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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/1411

Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Optimal learning rules for familiarity detection
Author(s): Greve, Andrea
Sterratt, David C
Donaldson, David
Willshaw, David J
van, Rossum Mark C W
Contact Email: did1@stir.ac.uk
Keywords: Familiarity
Learning
Memory
Issue Date: Jan-2009
Publisher: Springer
Citation: Greve A, Sterratt DC, Donaldson D, Willshaw DJ & van Rossum MCW (2009) Optimal learning rules for familiarity detection, Biological Cybernetics, 100 (1), pp. 11-19.
Abstract: It has been suggested that the mammalian memory system has both familiarity and recollection components. Recently, a high-capacity network to store familiarity has been proposed. Here we derive analytically the optimal learning rule for such a familiarity memory using a signalto- noise ratio analysis. We find that in the limit of large networks the covariance rule, known to be the optimal local, linear learning rule for pattern association, is also the optimal learning rule for familiarity discrimination. The capacity is independent of the sparseness of the patterns, as long as the patterns have a fixed number of bits set. The corresponding information capacity is 0.057 bits per synapse, less than typically found for associative networks.
Type: Journal Article
URI: http://hdl.handle.net/1893/1411
DOI Link: http://dx.doi.org/10.1007/s00422-008-0275-4
Rights: Published in Biological Cybernetics by Springer.; The original publication is available at www.springerlink.com
Affiliation: University of Edinburgh
University of Edinburgh
Psychology
University of Edinburgh
University of Edinburgh

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