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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents

ID: 419991.0, MPI für biologische Kybernetik / Biologische Kybernetik
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
Authors:Bethge, M.; Berens, P.
Editors:Platt, J. C.; Koller, D.; Singer, Y.; Roweis, S.
Date of Publication (YYYY-MM-DD):2008-09
Title of Proceedings:Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference
Start Page:97
End Page:104
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor scalability to high dimensions. In sensory coding, however, high-dimensional data is ubiquitous. Here, we introduce a new approach using a near-maximum entropy model, that makes this type of analysis feasible for very high-dimensional data---the model parameters can be derived in closed form and sampling is easy. We demonstrate its usefulness by studying a simple neural representation model of natural images. For the first time, we are able to directly compare predictions from a pairwise maximum entropy model not only in small groups of neurons, but also in larger populations of more than thousand units. Our results indicate that in such larger networks interactions exist that are not predicted by pairwise correlations, despite the fact that pairwise correlations explain the lower-dimensional marginal statistics extrem
ely well up to the limit of dimensionality where estimation of the full joint distribution is feasible.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/NWG Bethge
MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
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