Optimal learning rules for familiarity detection.

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 signal- to-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. In the limit of large networks, the capacity is independent of the sparseness of the patterns and the corresponding information capacity is 0.057 bits per synapse, which is somewhat less than typically found for associative networks.

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Greve A,Sterratt DC,Donaldson DI,Willshaw DJ,van Rossum MC

doi

10.1007/s00422-008-0275-4

subject

Has Abstract

pub_date

2009-01-01 00:00:00

pages

11-9

issue

1

eissn

0340-1200

issn

1432-0770

journal_volume

100

pub_type

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