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

ID: 548505.0, MPI für biologische Kybernetik / Biologische Kybernetik
A joint maximum-entropy model for binary neural population patterns and continuous signals
Authors:Gerwinn, S.; Berens, P.; Bethge, M.
Editors:Bengio, Y.; Schuurmans, D.; Lafferty, J.; Williams, C.; Culotta, A.
Date of Publication (YYYY-MM-DD):2010-04
Title of Proceedings:Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009
Start Page:620
End Page:628
Physical Description:9
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains. Here, we extend this approach to
take continuous stimuli into account as well. By constraining on the joint secondorder
statistics, we obtain a joint Gaussian-Boltzmann distribution of continuous
stimuli and binary neural firing patterns, for which we also compute marginal and
conditional distributions. This model has the same computational complexity as
pure binary models and fitting it to data is a convex problem. We show that the
model can be seen as an extension to the classical spike-triggered average and can
be used as a non-linear method for extracting features which a neural population
is sensitive to. Further, by calculating the posterior distribution of stimuli given
an observed neural response, the model can be used to decode stimuli and yields
a natural spike-train metric. Therefore, extending the framework of maximumentropy
models to continuous variables allows us to gain novel insights into the
relationship between the firing patterns of neural ensembles and the stimuli they
are processing.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/NWG Bethge
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