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  Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Gerwinn, S., Macke, J., Seeger, M., & Bethge, M. (2008). Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. In C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 (pp. 529-536). Red Hook, NY, USA: Curran.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C72F-3 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-3800-E
Genre: Konferenzbeitrag

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externe Referenz:
https://papers.nips.cc/paper/3300-bayesian-inference-for-spiking-neuron-models-with-a-sparsity-prior.pdf (Verlagsversion)
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 Urheber:
Gerwinn, S1, 2, 3, Autor           
Macke, J1, 2, 3, Autor           
Seeger, M2, 3, Autor           
Bethge, M1, 3, Autor           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution over all weights. In addition, we use a Laplacian prior to favor sparse solutions. Therefore, stimulus features that do not critically influence neural activity will be assigned zero weights and thus be effectively excluded by the model. This feature selection mechanism facilitates both the interpretation of the neuron model as well as its predictive abilities. The posterior distribution can be used to obtain confidence intervals which makes it possible to assess the statistical significance of the solution. In neural data analysis, the available amount of experimental measurements is often limited whereas the parameter space is large. In such a situation, both regularization by a sparsity prior and uncertainty estimates for the model parameters are essential.
We apply our method to multi-electrode recordings of retinal ganglion cells and use our uncertainty estimate to test the statistical significance of functional couplings between neurons. Furthermore we used the sparsity of the Laplace prior to select those filters from a spike-triggered covariance analysis that are most informative about the neural response.

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Sprache(n):
 Datum: 2008-09
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 4728
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2007-12-03 - 2007-12-06

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Quelle 1

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Titel: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Genre der Quelle: Konferenzband
 Urheber:
Platt, C, Herausgeber
Koller, D, Herausgeber
Singer, Y, Herausgeber
Roweis, ST, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 529 - 536 Identifikator: ISBN: 978-1-605-60352-0