Working memory and spontaneous activity of cell assemblies. A biologically motivated computational model

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 3070-3077

  Molter, Colin ^a   Colliaux, David ^b   Yamaguchi, Yoko ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^b Centre de Recherche en Epistemologie Appliquee Ecole Polytechnique   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE PROCESSING; CELL MEMBRANES; KETONES;

ASSOCIATIVE MEMORIES; CELL ASSEMBLIES; COGNITIVE TASKS; COMPUTATIONAL MODELS; LOCAL FIELD POTENTIALS; MEMBRANE POTENTIALS; PREFRONTAL CORTICES; RECURRENT NETWORKS; SPATIOTEMPORAL PATTERNS; SPONTANEOUS ACTIVITIES; WORKING MEMORIES;

NEURAL NETWORKS;

EID: 56349169007     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4634232     Document Type: Conference Paper

References (37)

1. T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, and A. Arieli. Spontaneously emerging cortical representations of visual attributes. Nature, 425:954-956, 2003.
2. J.N. MacLean, B.O. Watson, G.B. Aaron, and R. Yuste. Internal dynamics determine the cortical response to thalamic stimulation. Neuron, 48:811823, 2005.
3. Vincent JL. Buckner RL. Unrest at test: default activity and spontaneous network correlations. Neuroimage, 37(4):1091-6, 2007.
4. I. Tsuda. Towards an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behavioral and Brain Sciences, 24, 2001.
5. C. Molter, U. Salihoglu, and H. Bersini. The road to chaos by time asymmetric hebbian learning in recurrent neural networks. Neural Computation, 19(1): 100, 2007.
6. D. O. Hebb. The Organization of Behavior; a Neuropsychological Theory. Wiley, New York, 1949.
7. A.D. Baddeley. Working memory. Oxford University Press, New York, 1986.
8. JM. Fuster and GE. Alexander. Neuron activity related to short-term. memory. Science, 173(997):652-4, 1971.
9. JM. Fuster. Unit activity in prefrontal cortex during delayed-response performance: neuronal, correlates of transient memory. J Neurophysiology, 36(1):61-78, 1973.
10. G. Rainer, W.F. Asaad, and E.K. Miller. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature., 393:577-579, 1998.
11. J. D. Cohen, W.M. Perlstein, T.S. Braver, L.E. Nystrom, D.C Noll, Jonides J, and E.E. Smith. Temporal dynamics of brain activation during a working memory task. Nature, 386:604-608, 1997.
12. P.S. Goldinan-Rakic. Models of Information Processing In the Basal Ganglia, pages 131-148. MIT Press, Cambridge, Massachusetts, 1995.
13. Desimone R. Miller EK, Erickson CA. Neural mechanisms of visual working memory in prefrontal, cortex of the macaque. J Neurosci., 16(16):5154-67, 1996.
14. D. Durstewitz, J.K. Seamans, and T.J. Sejnowski, Neurocomputational models of working memory. Nature Neuroscience, 3:1184-1191, 2000.
15. F.G. Ashby, S.W. EII, VV. Valentin, and M.B. Casale. Frost: A distributed neurocomputational model of working memory maintenance. J. Cog. Neurose, 17(11):1728-1743, 2005.
16. J.E. Lisman and M.A. Idiart. Storage of 7±2 short-term memories in oscillatory subcycles. Science, 267:1512-1516, 1995.
17. X. Wu, X. Chen, Z. Li, S. Han, and D.Zhang. Binding of verbal and spatial information in human working memory involves large-scale neural synchronization at theta frequency. Neuroimage, 35(4): 1654-1662, 2007.
18. D. Colliaux, Y. Yamaguchi, C. Molter, and H. Wagatsuma. Working memory dynamics in a flip-flop oscillations network model with milnor attractor. Proceedings of ICONIP, Kyoto, Japan, 2007.
19. Bruno van Swinderen. Attention-like processes in drosophila require short-term memory genes. Science, 315(5818):1590-1593, 2007.
20. IP. Jaaskelainen, J. Ahveninen, JW. Belliveau, T. Raij, and M. Sams. Short-term plasticity in auditory cognition. Trends in Neuroscience, 30(12):653-661, 2007.
21. J. Milnor. On the concept of attractor. Commun. Math. Phys., 99(102):177-195, 1985.
22. K. Kaneko. Dominance of milnor attractors in globally coupled dynamical systems with more than 7±2 degrees of freedom. Physical Review E, 66:055201, 2002.
23. H. Wei, J. Zhang, F. Cousseau, T. Ozeki, and S. Amari. Dynamics of learning near singularities in layered networks. Neural Computation, 20:813-843, 2008.
24. I. Tsuda. Dynamic link of inemorychaotic memory map in nonequilibrium neural, networks. Neural Networks, 5:313326, 1992.
25. K. Kaneko. Pattern dynamics in spatiotemporal chaos. Physica D, 34:14.1, 1992.
26. K. Kaneko and I. Tsuda. Chaotic itinerancy. Chaos: Focus issue on Chaotic Itinerancy, 13(3):926-936, 2003.
27. G. Buzsáki. A two-stage model of memory trace formation: A role for "noisy" brain states. Neuroscience, 31:551-570, 1989.
28. C. Molter, N. Sato, and Y. Yamaguchi. Reactivation of behavioral activity during sharp waves, a computational model for two stage hippocampal dynamics. Hippocampus, 17(3):201-209, 2007.
29. J.J. Hopfield. Neural networks and physical systems with emergent collective computational, abilities. Proc Natl Acad Sci, 79:2554-2558, 1982.
30. C.A. Skarda and W. Freeman. How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences, 10:161-195, 1987.
31. E. Rodriguez, N. George, J.P. Lachaux, B. Renault, J. Martinerie, B. Reunault, and F.J. Varela. Perception's shadow: long-distance synchronization of human brain activity. Nature, 397:430-433, 1999.
32. R. Koztna and W.J. Freeman. Chaotic resonance - methods and applications for robust classification of noisy and variable patterns. Int. J. Bifurcation & Chaos, 11(6):1607-1629, 2001.
33. R. Kozma. On the constructive role of noise in stabilizing itinerant trajectories on chaotic dynamical systems. Chaos, Special Issue on Chaotic Itinerancy, 11(3): 1078-1090, 2003.
34. Y. Hirakura, Y. Yamaguchi, H. Shimizu, and S. Nagai. Dynamic linking among neural, oscillators leads to flexible pattern recognition with figure ground separation. Neural Networks, 9(2):189-209, 1996.
35. C. Molter, U. Salihoglu, and H. Bersini. Neurodynamics of Higher Level Cognition and Consciousness, chapter Giving meaning to cycles to go beyond the limitations of fixed point attractors. Springer Verlag, 2007.
36. A. Browne and R. Sun. Connectionist inference models. Neural Networks, 14(10): 1331-1355, 2001.
37. R. Llineas. I of the Vortex: from neurons to self. MIT press, 2001.