Variational Information Maximization for Neural Coding

  • Felix Agakov
  • David Barber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)

Abstract

Mutual Information (MI) is a long studied measure of coding efficiency, and many attempts to apply it to population coding have been made. However, this is a computationally intractable task, and most previous studies redefine the criterion in forms of approximations. Recently we described properties of a simple lower bound on MI [2]. Here we describe the bound optimization procedure for learning of population codes in a simple point neural model. We compare our approach with other techniques maximizing approximations of MI, focusing on a comparison with the Fisher Information criterion.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Felix Agakov
    • 1
  • David Barber
    • 2
  1. 1.University of EdinburghEdinburghUK
  2. 2.IDIAPRue du Simplon 4MartignySwitzerland

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