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}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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