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  Physiologically inspired neural model for the encoding of face spaces

Giese, M., & Leopold, D. (2005). Physiologically inspired neural model for the encoding of face spaces. Neurocomputing, 65-66, 93-101. doi:10.1016/j.neucom.2004.10.060.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-D7B8-A Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D7B9-9
Genre: Journal Article

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Giese, MA, Author              
Leopold, DA1, 2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 Abstract: The neural principles of the encoding of face spaces in visual cortex are still unclear and multiple competing theories have been proposed. Based on new electrophysiological data from macaque area IT we test two models realizing example-based and norm-referenced encoding. Comparing the experimentally measured tuning properties with predictions from the two models we find a better agreement for the norm-referenced encoding model. This suggests that a majority of IT neurons might represent deviations from a norm face, which is determined by an average over the distribution of typically occurring faces.

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 Dates: 2005-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.neucom.2004.10.060
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Title: Neurocomputing
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 65-66 Sequence Number: - Start / End Page: 93 - 101 Identifier: ISSN: 0925-2312
CoNE: https://pure.mpg.de/cone/journals/resource/954925566733