Advertisement

Biological Cybernetics

, Volume 100, Issue 1, pp 11–19 | Cite as

Optimal learning rules for familiarity detection

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

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.

Keywords

Familiarity Hopfield network Computational models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amit D (1989) Modeling brain function: the world of attractor neural networks. Cambridge University Press, CambridgeGoogle Scholar
  2. Bogacz R, Brown MW (2002) The restricted influence of sparseness of coding on the capacity of familiarity discrimination networks. Network 13(4): 457–485PubMedCrossRefGoogle Scholar
  3. Bogacz R, Brown MW (2003) Comparison of computational models of familiarity discrimination in the perirhinal cortex. Hippocampus 13: 494–524PubMedCrossRefGoogle Scholar
  4. Bogacz R, Brown MW, Giraud-Carrier C (2001) Model of familiarity discrimination in the perirhinal cortex. J Comput Neurosc 10: 5–23CrossRefGoogle Scholar
  5. Brunel N (1994) Storage capacity of neural networks: effect of the fluctuations of the number of active neurons per memory. Phys A 27: 4783–4789CrossRefGoogle Scholar
  6. Dayan P, Willshaw DJ (1991) Optimising synaptic learning rules in linear associative memories. Biol Cybern 65: 253–265PubMedCrossRefGoogle Scholar
  7. Fortin NJ, Wright SP, Eichenbaum H (2004) Recollection-like memory retrieval in rats is dependent on the hippocampus. Nature 431: 188–191PubMedCrossRefGoogle Scholar
  8. Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Perseus, ReadingGoogle Scholar
  9. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79: 2554–2558PubMedCrossRefGoogle Scholar
  10. Horner H (1989) Neural networks with low levels of activity: Ising vs. McCulloch-Pitts neurons. Z Physik B Condens Matter 75(1): 133–136CrossRefGoogle Scholar
  11. Kanter I, Sompolinsky H (1987) Associative recall of memory without errors. Phys Rev A 35: 350–392CrossRefGoogle Scholar
  12. Meunier C, Nadal JP (1995) Sparsely coded neural networks. In: Arbib MA (eds) The handbook of Brain theory, 1st edn. MIT press, CambridgeGoogle Scholar
  13. Nadal JP, Toulouse G (1990) Information storage in sparsely coded memory nets. Network 1: 61–74Google Scholar
  14. Tsodyks MV, Feigelman MV (1988) The enhanced storage capacity in neural networks with low activity level. Europhys Lett 6: 101–105CrossRefGoogle Scholar
  15. Willshaw DJ, Buneman OP, Longuet-Higgins HC (1969) Non-holographic associative memory. Nature 222: 960–993PubMedCrossRefGoogle Scholar
  16. Yakovlev V, Amit DJ, Romani S, Hochstein S (2008) Universal memory mechanism for familiarity recognition and identification. J Neurosci 28(1): 239–248PubMedCrossRefGoogle Scholar
  17. Yonelinas AP (2001) Components of episodic memory: the contribution of recollection and familiarity. Proc R Soc B 356: 1363–1374Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Andrea Greve
    • 1
  • David C. Sterratt
    • 2
  • David I. Donaldson
    • 3
  • David J. Willshaw
    • 2
  • Mark C. W. van Rossum
    • 2
  1. 1.Doctoral Training Centre for Neuroinformatics, School of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.Institute for Adaptive and Neural Computation, School of InformaticsUniversity of EdinburghEdinburghUK
  3. 3.School of PsychologyUniversity of StirlingStirlingUK

Personalised recommendations