Hostname: page-component-6bf8c574d5-gr6zb Total loading time: 0 Render date: 2025-02-17T17:36:44.009Z Has data issue: false hasContentIssue false
  • English
  • Français

Chaotic itinerancy is a key to mental diversity

Published online by Cambridge University Press:  14 February 2005

Ichiro Tsuda
Ichiro Tsuda*
Affiliation:
Department of Mathematics, Hokkaido University, Sapporo060-0810, Japanhttp://www.math.sci.hokudai.ac.jp/~tsuda/
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract:

Kampis proposes the study of chaotic itinerancy, pointing out its significance in domains of cognitive science and philosophy. He has discovered in the concept of chaotic itinerancy the possibility for a new dynamical approach that elucidates mental states with a physical basis. This approach may therefore provide the means to go beyond the connectionist approach. In accordance with his theory, I here highlight three issues regarding chaotic itinerancy: transitory dynamics, diversity, and self-modifying system.

Type
Author's Response
Information
Behavioral and Brain Sciences , Volume 27 , Issue 4 , August 2004 , pp. 586 - 588
Copyright
Copyright © Cambridge University Press 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Commentary onIchiro Tsuda (2001). Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. BBS 24(5):793–810.

References

Chalmers, D. (1996) Does a rock implement every finite automaton? Synthese 108:309–33. Available at: http://www.u.arizona.edu/∼chalmers/papers/ rock.html. [GK]Google Scholar
Dennett, D. C. (1995) Darwin's dangerous idea. Simon and Schuster. [GK]Google Scholar
Freeman, W. J. (1991) The physiology of perception. Scientific American 264:78–85. Available at: http://sulcus.berkeley.edu/FreemanWWW/manuscripts/IE1/ 91.html. [GK]Google Scholar
Freeman, W. J. (2004) Origin, structure, and role of background EEG activity: Part 1, Analytic amplitude; Part 2, Analytic phase. Clinical Neurophysiology 115:2089–107. [rIT]Google Scholar
Fujii, H., Ito, H., Aihara, K., Ichinose, N. & Tsukada, M. (1996) Dynamical cell assembly hypothesis – Theoretical possibility of spatio-temporal coding in the cortex. Neural Networks 9:1303–50. [rIT]Google Scholar
Gray, C., Engel, A. K., Koenig, P. & Singer, W. (1992) Synchronization of oscillatory neuronal responses in cat striate cortex: Temporal properties. Visual Neuroscience 8:337–47. [rIT]Google Scholar
Haugeland, J. (1995) Mind embodied and embedded. Acta Philosophica Fennica 58:233–67. [GK]Google Scholar
Kampis, G. (1991) Self-modifying systems in biology and cognitive science. Pergamon Press. [rIT]Google Scholar
Kaneko, K. (1998) On the strength of attractors in a high-dimensional system: Milnor attractor network, robust global attraction, and noise-induced selection. Physica D 124:308–30. [GK, rIT]Google Scholar
Kaneko, K. & Tsuda, I. (2001) Complex systems: Chaos and beyond – A constructive approach with applications in life sciences. Springer. [GK, rIT]Google Scholar
Kaneko, K. & Tsuda, I., eds. (2003) Focus issue: Chaotic itinerancy. Chaos 13:926–1164. [rIT]Google Scholar
Lampl, I., Reichova, I. & Ferster, D. (1999) Synchronous membrane potential fluctuations in neurons of the cat visual cortex. Neuron 22:361–74. [rIT]Google Scholar
Port, R. F. (2001) Dynamical systems hypothesis in cognitive science. In: Encyclopedia of cognitive science, ed. Lockyer, Amy. MacMillan. Available at: http://www.cs.indiana.edu/hyplan/port/pap/dynamic.cognition.sglspc.htm. [GK]Google Scholar
Putnam, H. (1988) Representation and reality. MIT Press. [GK]Google Scholar
Rosen, R. (1991) Life itself: A comprehensive inquiry into the nature, origin, and fabrication of life. Columbia University Press. [rIT]Google Scholar
Ryle, G. (1949) The concept of mind. Hutchinson. [GK]Google Scholar
Sauer, T. (2000) Abstracts for SIAM Pacific Rim Dynamical Systems Conference, August 9–13, 2000, Maui, HI, No. 51. Society for Industrial and Applied Mathematics. [rIT]Google Scholar
Sauer, T. (2003) Chaotic itinerancy based on attractors of one-dimensional maps. Chaos 13:947–52. [rIT]Google Scholar
Tsuda, I. (1991) Chaotic itinerancy as a dynamical basis of hermeneutics of brain and mind. World Futures 32:167–85. [rIT]Google Scholar
Tsuda, I. (1992) Dynamic link of memories – Chaotic memory map in nonequilibrium neural networks. Neural Networks 5:313–26. [rIT]Google Scholar
Tsuda, I. (2001) Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behavioral and Brain Sciences 24:793–847. [rIT]Google Scholar
Tsuda, I., Fujii, H., Tadokoro, S., Yasuoka, T. & Yamaguti, Y. (2004) Journal of Integrative Neurosciences 3:159–82. [rIT]Google Scholar
Tsuda, I., Koerner, E. & Shimizu, H. (1987) Memory dynamics in asynchronous neural networks. Progress of Theoretical Physics 78:51–71. [rIT]Google Scholar
Tsuda, I. & Kuroda, S. (2001) Cantor coding in the hippocampus. Japan Journal of Industrial and Applied Mathematics 18:249–58. [rIT]Google Scholar
Tsuda, I. & Umemura, T. (2003) Chaotic itinerancy generated by coupling of Milnor attractors. Chaos 13:926–36. [rIT]Google Scholar
Van Gelder, T. J. (1998) The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences 21:1–14. Available at: http://www.cogsci.soton.ac.uk/bbs/ Archive/bbs.vangelder.html. [GK]Google Scholar
von der Marlsburg, C. (1981) The correlation theory of brain function. Internal Report 81(2). Goettingen, Max Planck Institute for Biophysical Chemistry. [rIT]Google Scholar