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  Near-Maximum Entropy Models for Binary Neural Representations of Natural Images

Berens, P., & Bethge, M. (2007). Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. Poster presented at Neural Coding, Computation and Dynamics (NCCD 07), Hossegor, France.

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Berens, P1, 2, Author              
Bethge, M1, 2, Author              
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1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2007-09
 Publication Status: Published online
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 Identifiers: BibTex Citekey: 4730
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Title: Neural Coding, Computation and Dynamics (NCCD 07)
Place of Event: Hossegor, France
Start-/End Date: 2007-09-15 - 2007-09-18

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Title: Neural Coding, Computation and Dynamics (NCCD 07)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 16 Identifier: -