Optimal learning rules for familiarity detection

Biological Cybernetics

Volumn 100, Issue 1, 2009, Pages 11-19

  Greve, Andrea ^a   Sterratt, David C ^a   Donaldson, David I ^b   Willshaw, David J ^a   Van Rossum, Mark C W ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF STIRLING   (United Kingdom)

Author keywords

Computational models; Familiarity; Hopfield network

Indexed keywords

MAMMALS; OPTICAL COMMUNICATION; OPTIMIZATION; SIGNAL TO NOISE RATIO;

ASSOCIATIVE NETWORKS; COMPUTATIONAL MODELS; FAMILIARITY; HIGH CAPACITIES; HOPFIELD NETWORK; INFORMATION CAPACITIES; LARGE NETWORKS; LEARNING RULES; MEMORY SYSTEMS; PATTERN ASSOCIATIONS; SIGNAL-TO-NOISE RATIOS;

EDUCATION;

MAMMALIA;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; HUMAN; LEARNING; MATHEMATICS; PHYSIOLOGY; RECOGNITION;

HUMANS; LEARNING; MATHEMATICS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); RECOGNITION (PSYCHOLOGY);

EID: 60649104372     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-008-0275-4     Document Type: Article

References (17)

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