title: 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.
authors.affiliation: Doctoral Training Centre for Neuroinformatics, School of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh, EH1 2QL, UK.
authors.forename: Andrea
authors.initials: A
authors.lastname: Greve
authors.forename: David C
authors.initials: DC
authors.lastname: Sterratt
authors.forename: David I
authors.initials: DI
authors.lastname: Donaldson
authors.forename: David J
authors.initials: DJ
authors.lastname: Willshaw
authors.forename: Mark C W
authors.initials: MC
authors.lastname: van Rossum
mesh_terms: D006801:Humans; D007858:Learning / Q000502:physiology*; D008433:Mathematics; D008959:Models, Neurological; D016571:Neural Networks, Computer; D021641:Recognition, Psychology