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Optimal learning rules for familiarity detection

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.

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.

Familiarity; Learning; Memory

Subject headings
Memory Recognition (Psychology); Memory Recollection (Psychology); Meaning (Psychology)

AuthorsGreve Andrea, Sterratt David C, Donaldson David, Willshaw David J, van Rossum Mark C W
Publication date01/2009
ISSN 0340-1200

Biological Cybernetics: Volume 100, Issue 1 (2009-01)

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