Beyond GLMs: A generative mixture modeling approach to neural system 
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Bethge, M

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Beyond GLMs: A generative mixture modeling approach to neural system identification

Bethge, M. (2013). Beyond GLMs: A generative mixture modeling approach to neural system identification. Talk presented at Columbia University: Workshop on Quantifying Structure in Large Neural Datasets. New York, NY, USA.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0001-58E2-D Version Permalink: https://hdl.handle.net/21.11116/0000-0001-58E3-C

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http://grossmancenter.columbia.edu/workshop-2013.html (Table of contents) Open Access status unknown

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Creators:
Bethge, M^{1, 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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Dates: Date issued: 2013-10-16

Publication Status: Issued

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Title: Columbia University: Workshop on Quantifying Structure in Large Neural Datasets

Place of Event: New York, NY, USA

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Invited: Yes

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