Resonant spatiotemporal learning in large random recurrent networks

Biological Cybernetics

Volumn 87, Issue 3, 2002, Pages 185-198

  Daucé, Emmanuel ^a   Quoy, Mathias ^b   Doyon, Bernard ^c  

^a AIX MARSEILLE UNIVERSITY   (France)

^b Centre national de la recherche scientifique   (France)

^c CHU PURPAN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BRAIN; CONDITIONING; FEEDBACK SYSTEM; MOTONEURON; NOTE; PERCEPTION; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE; RECOGNITION; ROBOTICS; SENSORY NERVE CELL;

BRAIN; CONDITIONING (PSYCHOLOGY); FEEDBACK, BIOCHEMICAL; MOTOR NEURONS; NEURAL NETWORKS (COMPUTER); NEURONS, AFFERENT; PERCEPTION; PSYCHOMOTOR PERFORMANCE; RECOGNITION (PSYCHOLOGY); ROBOTICS;

EID: 0036724904     PISSN: 03401200     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00422-002-0315-4     Document Type: Article

References (38)

1. Ables M, Bergman H, Margalit E, Vaadia E (1993) Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J Neurophysiol 70: 1629-1638
2. Amari S (1972) Characteristics of random nets of analog neuron-like elements. IEEE Trans Syst Man Cybern 2: 643-657
3. Barnes CA, Suster MS, Shen J, McNaughton BL (1997) Multi-stability of cognitive maps in the hippocampus of old rats. Nature 388: 272-275
4. Bienenstock E, Geman S (1995) Compositionality in neural networks. In: Arbib M (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, Mass., pp 223-226
5. Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput vis graph image process 37: 54-115
6. Cessac B (1995) Increase in complexity in random neural networks. J Phys I 5: 409-432
7. Crick F (1984) Function of the thalamic reticular complex: the searchlight hypothesis. Proc Natl Acad Sci USA 81: 4586-4590
8. Daucé E, unpublished observation, 2000
9. Daucé E, Quoy M, Cessac B, Doyon B, Samuelides M (1998) Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning. Neural Netw 11: 521-533
10. Daucé E, Moynot O, Pinaud O, Samuelides M (2001) Mean-field theory and synchronization in random recurrent neural networks. Neural Process Lett 14: 115-126
11. Doyon B, Cessac B, Quoy M, Samuelides M (1993) Control of the transition to chaos in neural networks with random connectivity. Int J Bifure Chaos 3: 279-291
12. Elman JL (1990) Finding structure in time. Cogn Sci 14: 179-211
13. Gaussier P, Zrehen S (1995) Perac: A neural architecture to control artificial animals. Robot Auton Syst 16: 291-320
14. Gaussier P, Joulian C, Banquet J, Leprêtre S, Revel A (2000) The visual homing problem: an example of robotics/biology cross fertilization. Robot Auton Syst 30: 155-180
15. Gerstner W, Ritz R, van Hemmen J (1993) A biologically motivated and analytically soluble model of collective oscillations in the cortex. I. theory at weak locking. Biol Cybern 68: 363-374
16. Gray C, Siger W (1989) Simulus specific neural oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci USA 86: 1698-1702
17. Hermann M, Hertz J, Prugel-Bennett A (1995) Analysis of synfire chains. Network 63: 403-414
18. Hertz J, Prugel-Bennett A (1996) Learning synfire chains: turning noise into signal. Int J Neural Syst 7: 445-450
19. Herz A, Sulzer B, Kuhn R, van Hemmen JL (1989) Hebbian learning reconsidered: representation of static and dynamic objects in associative neural nets. Biol Cybern 60: 457-467
20. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Prob Nat Acad Sci USA 79: 2554-2558
21. Hupé J-M, James A, Payne B, Lomber S, Girard P, Bullier J (1998) Cortical feedback improves discrimination between figure and background by V1, V2, and V3 neurons. Nature 394: 784-787
22. Kuniyoshi Y, Berthouze L (1998) Neural learning of embodied interaction dynamics. Neural Netw 11: 1259-1276
23. MacLeod K, Laurent G (1996) Distinct mechanisms for synchronization and temporal patterning of odor-encoding neural assemblies. Science 274: 976-979
24. Malsburg C von der, Schneider W (1986) A neural cocktail-party processor. Biol Cybern 54: 29-40
25. Meunier C, Nadal J-P (1995) Sparsely coded neural networks. In: Arbib M (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, Mass., pp 899-901
26. Moynot O, Samuelides M (2002) Large deviations and mean-field theroy for asymmetric random recurrent neural networks. Probab Theory Relat Fields 123 1: 41-75
27. Revel A, Gaussier P, Banquet J (1999) Taking inspiration from the hippocampus can help solving robotics problems. In: Proceedings of the European Symposium on Artificial Neural Networks, Bruges, Belgium, 21-23 April, pp 357-362
28. Rodriguez E, George N, Lachaux J-P, Martinerie J, Renault B, Varela FJ (1999) Perception's shadow: long-distance synchronization of human brain activity. Nature 397: 430-433
29. Ross WD, Grossberg S, Mingolla E (2000) Visual cortical mechanisms of perceptual grouping: interacting layers, networks, columns, and maps. Neural Netw 13: 571-588
30. Schillen T, König P (1991) Stimulus dependent assembly formation of oscillatory responses: II. desynchronization. Neural Comput 3: 167-178
31. Schöner G, Dose M, Engels C (1995) Dynamics of behavior: theory and applications for autonomous robot architectures. Robot Auton Syst 16: 213-245
32. Sejnowski TJ (1977) Strong covariance with nonlinearly interacting neurons. J Math Biol 4: 303-321
33. Skarda C, Freeman W (1987) How brains make chaos in order to make sense of the world. Behav Brain Sci 10: 161-195
34. Sompolinsky H, Crisanti A, Sommers H (1988) Chaos in random neural networks. Phys Rev Lett 61: 259-262
35. Tani J, Nolfi S (1998) Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. In: Pfeifer R, Blumberg B, Meyer J, Wilson S (eds) From animals to animals: simulation of adaptive behavior. MIT Press, Cambridge Mass., pp 270-279
36. Thorpe SJ, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381: 520-522
37. Varela F, Thompson E, Rosch E (1991) the embodied mind. MIT Press, Cambridge, Mass
38. Williams RJ, Zipser D (1989) A learning algorithm for continually running recurrent neural networks. Neural Comput 1: 270-280