Optimal learning rules for familiarity detection

  • Authors:

  • Andrea Greve;David C. Sterratt;David I. Donaldson;David J. Willshaw;Mark C. W. van Rossum

  • Affiliations:

  • University of Edinburgh, Doctoral Training Centre for Neuroinformatics, School of Informatics, 5 Forrest Hill, EH1 2QL, Edinburgh, UK;University of Edinburgh, Institute for Adaptive and Neural Computation, School of Informatics, 5 Forrest Hill, EH1 2QL, Edinburgh, UK;University of Stirling, School of Psychology, FK9 4LA, Stirling, UK;University of Edinburgh, Institute for Adaptive and Neural Computation, School of Informatics, 5 Forrest Hill, EH1 2QL, Edinburgh, UK;University of Edinburgh, Institute for Adaptive and Neural Computation, School of Informatics, 5 Forrest Hill, EH1 2QL, Edinburgh, UK

  • Venue:
  • Biological Cybernetics - Volume 100: Half a Century of Biological Cybernetics
  • Year:
  • 2009

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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.