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Comparative reduction of theories — or over-simplification?

Published online by Cambridge University Press:  04 February 2010

Edgar Koerner
Edgar Koerner
Affiliation:
Honda R&D Co. Ltd., Wako Research Center, 1-4-1-Chuo Wako-shi, Saitama 351, Japan. edgar.koerner@f.rd.honda.co.jp
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Abstract

To model the organization of levels' of cortical dynamics, at least some general scheme for hierarchy, functional diversity, and proper intrinsic control must be provided. Rhythmic control forces the system to iterate its state by short trajectories, which makes it much more stable and predictable without discarding the desirable ability of chaotic systems to make rapid phase transitions. Rhythmic control provides a fundamentally different systems dynamics, one not provided by models that allow the emergence of continuous trajectories in the systems state space.

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Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 19 , Issue 2 , June 1996 , pp. 301 - 302
Copyright
Copyright © Cambridge University Press 1996

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References

Abarbanel, H. D. I., Brown, R., Sidorowich, J. J. & Tsimring, L. S. (1993) The analysis of observed chaotic data in physical systems. Review of Modem Physics 65: 1331–92. [LI]CrossRefGoogle Scholar
Abeles, M. (1982) Local cortical circuits. In: Studies in brain Junction 6. Springer-Verlag. [RM]Google Scholar
Abeles, M., Prut, Y., Bergman, H. & Vaadia, E. (1994) Synchronization in neuronal transmission and its importance for information processing. In: Temporal coding in the brain, ed. Buzsaki, G., Llinas, R., Singer, W., Berthoz, A. & Cristen, . Springer-Verlag. [ZJK]Google Scholar
Aertsen, A. & Amdt, M. (1993) Response synchronization in the visual cortex. Current Opinion in Neurobiology 3:586–94. [HP]Google Scholar
Agladze, N. N., Zhadin, M. N. & lgnatjev, D. A. (1995) Electrical activity of the rabbit isolated cerebral cortex after application of acetylcholine. Journal of Higher Nervous Activity 45:782–89 (in Russian). [MNZ]Google Scholar
Ahadin, M. N., Bakharev, B. V & Muravjova, L. I. (1977) Electrophysiological correlates of habituation and conditioning. Journal of Higher Nervous Activity 27:1173–79 (in Russian). [MNZ]Google Scholar
Alkon, D., Sanchez-Andres, J.-V, Ito, E. & Oka, K. (1992) Long-term transformation of an inhibitory into an excitatory GABAergic synaptic response. Proceedings of the National Academy of Sciences 89(24):11862–66. [HRE]Google Scholar
Amari, S. (1972) Learning patterns and pattern sequences by self-organising nets of threshold elements. IEEE Transactions on Computers 21:1197–1206. [aJJW]Google Scholar
Amit, D. J. (1990) Modelling brain function: The world of attractor neural networks. Cambridge University Press. [aJJW]Google Scholar
Amit, D. J. (1995) The Hebbian paradigm reintegrated. Behavioral and Brain Sciences 18:681. [DJA]Google Scholar
Amit, D. J. & Brunel, N. (1995) Global spontaneous activity and local structured (learned) delay activity in cortex. Submitted. [DJA, rJJW]Google Scholar
Amit, D. J. & Tsodyks, M. V. (1990) Attractor neural networks with biological probe records. Network 1:381–405. [aJJW]Google Scholar
Amit, D. J. & Tsodyks, M. V. (1991) Quantitative study of attractor neural networks retrieving at low spike rates: 2. Low rate retrieval in symmetric networks. Network 2:275–94. [ajJW]CrossRefGoogle Scholar
Aradi, I., Barna, G., Érdi, P. & Gröbler, T. (1995) Chaos and learning in the olfactory bulb. International Journal of Intelligent Systems 10:89–117. [PÉ]CrossRefGoogle Scholar
Babloyantz, A. & Lourenco, C. (1994) Computation with chaos: A paradign for cortical activity. Proceedings of the National Academy of Sciences USA 91:9027–31. [IT]Google Scholar
Becker, C. J. & Freeman, W. J. (1968) Prepyriform electrical activity after loss of peripheral or central input or both. Physiology & Behavior 3:597–99. [WJF]CrossRefGoogle Scholar
Bennett, C. H. 91995) Quantum information and computation. Physics Today 48:24–30. [LI]Google Scholar
Bower, J. M. & Beeman, D. (1995) The book of Genesis: Exploring realistic neural models with GEneral NEural SImulation System. TELOS/Springer-Verlag. [JJW]Google Scholar
Braitenberg, V. (1978) Cell assemblies in the cerebral cortex. In: Theoretical approaches to complex systems [Lecture notes in Biomathematics, vol. 21], ed. Hcim, R. & Palm, C.. Springer-Verlag. [HP]Google Scholar
Braitenberg, V. & Schuz, A. (1991) Anatomy of the cortex: Statistics and geometry. Springer-Verlag. [aJJW]Google Scholar
Bressler, S. L., Coppola, R. & Nakamura, R. (1993) Episodic multiregional cortical coherence at multiple frequencies during visual task performance. Nature 366:153–56. [HRE, rJJW]Google Scholar
Bressler, S. L. & Freeman, W. J. (1980) Frequency analysis of olfactory system EEC in cat, rabbit and rat. EEC and Clinical Neurophysiology 50:19–24. [WJF]CrossRefGoogle Scholar
Bullock, T. H. & McClune, M. C. (1989) Lateral coherence of the electrocorticogram: A new measure of brain synchrony. Electroencephalography and Clinical Neurophysiology 73:479–98. [THB]Google Scholar
Bullock, T. H., McClune, M. C, Achimowicz, J. Z., Iragui-Madoz, V. J., Duckrow, R. B. & Spencer, S. S. (1995) EEC coherence has structure in the millimeter domain: Subdural and hippocampal recordings from epileptic patients. Electroencephalography and Clinical Neurophysiology 95:161–77. [THB]Google Scholar
Bullock, T. H., McClune, M. C, Achimowicz, J. Z., Iragui-Madoz, V. J., Duckrow, R. B. & Spencer, S. S. (in press) Temporal fluctuations in coherence of brain waves. Proceedings of the National Academy of Sciences [THB]Google Scholar
Burkitt, G. (1994) Steady-state visually evoked potentials and travelling waves. PhD dissertation, Swinburne University of Technology, Melbourne, Australia. [aJJW]Google Scholar
Burns, B. D. (1958) Mammalian cerebral cortex. Williams and Wilkins. [WJF, rJJW]Google Scholar
Buzsaki, B. & Chrobak, J. J. (1995) Temporal structure in spatially organized neuronal emsembles: A role for intemeuronal networks. Current Opinion in Neurobiology 5:504–20. [EK]Google Scholar
Caianiello, E. R., De Luca, A. & Ricciardi, L. M. (1967) Reverberations and control of neural networks. Kybernetik 4:10–18. [aJJW]Google Scholar
Calvin, W. H. & Ojemann, G. A. (1994) Conversations with Neil's brain: The neural nature of thought and language. Addison Wesley. [HRE]Google Scholar
Case, R. (1992) The role of the frontal lobes in the regulation of cognitive development. Brain and Cognition 20:51–73. [AO]Google Scholar
Chang, H. J. & Freeman, W. J. (in press) Parameter optimization in models of the olfactory system. Neural Networks. [WJF]Google Scholar
Churchland, P. S. (1986) Neurophilosophy–Toward a unified science of the mindbrain. MIT Press. [aJJW]Google Scholar
Churchland, P. S. & Sejnowski, T. J. (1994) The computational brain. MIT Press. [HRE]Google Scholar
Crick, F. (1994) The astonishing hypothesis: The scientific search for the soul. Scribners. [HRE]Google Scholar
Dehaene, S., Changeux, J. P. & Nadal, J. P. (1987) Neural networks that learn temporal sequences by selection. Proceedings of the National Academy of Science 84:2727–31. [aJJW]CrossRefGoogle ScholarPubMed
Dennett, D. C. (1991) Consciousness explained. Little, Brown. [HRE]Google Scholar
DeWitt, B. S. (1957) Dynamical theory in curved spaces: 1. A review of the classical and quantum action principles. Review of Modem Physics 29:377–97. [LI]CrossRefGoogle Scholar
Donchin, E. (1981) Suprise! … Surprise? Psychophysiology 18:493–513. [MM]Google Scholar
Eckhom, R. (1994) Oscillatory and non-oscillatory synchronization in the visual cortex of cat and monkey. In: Oscillatory event-related brain dynamics ed. Pantev, C., Elbert, T. & Lutkenhoner, B.. NATO ASI Series A: life Sciences, vol. 271. Plenum. [ZJK]Google Scholar
Eckhom, R., Bauer, B., Jordan, W., Brosch, M., Kruse, W., Munk, M. & Reitboeck, H. J. (1988) Coherent oscillation: A mechanism of feature linking in visual cortex? Biological Cybernetics 60:121–30. [aJJW, HP]Google Scholar
Eeckman, F. H. & Freeman, W. J. (1991) Asymmetric sigmoid nonlinearity in the rat olfactory system. Brain Research 557:13–21. [aJJW]CrossRefGoogle ScholarPubMed
Elbert, T., Ray, W. J., Kowalik, Z. J., Skinner, J. E., Graf, K. E. & Birbaumer, N. (1994) Chaos and physiology: Deterministic chaos in excitable cell assemblies. Physiological Reviews 74:1–47. [ZJK, HP]Google Scholar
Elbert, T. & Rockstroh, B. (1987) Threshold regulation: A key to the understanding of the combined dynamics of EEC and event-related potentials. Journal of Psychophysiology 4:317–33. [HP]Google Scholar
Érdi, P. (1983) Hierarchical approach to the brain. International Journal of Neuroscience 20:193–216. [PÉ]Google Scholar
Érdi, P., Gröbler, T. & Toth, J. (1992) On the classification of some classification problems. International Symposium on Information Physics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan. [PÉ]Google Scholar
Farley, B. G. (1965) A neuronal network model and the “slow potentials” in electrophysiology. In: Computers in biomedical research, ed. Stacy, R. W. & Waxman, B. D.. Academic Press. [MNZ]Google Scholar
Farmer, J. D. (1982) Information dimension and the probabilistic structure of chaos. Zeitschrifi für Naturforschung 37a:1304–25. [HP]Google Scholar
Freeman, W. J. (1964) A linear distributed feedback model for prepyriform cortex. Experimental Neurology 10:525–47. [aJJW]Google Scholar
Freeman, W. J. (1972) Measurement of open-loop responses to electrical stimulation in olfactory bulb of cat. Journal of Neurophysiology 35:745–61. [aJJW]Google Scholar
Freeman, W. J. (1975) Mass action in the nervous system. Academic Press. [aJJW, HRE, WJF, PLN]Google Scholar
Freeman, W. J. (1979) Nonlinear gain mediation of cortical stimulus response relations. Biological Cybernetics 33:237–47. [aJJW, WJF, HL]Google Scholar
Freeman, W. J. (1987a) Techniques used in the search for die physiological basis of the EEC. In: Handbook of electroencephalography and clinical neurophysiology, vol. 3A, ed. Gevins, A. S. & Remond, A.. Elsevier. [aJJW]Google Scholar
Freeman, W. J. (1987b) Simulation of chaotic EEC patterns with dynamic model of the olfactory system. Biological Cybernetics 56:139–50. [aJJW]CrossRefGoogle Scholar
Freeman, W. J. (1988) Strange attractors diat govern mammalian brain dynamics shown by trajectories of electroencephalographic (EEG) potential. IEEE Transactions on Circuits and Systems 35:781–83. [aJJW]Google Scholar
Freeman, W. J. (1991) Predictions on neoeortical dynamics derived from studies in paleoeortex. In: Induced rhythms of the brain, ed. Basar, E. & Bullock, T. H.. Birkhaeuser Boston Inc. [aJJW, ZJK]Google Scholar
Freeman, W. J. (1992) Tutorial in neurobiology: From single neurons to brain chaos. International Journal of Bifurcation and Chaos 2:451–82. [WJF]Google Scholar
Freeman, W. J. (1994) Neural mechanisms underlying destabilization of cortex by sensory input. Physica D 75:151–64. [IT]Google Scholar
Freeman, W. J. (1995a) Societies of brains: A study in the neuroscience of love and hate. Erlbaum. [HRE, WJF, IT]Google Scholar
Freeman, W. J. (1995b) Foreword. In: Chaos theory in psychology and the life sciences, ed. Robertson, R. & Combs, A.. Erlbaum. [rJJW, MM]Google Scholar
Freeman, W. J. (1995c) Chaos in the brain: Possible roles in biological intelligence. International Journal of Intelligent Systems 10:71–88. [IT]CrossRefGoogle Scholar
Freeman, W. J. & Barrie, J. M. (1994) Chaotic oscillations and the genesis of meaning in cerebral cortex. In: Temporal coding in the brain, ed. Buzsaki, C., Llinás, R., Singer, W., Berthoz, A. & Christen, Y.. Springer-Verlag. [WJF]Google Scholar
Freeman, W. J., Barrie, J. M., Lenhart, M. & Tang, R. X. (1995) Spatial phase gradients in neoeortical EECs give modal diameter of “binding” domains in perception. Society for Neuroscience Abstracts 21:1649. [WJF]Google Scholar
Freeman, W. J. & Jakubith, S. (1993) Bifurcation analysis of continuous time dynamics of oscillatory neural networks. In: Brain theory, ed. Aertson, A.. Springer-Verlag. [aJJW, ZJK]Google Scholar
Freeman, W. J. & Skarda, C. A. (1985)' Spatial EEG patterns, nonlinear dynamics and perception: The neo-Sherringtonian view. Brain Research Reviews 10:147–75. [aJJW]Google Scholar
Friedrich, R., Fuchs, A. & Hakan, H. (1991) Spatio-temporal EEG patterns. In: Rhythms in physiological systems, ed. Hakan, H. & Koepshen, H. P.. Springer-Verlag. [PLN]Google Scholar
Fuchs, A., Kelso, J. A. S. & Hakan, H. (1992) Phase transitions in the human brain spatial mode dynamics. International Journal of Bifurcation and Chaos 2:917–39. [PLN]Google Scholar
Gevins, A. S., Schaffer, R. E., Doyle, J. C., Cuttilo, B. A., Tannehill, R. S. & Bressler, S. L. (1983) Shadows of thought: Shifting lateralisation of human brain electrical patterns during a brief visuomotor task. Science 220:97–99. [aJJW]CrossRefGoogle ScholarPubMed
Glass, L., Kaplan, D. T. & Lewis, J. E. (1993) Tests for deterministic dynamics in real and model neural networks. In: Nonlinear dynamical analysis of the EEG, ed. Jansen, B. H. & Brandt, M. E.. World Scientific. [WSP]Google Scholar
Graham, R. (1978) Path-integral methods on nonequilibrium thermodynamics and statistics. In: Stochastic processes in nonequilibrium systems, ed. Garrido, L., Seglar, P. & Shepherd, P. J.. Springer-Verlag. [LI]Google Scholar
Grassberger, P. & Procaccia, I. (1983) Measuring die strangeness of strange attractors. Physica D 9:189–208. [MM, WSP]Google Scholar
Gray, C. M., Koenig, P., Engel, K. A. & Singer, W. (1989) Oscillatory responses in cat visual cortex exhibit intereolumnar synchronisation which reflects global stimulus properties. Nature 338:334–37. [aJJW, HP]Google Scholar
Gregson, R. A. M. (1992) Cognitive load as a determinant of the dimensionality of the electroencephalogram: A replication study. Biological Psychology 35:165–78. [MM]CrossRefGoogle Scholar
Grey, G. M., Koning, P., Engle, A. K. & Singer, W. (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334–37. [PLN]Google Scholar
Griniasty, M., Tsodyks, M. V. & Amit, D. J. (1993) Conversion of temporal correlations between stimuli to spatial correlations between attractors. Neural Computation 5:1–17. [aJJW]Google Scholar
Haken, H. (1977) Synergistics: An introduction. Springer-Verlag. [PÈ]Google Scholar
Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proceedings of National Academy of Sciences 79:2554–58. [aJJW]Google Scholar
Hopfield, J. J. (1984) Neurones with graded response have collective computational properties like those of two state neurones. Proceedings of National Academy of Science 81:3088–92. [aJJW]CrossRefGoogle Scholar
Hopfield, J. J. & Tank, D. W. (1986) Computing with neural circuits: A model. Science 233:625–33. [aJJW]Google Scholar
Houk, J. (1974) Feedback control of muscle: A synthesis of the peripheral mechanisms. In: Medical physiology, 13th ed., ed. Mountcastle, V. B.. Mosby. [WJF]Google Scholar
Ikeda, K., Otsuka, K. & Matsumoto, K. (1989) Maxwell-Bloch turbulence. Progress of Theoretical Physics [Suppl.] 99:295–313. [IT]Google Scholar
Ingber, L. (1981) Towards a unified brain theory. Journal of Social and Biological Structures 4:211–24. [LI]CrossRefGoogle Scholar
Ingber, L. (1982) Statistical mechanics of neocortical interactions: 1. Basic formulation. Physica D 5:83–107. [LI]CrossRefGoogle Scholar
Ingber, L. (1983) Statistical mechanics of neocortical interactions: Dynamics of synaptic modification. Physical Review A 28:395–416. [LI, PLN]Google Scholar
Ingber, L. (1984a) Path-integral Riemannian contributions to nuclear Schrödinger equation. Physical Review D 29:1171–74. [LI]Google Scholar
Ingber, L. (1984b) Statistical mechanics of neocortical interactions: Derivation of shortterm-memory capacity. Physical Review A 29:3346–58. [LI]Google Scholar
Ingber, L. (1984c) Statistical mechanics of nonlinear nonequilibrium financial markets. Mathematical Modelling 5:343–61. [LI]Google Scholar
Ingber, L. (1985) Statistical mechanics of neocortical interactions: Stability and duration of the 7 ± 2 rule of short-term-memory capacity. Physical Review A 31:1183–86. [LI]Google Scholar
Ingber, L. (1989) Very fast simulated re-annealing. Mathematical and Computer Modelling 12:967–73. [LI]Google Scholar
Ingber, L. (1990) Statistical mechanical aids to calculating term structure models. Physical Review A 42:7057–64. [LI]CrossRefGoogle ScholarPubMed
Ingber, L. (1991) Statistical mechanics of neoeortical interactions: A scaling paradigm applied to electroencephalography. Physical Review A 44:4017–60. [LI]Google Scholar
Ingber, L. (1992) Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions. Physical Review A 45:2183–R2186. [LI]CrossRefGoogle ScholarPubMed
Ingber, L. (1993) Adaptive simulated annealing (ASA) [ftp.alumni.caltech.edu:/pub/ingber/ASA-shar, ASA-shar.Z, ASA.tar.Z, ASA.tar.gz, ASA.zip]. Lester Ingber Research. [LI]Google Scholar
Ingber, L. (1994) Statistical mechanics of neocortical interactions: Path-integral evolution of short-term memory. Physical Review E 49:4652–64. [LI]Google Scholar
Ingber, L. (1995a) Statistical mechanics of multiple scales of neocortical interactions. In: Neocortical dynamics and human EEG rhythms, ed. Nunez, P. L.. Oxford University Press. [LI, PLN]Google Scholar
Ingber, L. (1995b) Statistical mechanics of neocortical interactions (SMN1). SMNI Lecture Plates. Lester Ingber Research. [LI]Google Scholar
Ingber, L. (1996a) Adaptive simulated annealing of canonical momenta indicators of financial market. Submitted. [LI]Google Scholar
Ingber, L. (1996b) Trading markets with canonical momenta and adaptive simulated annealing. Submitted. [LI]Google Scholar
Ingber, L. (in press a) Statistical mechanics of neocortical interactions: Constraints on 40 Hz models of short-term memory. Physical Review E [LI]Google Scholar
Ingber, L. (in press b) Statistical mechanics of nonlinear nonequilibrium financial markets: Applications to optimized trading. Mathematical and Computer Modelling. [LI]Google Scholar
Ingber, L. (in press c) Statistical mechanics of neocortical interactions: Statistical mechanics of neoeortical interactions: Multiple scales of EEG. Electroencephalography and Clinical Neurophysiology. [LI]Google Scholar
Ingber, L. & Nunez, P. L. (1990) Multiple scales of statistical physics of neocortex: Application to electroencephalogaphy. Mathematical and Computer Modelling 13:83–95. [aJJW, LI]Google Scholar
Ingber, L. & Nunez, P. L. (1995) Statistical mechanics of neocortical interactions: High resolution pathintegral calculation of short-term memory. Physical Review E 51:5074–83. [LI, PLN]Google Scholar
Ingber, L., Srinivasan, R. & Nunez, P. L. (in press) Path-integral evolution of chaos embedded in noise: Duffing neocortical analog. Mathematical and Computer Modelling. [LI]Google Scholar
Joliot, M., Ribary, U. & Llinás, R. (1994) Human oscillatory brain activity near 40 Hz coexists with cognitive temporal binding. Proceedings of the National Academy of Sciences USA 91:11748–51. [EK]CrossRefGoogle ScholarPubMed
Kaneko, K. (1990a) Globally coupled chaos violates the law of large numbers. Physical Review Letters 65:1391–94. [aJJW, ZJK, IT]Google Scholar
Kaneko, K. (1990b) Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements. Physica D 41:137–72. [IT]Google Scholar
Kaneko, K. (1992) Mean field fluctuation in network of chaotic elements. Physica D 55:368–84. [aJJW, IT]Google Scholar
Kaplan, D. & Glass, L. (1992) Direct test for determinism in a time series. Physical Review Letters 68:427–30. [ZJK, WSP]CrossRefGoogle Scholar
Kay, L., Shimoide, K. & Freeman, W. J. (1995) Comparison of EEG time series from rat olfactory system with model composed of nonlinear coupled oscillators. International Journal of Bifurcation and Chaos 5:849–58. [IT]Google Scholar
Kerszberg, M., Dehaene, S. & Changeux, J. P. (1992) Stabilization of complex input-output functions in neural clusters formed by synapse selection. Neural Networks 5:403–13. [AO]CrossRefGoogle Scholar
Kishida, K. (1982) Physical Langevin model and the time-series model in systems far from equilibrium. Physical Review A 25:496–507. [LI]Google Scholar
Kishida, K. (1984) Equivalent random force and time-series model in systems far from equilibrium. Jounal of Mathematical Physics 25:1308–13. [LI]Google Scholar
Kleinfeld, D. (1986) Sequential state generation by model neural networks. Proceedings of National Academy of Sciences 83:9469–73. [rJJW]Google Scholar
Kowalik, Z. J., Elbert, T. & Hoke, M. (1993) Mapping brain functions: The largest Lyapunov exponents derived from multi-channel magnetoencephalography. In: Nonlinear dynamic analysis of the EEG, & Jansen, B. H. & Brandt, M. E.. Singapore: World Scientific. [ZJK]Google Scholar
Krakowska, D., Waleszczyk, W., Bekisz, M. & Wrobel, A. (1995) General 20 Hz synchronization within cortico-thalamic division of the cat's visual system shifts to specific pattern during visual attention. European Journal of Neuroscience [Suppl.] 8:38. [ZJK]Google Scholar
Langouche, F., Roekaerts, D. & Tirapegui, E. (1982) Functional integration and semiclassical expansions. Reidel. [LI]Google Scholar
Langton, C. G., Taylor, C., Farmer, J. D., & Rassmussen, S., Eds. (1992) Artificial life 2: Santa Fe Institute studies in the sciences of complexity [Proceedings vol. 10]. Addison-Wesley. [arJJW]Google Scholar
Liley, D. T. J. (1995) Models of electrocortical dynamics. PhD thesis, University of Auckland, New Zealand. [rJJW]Google Scholar
liley, D. T. J. & Wright, J. J. (1994) Intracortical connectivity of pyramidal and stellate cells: Estimates of synaptic densities and coupling symmetry. Network: Computation in Neural Systems 5:175–89. [arJJW]Google Scholar
Liljenström, H. (1991) Modeling the dynamics of olfactory cortex using simplified network units and relistic architecture. International Journal Systems 2:1–15. [HL]Google Scholar
Liljenström, H. & Hasselmo, M. E. (1995) Cholinergic modulation of cortical oscinamics. Journal of Neurophysiology 74:288–97. [HL]CrossRefGoogle Scholar
Liljenström, H. & Wu, X. (1995) Noise-enhanced performance in a cortical associative memory model. International Systems 6:19–29. [HL]Google Scholar
Lisman, J. E. & Idiart, M. A. P. (1995) Storage of 7 ± 2 short-term memories in oscillatory subcycles. Science 267:1512–15. [LI]Google Scholar
Little, W. A. & Shaw, G. L. (1975) A statistical theory of short and long term memory. Behavioural Biology 14:115–33. [aJJW]Google Scholar
Llinás, R. & Ribary, U. (1993) Coherent 40-Hz oscillation characterized dream state in humans. Proceedings of the National Academy of Sciences USA 90:2078–81. [EK]Google Scholar
Lopes da Silva, F. H. & Storm van Leeuwen, W. (1978) The cortical alpha rhythm in dog: The depth and surface profile of phase. In: Architectonics of the cerebral cortex, ed. Brazier, M. A. B. & Petsche, H.. Raven. [aJJW]Google Scholar
Lopes da Silva, F. H., van Rotterdam, A., Barts, P., van Heusden, E. & Burr., B. (1976) Models of neuronal populations, the basic mechanisms of rhythmicity. Progress in Brain Research 45:282–308. [MNZ]Google Scholar
Lutzenberger, W., Elbert, T., Ray, W. J. & Birbaumer, N. (1993) The scalp distribution of the fractal dimension of the EEG and its variation with mental tasks. Brain Topography 5:27–34. [MM]Google Scholar
Matsumoto, K. & Tsuda, I. 91983) Noise-induced order. Journal of Statistical Physics 31:87–106. [IT]Google Scholar
McCulloch, W. S. & Pitts, W. H. (1943) A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5:115–33. [HRE]Google Scholar
Menon, V., Freeman, W. J., Cutillo, B. A., Desmond, J. E., Ward, M. F., Bressler, S. L., Laxer, K. D., Barbaro, N. M. & Gevins, A. S. (in press) Spatio-temporal correlations in human gamma band electroeorticograms. Electroencephalography and Clincal Neurophysiology. [WJF]Google Scholar
Miller, G. A. (1956) The magical number seven, plus or minus two. Psychology Review 63:81–97. [LI]Google Scholar
Miller, K. D., Keller, J. D. & Stryker, M. P. (1989) Ocular dominance column development: Analysis and simulation. Science 245:605–15. [AO]CrossRefGoogle ScholarPubMed
Miller, R. (1975) Distribution and properties of commissural and other neurons in cat sensorimotor cortex. Journal of Comparative Neurology 164:361–74. [RM]Google Scholar
Miller, R. (1989) Cortico-hippocampal interplay: Self-organizing phase-locked loops for indexing memory. Psychobiology 17:115–28. [RM]Google Scholar
Miller, R. (1993) An interpretation, based on cell assembly theory, of the psychological impairments following lesions of the the hippocampus and related structures. In:The memory system of the brain: Advanced series in neuroscience, vol. 4. Singapore: World Scientific. [RM]Google Scholar
Miller, R. (1994) What is the contribution of axonal conduction delay to temporal structure in brain dynamics? In:Oscillatory event-related brain dynamics [NATO ASI Series, vol. 271], ed. Pantev, C., Elbert, T. & Lutkenhoner, B.. Plenum. [RM]Google Scholar
Miller, R. (in press) Axonal conduction time and human cerebral laterality: A psychobiological theory. Gordon and Breach. [RM]CrossRefGoogle Scholar
Minsky, M. (1993) Book review: Allen Newell, Unified theory of cognition. Artificial Intelligence 59:343–54. [EK, rJJW]Google Scholar
Miyashita, Y. (1988) Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature 335:817–20. [aJJW]Google Scholar
Miyashita, Y. & Chang, H. S. (1988) Neural correlate of pictorial short-term memory in the primate temporal cortex. Nature 331:68–70. [aJJW]Google Scholar
Molnár, M. (1994) On the origin of the P3 event-related potential component. International Journal of Psychophysiology 17:129–44. [MM]CrossRefGoogle ScholarPubMed
Molnár, M. & Skinner, J. E. (1992) Low-dimensional chaos in event-related brain potentials. International Journal of Neuroscience 66:263–76. [MM]Google Scholar
Molnár, M., Skinner, J. E., Csépe, V., Winkler, I. & Karmos, G. (1995) Correlation dimension changes accompanying the occurrence of the msimatch-negativity and the P3 event-related potential component. Electroencephalography and Clinical Neurophysiology 95:118–26. [MM]Google Scholar
Mühlnickel, W., Rendtorff, N., Kowalik, Z. J., Rockstroh, B., Miltner, W. & Elbert, T. (1994) Testing the determinism of EEG and MEG. lntegrative Physiological and Behavioral Science 29:260–67. [ZJK]Google Scholar
Nadal, J. P., Toulouse, G., Changeux, J. P. & Dehaene, S. (1986) Europhysics Letters 1(10):535–42. [aJJW]Google Scholar
Nebenzahl, I. (1987) Recall of associated memories. Journal of Mathematical Biology 25:511–19. [aJJW]Google Scholar
Nicolis, G. & Prigogine, I. (1977) Self-organization in nonequilibrium systems. Wiley-Interscience. [PÉ, IT]Google Scholar
Nunez, P. L. (1989a) Generation of human EEG by a combination of long and short range neocortical interactions. Brain Topography 1:199–215. [aJJW]Google Scholar
Nunez, P. L. (1989b) Towards a physics of neocortex. In: Advanced methods of physiological systems modelling, vol. 2., ed. Marmarelis, V. Z.. Plenum. [PLN]Google Scholar
Nunez, P. L. (1995) Neocortical dynamics and human EEG rhythms. Oxford University Press. [aJJW, WJF, LI, PLN]Google Scholar
Nunez, P. L., Ed. (1981) Electric fields of the brain: The neurophysics of EEG. Oxford University Press. [aJJW, PLN]Google Scholar
Nunez, P. L., Silberstein, R. B., Cadusch, P. J., Wijesinghe, R. S., Westdorp, A. F. & Srinivasan, R. (1994) A theoretical and experimental study of high-resolution EEG based on surface Laplacians and cortical imaging. Electrocncephalography and Clinical Neurophysiology 90:40–57. [PLN]Google Scholar
Nunez, P. L. & Srinivasan, R. (1993) Implications of recording strategy for estimates of neocortical dynamics with electroencephalography. Chaos 3:257–66. [aJJW]Google Scholar
Oliver, A., Johnson, M. H. & Shrager, J. (1995) The emergence of hierarchical clustered representations in a Hebbian neural network model. Submitted. [AO]Google Scholar
Palus, M. (1994) Nonlinearity in normal human EEG: Cycles and randomness not chaos [Santa Fe Institute publication no. 94–10–054]. Santa Fe Institute. [WSP]Google Scholar
Parisi, G. (1986a) A memory which forgets. Journal of Physics 19:L617–L620. [aJJW]Google Scholar
Parisi, G. (1986b) Asymmetric neural networks and the process of learning. Journal of Physics 19:L675–L680. [aJJW]Google Scholar
Penrose, R. (1989) The emperor's new mind. Oxford University Press. [HRE]Google Scholar
Peretto, P. & Niez, J. J. (1986) Collective properties of neuronal networks. In: Disordered systems and biological organisation, ed. Bienenstock, E., Fogelman-Soulie, F. & Weisbuch, G.. Springer-Verlag. [aJJW]Google Scholar
Pfurtscheller, G. & Cooper, R. (1975) Frequency dependence of the transmission of the EEG from cortex to scalp. Electroencephalography and Clinical Neurophysiology 38:93–96. [PLN]Google Scholar
picton, T. W. & Hillyard, S. A. (1988) Endogenous event related potentials. In: EEG handbook, ed. Picton, T. W.. Elsevier. [aJJW]Google Scholar
Pijn, J. P., van Neerven, J., Noest, A.& Lopes da Silva, F. H. 91991) Chaos or noise in EEG signals; dependence on state and brain site. Electroencephalography and Clincal Neurophysiology 79:371–81. [WSP]CrossRefGoogle Scholar
Pritchard, W. S., Duke, D. W. & Krieble, K. K. (1995) Dimensional analysis of resting human EEG: 2. Surrogate-data testing indicates nonlinearity but now low-dimensional chaos. Psychophysiology 32:486–91. [ZJK, WSP]CrossRefGoogle Scholar
Pritchard, W. S., Krieble, K. K. & Duke, D. W. (1995b) No effect of cigarette smoking on electroencephalographic nonlinearity. Psychopharatnacology 199:349–51. [WSP]Google Scholar
Rapp, P. E., Albano, A. M., Schinah, T. I. & Farwell, L. A. (1993) Filtered noise can mimic low dimensional chaotic attractors. Physical Review E 47:2289–97. [LI]Google Scholar
Rapp, P. E., Bashore, T. R., Martineire, J. M., Albano, A. M., Zimmerrman, I. D. & Mees, A. I. (1989) Dynamics of brain electrical activity. Brain Topography 2:99–118. [MM]Google Scholar
Ruelle, D. (1994) Physics Today 47:24–30. [aJJW]Google Scholar
Ruppeiner, G. (1995) Riemannian geometry in thermodynamic fluctuation theory. Review of Modern Physics 67:605–59. [LI]Google Scholar
Sakai, K. & Miyashita, Y. (1991) Neural organisation for the long-term memory of paired associates. Nature 354:152–55. [aJJW]Google Scholar
Schiff, J. S., Jerger, K., Duong, D. H., Chang, T., Spano, M. L. & Ditto, W. L. (1994) Controlling chaos in the brain. Nature 370:615–20. [aJJW]Google Scholar
Schuz, A. (1994) Patchiness as a means to get a message across. Trends in Neuroscience 17:365. [RMJ]Google Scholar
Shepherd, G. M. (1994) Neurobiology, 3d ed.Oxford University Press. [HRE]Google Scholar
Sholl, D. A. (1953) Dendritic organization in the neurones of the visual and motor cortices of the cat. Journal of Anatomy 87:387–407. [aJJW]Google Scholar
Silberstein, R. B. (1994) Neuromodulation of neocortical dynamics. In: Neocortical dynamics and human EEG rhythms, ed. Nunez, P. L.. Oxford University Press. [aJJW]Google Scholar
Silberstein, R. B. (1995) Neuromodulation of neocortical dynamics. In: Neocortical dynamics and human EEG rhythms, ed. Nunez, P. L.. Oxford University Press. [PLN]Google Scholar
Singer, W. (1994) Putative functions of temporal correlations in neocortical processing. In: Large-scale neuronal theories of the brain, ed. Koch, C.& Davis, J. L.. MIT Press. [rJJW]Google Scholar
Skarda, C. A. & Freeman, W. J. (1987) How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 10:161–95. [MM]Google Scholar
Skinner, J. E., Molnár, M. & Tomberg, C. (1994) The point correlation dimension: Performance with nonstationaiy surrogate data and noise. lntegrative Physiological and Behavior Science 29:217–34. [MM]Google Scholar
Skinner, J. E., Molnár, M., Vybiral, T. & Mitra, M. (1992) Application of chaos theory to biology and medicine. lntegrative Physiological and Behavioral Science 27:39–53. [MM]Google Scholar
Steriade, M., Gloor, P., Llinas, R. R., Lopes da Silva, F. H. & Mesulam, M. M. (1990) Basic mechanisms of cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology 76:481–508. [arJJW]Google Scholar
Stewart, M. & Fox, S. E. (1990) Do septal neurons pace the hippocampal theta rhythm? TINS 13:163–68. [EK]Google Scholar
Stryker, M. P. (1989) Is grandmother an oscillation? Nature 338:297–98. [aJJW]Google Scholar
Swadlow, H. A. (1994) Efferent neurons and suspected interneurons in motor cortex of the awake rabbit: Axonal properties, sensory receptive fields and subthreshold synaptic inputs. Journal of Neurophysiology 71:437–53. [RM]Google Scholar
Thatcher, R. W., Krause, P. J. & Hrybyk, M. (1986) Cortico-cortical associations and EEG coherence: A two-compartmental model. Electroencephalography and Clinical Neurophysiology 64:123–43. [aJJW, RM]Google Scholar
Theiler, J., Eubank, S., Longtin, A., Galdrikian, B. & Farmer, J. D. (1992) Testing for nonlinearity in time series: The method of surrogate data. Physica D 58:77–94. [WSP]Google Scholar
Tovee, M. J. & Rolls, E. T. (1992) Oscillatory activity is not evident in the primate temporal visual cortex. Neuroreport 3:369–72. [aJJW]Google Scholar
Truesdell, C. (1969) Rational thermodynamics. McGraw-Hill. [PÉ]Google Scholar
Tsuda, I. (1991) Chaotic neural networks and thesaurus. In: Neurocomputers and attention: I. Neurobiology, synchronization and chaos, ed. Holden, A.V. & Krukov, V. I.. Manchester University. [IT]Google Scholar
Tsuda, I. (1992) Dynamic link of memory-chaotic memory maps in non-equilibrium neural networks. Neural Networks 5:313–26. [aJJW]CrossRefGoogle Scholar
Tsuda, I. (1994) Can stochastic renewal of maps be a model for cerebral cortex? Physica D 75:165–178. [aJJW]Google Scholar
Tsuda, I., Koemer, E. & Shimuzu, H. (1987) Memory dynamics in asynchronous neural networks. Progress in Theoretical Physics 78:51–71. [aJJW, IT]Google Scholar
Tsuda, I. & Matsumoto, K. (1984) Noise-induced order: Complexity theoretical digression. In: Chaos and statistical methods, ed. Kuramoto, Y.. Springer-Verlag. [IT]Google Scholar
Uttley, A. M. (1956) The probability of neural connexions. Proceedings of the Royal Society B 142:229–41. [aJJW]Google Scholar
van Kampen, N. G. (1976) Fluctuations in closed and open non-linear systems. In: Statistical physics, ed. Pál, L.& Szépfalusy, P.. North-Holland. [LI]Google Scholar
van Rotterdam, A., Lopes da Silva, F. H., van Den Ende, J., Veirgever, M. A. & Hermans, A. J. (1982) A model of the spatiotemporal characteristics of the alpha rhythm. Bulletin of Mathematical Biology 44:283–305. [rJJW, PLN, MJZ]Google Scholar
Ventriglia, F. (1988) Computational simulation of cortical-like neural systems. Bulletin of Mathematical Biology 52:397–429. [PÉ]Google Scholar
Ventriglia, F. (1990) Towards a kinetic theory of some global brain activities. Acta Neurologica 52:1–17. [PÉ]Google Scholar
Verleger, R. (1988) Event-related potentials and cognition: A critique of the context updating hypothesis and an alternative interpretation of the P3. Behavioral and Brain Sciences 11:343–427. [MM]Google Scholar
Vinogradova, O. S., Brazhnik, E. S., Stafekhina, V. S. & Kitchigina, V. F. (1993) Acetylcholine, theta-rhythm and activity of hippocampus: 2. Septal input. Neuroscience 53:971–79. [EK]Google Scholar
Vorobjov, N. A., Pavlik, V. D., Bakharev, B. V. & Zhadin, M. N. (1988) Spectra of electrical activity of the neocortex and hippocampus at stimulation of the reticular formation. Journal of Higher Nervous Activity 38:313–22 (in Russian). [MNZ]Google Scholar
Whittington, M. A., Traub, R. D. & Jeffreys, J. G. R. (1995) Synchronized oscillations in interneuronal networks driven by metabotropic glutamate receptor activation. Nature 373:612–15. [HRE]Google Scholar
Wilczek, F. (1994) A call for a new physics. Science 266:1737–38. [LI]Google Scholar
Wilson, M. A. & Bower, J. M. (1992) Cortical oscillations and temporal interactions in a computer simulation of periform cortex. Journal of Neurophysiology 67:981–95. [HL]Google Scholar
Wilson, M. A. & Cowan, J. D. (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13:55–80. [aJJW, PLN]Google Scholar
Wright, J. J. (1990) Reticular activation and the dynamics of neuronal networks. Biological Cybernetics 62:289–98. [aJJW]Google Scholar
Wright, J. J., Kydd, R. R & Sergejew, A. A. (1990a) Autoregression models of EEG. Biological Cybernetics 62:201–10. [arJJW]Google Scholar
Wright, J. J. & Liley, D. T. J. (1994) A millimetric-scale simulation of electrocortical wave dynamics based on anatomical estimates of cortical synaptic density. Network: Computation in Neural Systems 5:191–202. [aJJW]Google Scholar
Wright, J. J. & Liley, D. T. J. (1995) Simulation of electrocortical waves. Biological Cybernetics 72:347–56. [arJJW]CrossRefGoogle ScholarPubMed
Wright, J. J. & Sergejew, A. A. (1991) Radial coherence, wave velocity and damping of electrocortical waves. Electroencejrfialography and Clinical Neurophysiology 79:403–12. [arJJW]Google Scholar
Wright, J. J., Sergejew, A. A. & Liley, D. T. J. (1994) Computer simulation of electrocortical activity at millimetric scale. Electroencephalography and clinical Neurophysiology 90:365–75. [aJJW]CrossRefGoogle ScholarPubMed
Wright, J. J., Sergejew, A. A. & Stampfer, H. G. (1990b) Inverse filter computation of the neural impulse giving rise to the auditory evoked potential. Brain Topography 2:293–302. [aJJW]Google Scholar
Wu, X. & Liljenström, H. (1994) Regulating the nonlinear dynamics of olfactory cortex. Network: Computation in Neural Systems 5:47–60. [HL]Google Scholar
Yao, Y. & Freeman, W. J. (1990) Model of biological pattern recognition with spatially chaotic dynamics. Neural Networks 3(2):153–70. [HRE]Google Scholar
Yeterian, E. H. & Pandya, D. N. (1988) Architectonic features of the primate brain: Implications for information processing and behavior. In: Information processing by the brain, ed. Markovich, H. J.. Hans Huber. [EK]Google Scholar
Zhadin, M. N. (1977) Model of conditioning and analysis of functional significance of electrophysiological correlates of learning. Journal of Higher Nervous Activity 27:949–56 (in Russian). [MNZ]Google Scholar
Zhadin, M. N. (1982) Theory of rhythmic processes in the cerebral cortex. Academic Press (in Russian). [MNZ]Google Scholar
Zhadin, M. N. (1984) Rhythmic processes in cerebral cortex. Journal of Theoretical Biology 108:565–95. [PLN, MNZ]Google Scholar
Zhadin, M. N. (1991) Biophysical mechanisms of the EEG formation. In: Mathematical approaches to brain functioning diagnostics, ed. Dvorak, I.& Holden, A. V.. Manchester University Press. [MNZ]Google Scholar
Zhadin, M. N. (1994) Formation of rhythmic processes in the bioelectric activity of the cerebral cortex. Biophysics 39:133–50. [MNZ]Google Scholar
Zhang, G. & Simon, H. A. (1985) STM capacity for Chinese words and idioms: Chunking and acoustical loop hypotheses. Memory & Cognition 13:193–201. [LI]Google Scholar