Physiologically inspired neural model for the prototype-referenced encoding of 
faces

Giese, MA; Leopold, DA

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Physiologically inspired neural model for the prototype-referenced encoding of faces

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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.cnsorg.org/assets/docs/CNS_Program_books/cns2004_abstract_book.pdf
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Citation

Giese, M., & Leopold, D. (2004). Physiologically inspired neural model for the prototype-referenced encoding of faces. Poster presented at Thirteenth Annual Computational Neuroscience Meeting (CNS*2004), Baltimore, MD, USA.

Cite as: https://hdl.handle.net/21.11116/0000-0005-62DA-7

Abstract

Due to the lack of electrophysiological data the neural basis of the encoding of faces is largely
unclear. We present a model for the neural encoding of face spaces that is based on new
electrophysiological results. It reproduces important properties of the physiological data and
shows that faces might be encoded exploiting a \em norm-based rather than an \em examplebased
neural representation. This implies that the encoding exploits an internal representation of an average (norm) face that might be derived by averaging over the previous stimulus history.