue de Simplon 4, Martigny, CH-1920, Switerland
Note: IDIAP-RR 04-85
Abstract: The goal of neural processing assemblies is varied, and in many cases still rather unclear. However, a possibly reasonable subgoal is that sensory information may be encoded efficiently in a population of neurons. In this context, Mutual Information is a long studied measure of coding efficiency, and many attempts to apply this to {\em population coding} have been made. However, this is a numerically intractable task, and most previous studies redefine the criterion in forms of an approximation to Mutual Information, the Fisher Information being one such well-known approach. Here we describe a principled bound maximisation procedure for Mutual Information learning of population codes in a simple point neural model, and compare it with other approaches.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Barber, David
Added by: [UNK]
Total mark: 0
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