Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network

Network: Computation in Neural Systems

Volumn 9, Issue 1, 1998, Pages 123-152

  Brunel, Nicolas ^a   Carusi, Francesco ^b   Fusi, Stefano ^b,c  

^a ECOLE NORMALE SUPÉRIEURE   (France)

^b INFN SEZIONE DI ROMA   (Italy)

^c HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; LEARNING; PATTERN RECOGNITION; PHYSIOLOGY; STATISTICS; TIME;

GENERALIZATION, STIMULUS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, VISUAL; PHYSIOLOGY; STOCHASTIC PROCESSES; TIME FACTORS;

EID: 0042255141     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X_9_1_007     Document Type: Article

References (34)

1. Amit D J 1989 Modeling Brain Function (New York: Cambridge University Press)
2. Amit D J and Brunel N 1995 Learning internal representations in an attractor neural network with analogue neurons Network: Comput. Neural Syst. 6 359
3. _1997 Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex Cerebral Cortex 7 237
4. Amit D J, Brunel N and Tsodyks M V 1994 Correlations of cortical Hebbian reverberations: experiment versus theory J. Neurosci. 14 6435
5. Amit D J and Fusi S 1992 Constraints on learning in dynamic synapses Network: Comput. Neural Syst. 3 443
6. _1994 Dynamic learning in neural networks with material synapses Neural Comput. 6 957
7. Amit D J, Fusi S and Yakovlev V 1997 A paradigmatic attractor cell in IT Neural Comput. 9 1101
8. Annunziato M 1995 Hardware implementation of an attractor neural network with integrate-and-fire neurons and stochastic learning Thesis Università di Roma 'La Sapienza' (in Italian)
9. Artola A and Singer W 1993 Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation Trends Neurosci. 16 480
10. Badoni D, Bertazzoni S, Buglioni S, Salina G, Amit D J and Fusi S 1995 Electronic implementation of an analogue neural network with stochastic transitions Network: Comput. Neural Syst. 6 125
11. Bienenstock E, Cooper L and Munro 1982 Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex J. Neurosci. 2 32
12. Bliss TVP and Collingridge G L 1993 A synaptic model of memory: long-term potentiation in the hippocampus Nature 361 31
13. Brunel N 1994 Dynamics of an attractor neural network converting temporal into spatial correlations Network: Comput. Neural Syst. 5 449
14. _Brunel N 1996 Hebbian learning of context in recurrent neural networks Neural Comput. 8 1677
15. Christie B R, Kerr D S and Abraham W C 1994 Flip side of synaptic plasticity: long-term depression mechanisms in the hippocampus Hippocampus 4 127
16. Fusi S 1995 Prototype extraction in material attractor neural networks with stochastic dynamic learning Proc. SPIE 2492 (Proc. SPIE'95, Applications and Science of Artificial Neural Networks, Orlando, FL) ed S K Rogers and D W Ruck, p 1027
17. Fuster J 1995 Memory in the Cerebral Cortex (Cambridge, MA: MIT Press)
18. Hebb D O 1949 The Organization of Behavior: a Neuropsychological Theory (New York: Wiley)
19. Heskes T M and Kappen B 1991 Learning processes in neural networks Phys. Rev. A 44 2718
20. Hopfield J J 1982 Neural networks and systems with emergent selective computational abilities Proc. Natl Acad. Sci. USA 79 2554
21. Marr D 1971 Simple memory: a theory for archicortex Phil. Trans. R. Soc. B 262 21
22. Meunier C and Nadal J P 1995 Sparsely coded neural networks Handbook of Brain Theory and Neural Networks ed M Arbib (Cambridge, MA: MIT Press) p 899
23. Miyashita Y 1988 Neuronal correlate of visual associative long-term memory in the primate temporal cortex Nature 335 817
24. _1993 Inferior temporal cortex: where visual perception meets memory Ann. Rev. Neurosci. 16 245
25. Nadal J P and Toulouse G 1990 Network: Comput. Neural Syst. 1 61
26. Nadal J P, Toulouse G, Changeux J P and Dehaene S 1986 Networks of formal neurons and memory palimpsests Europhys. Lett. 1 535
27. Parisi G 1986 A memory which forgets J. Phys. A: Math. Gen. 19 L617
28. Rolls E T 1990 Principles underlying the representation and storage of information in neuronal networks in the primate hippocampus and cerebral cortex An Introduction to Neural and Electronic Networks ed Zornetzer et al (San Diego: Academic)
29. Sejnowski T J 1977 Storing covariance with nonlinearly interacting neurons J. Math. Biol. 4 303
30. Shinomoto S 1987 Memory maintenance in neural networks J. Phys. A: Math. Gen. 20 L1305
31. Tsodyks M V 1990 Associative memory in neural networks with binary synapses Mod. Phys. Lett. B 4 713
32. Tsodyks M V and Feigel'man M V 1988 The enhanced storage capacity in neural networks with low activity level Europhys. Lett. 46 101
33. Willshaw D, Buneman O P and Longuet-Higgins H 1969 Non-holographic associative memory Nature 222 960
34. Wong KYM, Kahn P E and Sherrington D 1991 A neural network model of working memory exhibiting primacy and recency J. Phys. A: Math. Gen. 24 1119