Physiologically inspired neural model for the encoding of face spaces

Giese, MA; Leopold, DA

doi:10.1016/j.neucom.2004.10.060

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Physiologically inspired neural model for the encoding of face spaces

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Leopold, DA
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S0925231204004485
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0004-D7B8-A

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.