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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Agakov, F.V., Barber, D.: Variational Information Maximization and Fisher Information. Technical report, UoE (2004)
Barber, D., Agakov, F.V.: The IM Algorithm: A Variational Approach to Information Maximization. In: NIPS (2003)
Barlow, H.: Unsupervised Learning. Neural Computation 1, 295–311 (1989)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129–1159 (1995)
Brunel, N., Nadal, J.-P.: Mutual Information, Fisher Information and Population Coding. Neural Computation 10, 1731–1757 (1998)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)
Linsker, R.: An Application of the Principle of Maximum Information to Linear Systems. In: NIPS (1989)
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© 2004 Springer-Verlag Berlin Heidelberg
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Agakov, F., Barber, D. (2004). Variational Information Maximization for Neural Coding. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_83
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DOI: https://doi.org/10.1007/978-3-540-30499-9_83
Publisher Name: Springer, Berlin, Heidelberg
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