Brain and Mind

, Volume 2, Issue 3, pp 261–296 | Cite as

Operational Architectonics of the Human Brain Biopotential Field: Towards Solving the Mind-Brain Problem

  • Andrew A. Fingelkurts
  • Alexander A. Fingelkurts
Article

Abstract

The understanding of the interrelationshipbetween brain and mind remains far from clear.It is well established that the brain'scapacity to integrate information from numeroussources forms the basis for cognitiveabilities. However, the core unresolvedquestion is how information about the``objective'' physical entities of the externalworld can be integrated, and how unifiedand coherent mental states (or Gestalts) can beestablished in the internal entities ofdistributed neuronal systems. The present paperoffers a unified methodological and conceptualbasis for a possible mechanism of how thetransient synchronization of brain operationsmay construct the unified and relatively stableneural states, which underlie mental states.It was shown that the sequence of metastablespatial EEG mosaics does exist and probablyreflects the rapid stabilization periods of theinterrelation of large neuron systems. At theEEG level this is reflected in thestabilization of quasi-stationary segments oncorresponding channels. Within the introducedframework, physical brain processes andpsychological processes are considered as twobasic aspects of a single whole informationalbrain state.The relations between operational process ofthe brain, mental states and consciousness arediscussed.

adaptive segmentation binding problem coherence EEG microstructure functional integration metastability neocortical dynamics nonstationarity operational synchronization spatial scale 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adrian, E.D. and Matthews, B.H.C., 1934: The Berger rhythm: potential changes from occipital lobes in man, Brain 57(2), 355-385.Google Scholar
  2. Adrian, E.D. and Yamagiwa, K., 1935: The origin of the Berger rhythm, Brain 56(3), 323-352.Google Scholar
  3. Alexandrov, Yu.I., 1997: Systemic Psychophysiology, in Yu.I. Alexandrov (ed.), Basics of Psychophysiology, Infra-M, Moscow, 431 pp. (in Russian).Google Scholar
  4. Alexandrov, Yu.I., 1999: Psychophysiological regularities of the dynamics of individual experience and the “stream of consciousness”, in C. Taddei-Feretti and C. Musio (eds.), Series on Biophysics and Biocybernetics, Neural Basis and Psychological Aspects of Consciousness, World Scientific, Singapore-New Jersey-London-Hongkong, Vol. 8-Biocybernetics, pp. 201-219.Google Scholar
  5. Alves, R. and Savageau, M.A., 2000: Systemic properties of ensembles of metabolic networks: application of graphical and statistical methods to simple unbranched pathways, Bioinformatics 16, 534-547.Google Scholar
  6. Anokhin, P.K., 1973: Biology and Neurophysiology of Conditioned Reflex and Its Role in Adaptive Behavior, Pergamon Press, Oxford.Google Scholar
  7. Arbib, M.A., 1995: Brain Theory and Neural Networks, MIT Press, Cambridge, MA.Google Scholar
  8. Artola, A. and Singer, W., 1993: Long term depression of excitatory synaptic transmition and its relationship to long-term potentiation, Trends Neurosci. 16, 480-487.Google Scholar
  9. Baars, B.J., 2001: The brain basis of a “Consciousness Monitor”: scientific and medical significance, Conscious. Cogn. 10, 159-164.Google Scholar
  10. Baars, B.J., Tononi, G. and Bickle J., 2000: Criteria for consciousness in the brain: Methodological implications of recent developments in cognitive neuroscience, Conscious. Cogn. 9(2), S20-21.Google Scholar
  11. Bargh, J.A., 1997: The automaticity of everyday life, in R.S. Wyer, Jr. (ed.), Advances in Social Cognition, Erlbaum, Mahwah, NJ, V. 10, pp. 1-6.Google Scholar
  12. Barlow, J.S., 1985: Methods of analysis of nonstationary EEGs, with empahasis on segmentation techniques: A comparative review, J. Clin. Neurophysiol. 2(3), 267-304.Google Scholar
  13. Basar, E. and Bullock T.H., 1992: Induced Rhythms in the Brain, Birkhauster, Boston-Basel-Berlin.Google Scholar
  14. Basar, E., Basar-Eroglu, C., Karakas, S. and Schurmann, M., 2001: Gamma, alpha, delta, and theta oscillations govern cognitive processes, Int. J. Psychophysiol. 39, 241-248.Google Scholar
  15. Bertrand, O. and Tallon-Baudry, C., 2000: Oscillatory gamma activity in humans: a possible role for object representation, Int. J. Psychophysiol. 38(3), 211-223.Google Scholar
  16. Bi, G-q. and Poo, M-m., 2001: Synaptic modification by correlated activity: Hebb's postulate revisited, Annu. Rev. Neurosci. 24, 139-166.Google Scholar
  17. Binder, J.R., Frost, T.A., Hammeke, P.S., Bellgowan, P.S.F., Rao, S.M. and Cox, R.W., 1999: Conceptual processing during the conscious resting state: A functional MRI study, J. Cogn. Neurosci. 11(1), 80-93.Google Scholar
  18. Bliss, T.V.P. and Collingridge, G.L., 1993: A synaptic model of memory: LTP in the Hippocampus, Nature 361, 31-39.Google Scholar
  19. Bodenstein, G. and Praetorius, H.M., 1977: Feature extraction from the electroencephalogram by adaptive segmentation, Proc. IEEE. 65, 642-652.Google Scholar
  20. Bressler, S.L., 1987a: Relation of olfactory bulb and cortex: I. Spatial variation of bulbocortical interdependence, Brain Res. 409, 285-293.Google Scholar
  21. Bressler, S.L., 1987b: Relation of olfactory bulb and cortex: II. Model for driving of cortex by bulb, Brain Res. 409, 294-301.Google Scholar
  22. Bressler, S.L. and Keslo, J.A.S., 2001: Cortical coordination dynamics and cognition, Trends Cogn. Sci. 5(1), 26-36.Google Scholar
  23. Brodsky, B.E. and Darkhovsky, B.S., 1993: Nonparametric Methods in Change-Point Problems, Kluwer, Dordrecht.Google Scholar
  24. Brodsky, B.E. and Darhovsky, B.S., 2000: Nonparametric Statistical Diagnosis. Problems and Methods, Kluwer, Dordrecht.Google Scholar
  25. Buckner, R.L., Koutstaal, W., Schacter, D.L. and Rosen, B.R., 2000: Functional MRI evidence for a role of frontal and inferior temporal cortex in amodal components of priming, Brain 123, 620-640.Google Scholar
  26. Bunsey, M. and Eichenbaum, H., 1996: Conservation of hippacampal memory function in rats and humans, Nature 379, 255-257.Google Scholar
  27. Buonomano, D.V. and Merzenich, M.M., 1998: Cortical plasticity: from synapses to maps, Annu. Rev. Neurosci. 21, 149-186.Google Scholar
  28. Burnod, Y., Baraduc, P., Battaglia-Mayer, A., Guigon, E., Koechlin, E., Ferraina, S., Lacquaniti, F. and Caminiti, R., 1999: Parieto-frontal coding of reaching: an integrated framework, Exp. Bain Res. 129, 325-346.Google Scholar
  29. Burton, S., Murphy, D., Qureshi, U., Sutton, P. and O'Keefe, J., 2000: Combined lesions of hippocampus and subiculum do not produce deficits in a nonspatial social olfactory memory task, J. Neurosci. 20, 5468-5475.Google Scholar
  30. Callan, D.E., Callan, A.M., Kroos, C. and Vatikiotis-Bateson, E., 2001: Multimodal contribution to speech perception revealed by independent component analysis: A single-sweep EEG case study, Brain Res. Cogn. Brain Res. 10(3), 349-353.Google Scholar
  31. Chafe, W.L., 1994: Discourse, Consciousness, and Time: The Flow and Displacement of Conscious Experience in Speaking and Writing, Univ. of Chicago Press, Chicago.Google Scholar
  32. Chafee, M.V. and Goldman-Rakic, P.S., 2000: Inactivation of parietal and prefrontal cortex reveals interdependence of neural activity during memory-guided saccades, J. Neurophysiol. 83, 1550-1566.Google Scholar
  33. Corkin, S., Amaral, D.G., Gonzalez, R.G., Johnson, K.A. and Hyman, B.T., 1997: H.M.'s medical temporal lobe lesion: Findings from magnetic resonance imaging, J. Neurisci. 17, 3964-3979.Google Scholar
  34. Creutzfildt, O.D., Bodenstein, G. and Barlow, J.S., 1985: Computerized EEG pattern classification by adaptive segmentation and probability density function classification: clinical evaluation, Electroencephalogr. Clin. Neurophysiol. 60, 373-393.Google Scholar
  35. Crick, F., 1984: Function of the thalamic reticular complex: The searchlight hypothesis, Proc. Natl. Acad. Sci., USA. 81, 4586-4590.Google Scholar
  36. Damasio, A.R., 1990: Synchronous activation in multiple cortical regions: A mechanism for recall, Semin. Neurosci. 2, 287-296.Google Scholar
  37. Damasio, A.R. and Damasio, H., 1994: Cortical systems for retrieval of concrete knowledge: the convergence zone framework, in C. Koch and J. Davis (eds.), Large-Scale Neuronal Theories of The Brain, MIT Press, Cambridge, MA, pp. 61-74.Google Scholar
  38. Davis, G.W. and Goodman, C.S., 1998: Genetic analysis of synaptic development and plasticity: homeostatic regulation of synaptic efficacy, Curr. Opin. Neurobiol. 8, 149-156.Google Scholar
  39. Davis, G.W. and Bezprozvanny, I., 2001: Maintaining the stability of neural function: a homeostatic hypothesis, Annu. Rev. Physiol. 63, 847-869.Google Scholar
  40. Dijksterhuis, A., Knippenberg, A., Spears, R. and Postmes, T., 1998: Seeing one thing and doing another: Contrast effects in automatic behavior, J. Pers. Soc. Psychol. 75, 862-871.Google Scholar
  41. Doi, S., Nabetani, S. and Kumagai, S., 2001: Complex nonlinear dynamics of the Hodgkin-Huxley equations induced by time scale changes, Biol. Cybern. 85, 51-64.Google Scholar
  42. Driver, J. and Spence, C., 1998: Attention and the crossmodal construction of space, Trends Cogn. Sci. 2(7), 254-262.Google Scholar
  43. Dulany, D.E., 2000: A mentalistic view of conscious unity and dissociation, Conscious. Cogn. 9(2), S41-42.Google Scholar
  44. Edelman, G.M., 1987: Neuronal Darwinism: The Theory of Neuronal Group Selection, Basic Books, New York.Google Scholar
  45. Edelman, G.M., 1989: The Remembered Present: A Biological Theory of Consciousness, Basic Books, New York.Google Scholar
  46. Elman, J.L. and McClelland, J.L., 1984: Speech perception as a cognitive process: the interactive activation model, in N. Lass (ed.), Speech and Language, Academic Press, Vol. 10, pp. 337-374.Google Scholar
  47. Engel, A.K., Fries, P., Brecht, M. and Singer, W., 1997: Role of temporal domain for response selection and perceptual binding, Cereb. Cortex 7, 571-582.Google Scholar
  48. Engel, A.K., Fries, P., König, M.B. and Singer, W., 1999: Temporal binding, binocular rivalry, and consciousness, Conscious. Cogn. 8, 128-151.Google Scholar
  49. Epstein, R., 2000: The neural-cognitive basis of the Jamesian stream of thought, Conscious. Cogn. 9, 550-575.Google Scholar
  50. Erdi, P., 2000: On the ‘Dynamic Brain’ Metaphor, Brain and Mind 1, 119-145.Google Scholar
  51. Erdi, P. and Barna, G., 1984: Self-organizing mechanism for the formation of ordered neural mappings, Biol. Cybern. 51, 93-101.Google Scholar
  52. Fell, J., Kaplan, A., Darkhovsky, B. and Röschke, J., 2000: EEG analysis with nonlinear deterministic and stochastic methods: a combined strategy, Acta Neurobiol. Exp. 60, 87-108.Google Scholar
  53. Felleman, D.J. and Van Essen, D.C., 1991: Distributed hierarchical processing in the primate cerebral cortex, Cereb. Cortex 1, 1-47.Google Scholar
  54. Fingelkurts, Al.A., 1998: Some regularities of human EEG spectral patterns dynamics during cognitive activity, Ph.D. Dissertation, Moscow State Univ., Moscow, p. 305 (in Russian).Google Scholar
  55. Fingelkurts, An.A., 1998: Time-spatial organization of human EEG segment's structure, Ph.D. Dissertation, Moscow State Univ., Moscow, p. 415 (in Russian).Google Scholar
  56. Fingelkurts, An.A. and Fingelkurts, Al.A., 2001: Operational architectonics of the human EEG, World Congress on Neuroinformatics (September 24-29, 2001, Vienna) (invited full-text contribution).Google Scholar
  57. Fingelkurts, An.A., Fingelkurts, Al.A., Ivachko, R.M. and Kaplan, A.Ya., 1998: EEG analysis of operational synchrony between human brain cortical areas during memory task performance, VestnikMoskovskogo Universiteta (Bull. Moscow Univ.), Series 16, Biology 1, 3-11 (in Russian).Google Scholar
  58. Fingelkurts, An.A., Fingelkurts, Al.A., Borisov, S.V., Ivashko, R.M. and Kaplan, A.Ya., 2000: Spatial structures of human multichannel EEG quasi-stationary segments during memory task, Vestnik Moskovskogo Universiteta (Bull. Moscow Univ.), Series 16, Biology3, 3-10 (in Russian).Google Scholar
  59. Freeman, W.J., 1991: The physiology of perception, Sci. Am. 264(2), 78-85.Google Scholar
  60. Friston, K.J., 1997: Transients, metastability and neural dynamics, Neuroimage 5, 164-171.Google Scholar
  61. Friston, K.J., Tononi, G., Sporns, O. and Edelman, G.M., 1995: Characterizing the complexity of neuronal interactions, Hum. Brain Mapp. 3, 302-314.Google Scholar
  62. Galin, D., 1994: The structure of awareness: Contemporary applications ofWilliam James’ forgotten concept of “the fringe”, J. Mind Behav. 15, 375-402.Google Scholar
  63. Galin, D., 2000: Comments on Epstein's neurocognitive interpretation of William James's model of consciousness, Conscious. Cogn. 9, 576-583.Google Scholar
  64. Gath, I., Lehmann, D. and Bar-On, E., 1983: Fuzzy clustering of EEG signal and vigilance performance, Int. J. Neurosci. 20, 303-312.Google Scholar
  65. Gazzaniga, M.S., 1995: The Cognitive Neuroscience, MIT Press.Google Scholar
  66. Gazzaniga, M., 1998: The split brain revisited, Sci. Am. 279, 35-39.Google Scholar
  67. Gell-Mann, M. and Lloyd, S., 1996: Information measures, effective complexity, and total information, Complexity 2, 44.Google Scholar
  68. Gevins, A.S. and Cutillo, B.A., 1986: Signals of cognition, in F.H. Lopes da Silva (ed.), Handbook of Electroencephalography and Clinical Neurophysiology, Vol. 2, Elsevier, Amsterdam, pp. 335-381.Google Scholar
  69. Gevins, A.S. and Cutillo, B.A., 1995: Neuroelectric measures of mind, in P.L. Nunez (ed.), Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, New York, pp. 304-338.Google Scholar
  70. Graham, G. and Neisser, J., 2000: Probing for relevance:What metacognition tells us about the power of consciousness, Conscious. Cogn. 9, 172-177.Google Scholar
  71. Gray, C.M. and Singer, W., 1989: Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex, Proc. Natl. Acad. Sci. USA 86(5), 1698-702.Google Scholar
  72. Gray, C.M., Konig, P., Engel, A.K. and Singer, W., 1989: Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties, Nature 338(6213), 334-337.Google Scholar
  73. Grechenko, T.N., 1991: Features of associative learning of small isolated neurons of the edible snail, Neurosci. Behav. Physiol. 21(1), 41-43.Google Scholar
  74. Grossberg, S., 2000: The complementary brain: unifying brain dynamics and modularity, Trends Cogn. Sci. 4(6), 233-246.Google Scholar
  75. Haken, H., 1996: Principles of Brain Functioning, Springer.Google Scholar
  76. Haken, H., 1999: What can synergetics contribute to the understanding of brain functioning?, in C. Uhl (ed.), Analysis of Neurophysiological Brain Functioning, Springer-Verlag, Berlin, pp. 7-40.Google Scholar
  77. Haken, H., Kelso, J.A. and Bunz, H., 1985: A theoretical model of phase transitions in human hand movements, Biol. Cybern. 51, 347-356.Google Scholar
  78. Hebb, D.O., 1949: The Organization of Behavior, Wiley, New York.Google Scholar
  79. Heinrich, R. and Rapoport, T.A., 1974: A linear steady-state treatment of enzymatic chains: general properties, control and effector strength, Eur. J. Biochem. 42, 89-95.Google Scholar
  80. Heisenberg, M., 1994: Voluntariness (Willkurfahigkeit) and the General Brganization of Behavior, Flexibility and Constraint in Behavioral Systems, John Wiley & Sons, England, 147 pp.Google Scholar
  81. Helekar, S.A., 1999: On the possibility of universal neural coding of subjective experience, Conscious. Cogn. 8, 423-449.Google Scholar
  82. Hilgard, E.R., 1965: Hypnotic Susceptibility, Harcourt, Brace & World, New York.Google Scholar
  83. Hobson, A.J., 1988: The Dreaming Brain, Basic Books, New York.Google Scholar
  84. Hodgkin, A.L. and Huxley, A.F., 1952: A quantitative description of membrane current and its applications to conduction and excitation in nerve, J. Physiol. 117, 500-544.Google Scholar
  85. Hofmeyr, J.H.S. and Westerhoff, H.V., 2001: Building the cellular puzzle: Control in multi-level reaction networks, J. Theor. Biol. 208, 261-285.Google Scholar
  86. Hoppensteadt, F.C. and Ishikevich, E.M., 1999: Oscillatory neurocomputers with dynamic connectivity, Phys. Rev. Lett. 82, 2983-2986.Google Scholar
  87. Inouye, T., Toi, S. and Matsumoto, Y., 1995: A new segmentation method of electroencephalograms by use of Akaike's information criterion, Brain Res. Cogn. Brain Res. 3, 33-10.Google Scholar
  88. Ivanitsky, A.M., 1997: Informational synthesis in crucial cortical area as the brain base of the subjective experience, Zh. Vyssh. Nerv. Deiat. Im. I. P. Pavlova (Journal of Higher Nervous Activity) 47(2), 10-21 (Original Russian Version: pp. 209-225).Google Scholar
  89. Jackendoff, R., 1994: Patterns in The Mind, Basic Books, New York.Google Scholar
  90. James, W., 1890: The Principles of Psychology, Vol. I, Dover, New York.Google Scholar
  91. Jansen, B.H., 1991: Quantitative analysis of the electroencephalograms: is there chaos in the future, Int. J. Biomed. Comput. 27, 95-123.Google Scholar
  92. Jansen, B.H., Hasman, A. and Lenten, R., 1981: Piece-wise EEG analysis: an objective evaluation, Int. J. Biomed. Comput. 12, 17-27.Google Scholar
  93. Jirsa, V.K. and Haken, H., 1997: A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics, Physica D99, 503-526.Google Scholar
  94. John, E.R., 2001: A Field theory of consciousness, Conscious. Cogn. 10, 184-213.Google Scholar
  95. Jung, T.P., Makeig, S., Stensmo, M. and Sejnowski, T.J., 1997: Estimating alertness from the EEG power spectrum, IEEE Trans. Biomed. Eng. 44(1), 60-69.Google Scholar
  96. Kacser, H., Burns, J.A. and Fell, D.A., 1995: The control of flux: 21 years on, Biochem. Soc. Trans. 23, 341-366.Google Scholar
  97. Kalitzin, S., van Dijk, B.W. and Spekreijse, H., 2000: Self-organized dynamics in plastic neural networks: bistability and coherence, Biol. Cybern. 83, 139-150.Google Scholar
  98. Kaplan, A.Ya., 1998: Nonstationary EEG: methodological and experimental analysis, Usp. Physiol. Nayk. (Success in Physiological Sciences) 29(3), 35-55 (in Russian).Google Scholar
  99. Kaplan, A.Ya., 1999: The problem of segmental description of human electroencephalogram, Human Physiol. 25(1), 107-114 (Translated from Physiol. Cheloveka).Google Scholar
  100. Kaplan, A.Ya. and Shishkin, S.L., 2000: Application of the change-point analysis to the investigation of the brain's electrical activity, in B.E. Brodsky and B.S. Darkhovsky (eds.), Non-parametric Statistical Diagnosis. Problems and Methods, Kluwer, Dordrecht, pp. 333-388.Google Scholar
  101. Kaplan, A.Y., Brodsky, B.E., Darkhovsky, B.S., Shishkin, S.L., Fingelkurts, Al.A. and Fingelkurts, An.A., 1995: Change-point mapping: a new technique for EEG brain imaging, in Proc. First International Conference on Functional Mapping of the Human Brain, Hum. Brain Mapp. 1, 97.Google Scholar
  102. Kaplan, A.Y., Darkhovsky, B.S. and Roeschke, J., 1997a: Microstructure of the sleep stages: stateshift analysis of the EEG in humans, Electroencephalogr. Clin. Neurophysiol. 103, 178.Google Scholar
  103. Kaplan, A.Y., Fingelkurts, Al.A., Fingelkurts, An.A. and Darkhovsky B.S., 1997b: Topological mapping of sharp reorganization synchrony in multichannel EEG, Am. J. Electroneurodiagnostic Technol. (Am J END) 37, 265-275.Google Scholar
  104. Kaplan, A.Ya., Fingelkurts, An.A., Fingelkurts, Al.A. and Ivashko, R.M., 1998: Temporal consistency of phasic changes in the EEG basic frequency components, Zh. Vyssh. Nerv. Deiat. Im. I. P. Pavlova (Journal of Higher Nervous Activity) 48(5), 816-826 (in Russian).Google Scholar
  105. Kaplan, A.Ya., Fingelkurts, Al.A., Fingelkurts, An.A. and Ermolaev, V.A., 1999: Topografic variability of the EEG spectral patterns, Human Physiol. 25(2), 140-147 (Translated from Fiziol. Cheloveka, 25(2), 21-29).Google Scholar
  106. Kaplan, A.Ya., Fingelkurts, An.A., Fingelkurts, Al.A., Shishkin, S.L. and Ivashko, R.M., 2000: Spatial synchrony of human EEG segmental structure, Zh. Vyssh. Nerv. Deiat. Im. I. P. Pavlova (Journal of Higher Nervous Activity) 50(4), 624-637 (in Russian).Google Scholar
  107. Kaplan, A., Röschke, J., Darkhovsky, B. and Fell, J., 2001: Macrosructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis, J. Neurosci. Methods 106, 81-90.Google Scholar
  108. Kelley, C.M. and Jacoby, L.L., 1998: Subjective reports and process dissociation: Fluency, knowing, and feeling, Acta Psychol. 98, 127-140.Google Scholar
  109. Kelso, J.A.S., 1984: Phase transitions and critical behavior in human bimanual coordination, Am. J. Physiol. Regul. Integr. Comp. Physiol. 15, R1000-R1004.Google Scholar
  110. Kelso, J.A.S., 1995: Review of Dynamic Patterns: The Self-organization of Brain and Behavior, MIT Press, Cambridge, MA.Google Scholar
  111. Kelso, J.A.S., 2000: Principles of dynamic pattern formation and change for a science of human behavior, in L.R. Bergman, R.B. Cairnce, L-G. Nilsson and L. Nystedt (eds.), Developmental Science and the Holistic Approach, Erlbaum, pp. 63-83.Google Scholar
  112. Kempter, R., Leibold, C., Wagner, H. and van Hemmen, J.L., 2001: Formation of temporal-feature maps by axonal propagation of synaptic learning, Proc. Natl. Acad. Sci. USA 98(7), 4166-4171.Google Scholar
  113. Khazen, A., 1992: Origin and evolution of life and reason in terms of information synthesis, Biophysics 37(1), 88-103 (In original version pp. 105-122).Google Scholar
  114. Khazen, A., 1993: Maximum entropy production as a motive force of progressive biological evolution, Biophysics 38(3), 537-565 (In original version pp. 531-551).Google Scholar
  115. Khazen, A., 1998: Introduction of the Information Measure Into the Axiomatic Basis of Mechanics, II edition. RAUB, Moscow.Google Scholar
  116. Khazen, A., 2000: Nature's Intelligence and Intelligence of Man, Mosobluprpoligrafizdat, Moscow.Google Scholar
  117. Koriat, A., 2000: The feeling of knowing: Some metatheoretical implications for consciousness and control, Conscious. Cogn. 9, 149-171.Google Scholar
  118. Koriat, A. and Levy-Sadot, R., 2000: Conscious and unconscious metacognition: a rejoinder, Conscious. Cogn. 9, 193-202.Google Scholar
  119. Lazarev, V.V., 1997: On the interrelation of some frequency and amplitude parameters of the human EEG and its functional significance. Communication I: Multidimentional neurodynamic organization of functional stats of the brain during intellectual perceptive and motor activity in normal subjects, Int. J. Psychophysiol. 28, 77-98.Google Scholar
  120. Lehmann, D., 1971: Multichannel topography of human alpha EEG fields, Electroencephalogr. Clin. Neurophysiol. 31(5), 439-449.Google Scholar
  121. Lehmann, D., 1980: Fluctuation of functional state: EEG patterns, and perceptual and cognitive strategies, in M. Koukkou et al. (eds.), Functional States of the Brain: Their Determinants, Elsevier, Amsterdam, pp. 189-202.Google Scholar
  122. Lehmann, D., 1987: Principles of spatial analysis: Methods of analysis of brain electrical and magnetic signals, in A.S. Gevins and A. Remond (eds.), EEG Handbook (revised series), Vol. 1, Chapter 12, pp. 309-354.Google Scholar
  123. Lehmann, D., 1991: Brain electric field mapping and map analysis in psychiatry: The “Atoms of Thought”, Biol. Psychiatry 67(2), 391.Google Scholar
  124. Lehmann, D. and Koenig, T., 1997: Spatio-temporal dynamics of alpha brain electric fields, and cognitive models, Int. J. Psychophysiol. 26, 99-112.Google Scholar
  125. Lehmann, D., Wackermann, J., Michel, C.M. and Koenig T., 1993: Space-oriented EEGsegmentation reveals changes in Brain electric field maps under the influence of a nootropic drug, Psychiatry Res. 50, 275-282.Google Scholar
  126. Lehmann, D., Kochi, K., Koenig, T., Koukkou, M., Michel, C.M. and Strik, W.K., 1995: Microstates of the brain electric field and momentary mind states, in M. Eiselt, U. Zwiener and H. Witte (eds.), Quantitative and Topological EEG and MEG Analysis, Universitatsverlag Jena, pp. 139-146.Google Scholar
  127. Llinas, R., 1990: Intrinsic electrical properties of mammalian neurons and CNS function, Fidea Res. Found. Neurosci. Award Lect. 4, 1-10.Google Scholar
  128. Llinas, R., Ribary, U., Joliot, M. and Wand, X.J., 1994: Content and context in temporal thalamocortical binding, in G. Buzsaki et al. (eds.), Temporal Coding in the Brain, Springer-Verlag, Berlin, pp. 251-272.Google Scholar
  129. Llinas, R., Ribary, U., Contreras, D. and Pedroarena, C., 1998: The neuronal basis for consciousness, Phil. Trans. R. Soc. London Ser. B353, 1841-1849.Google Scholar
  130. Lopes da Silva, F.H., 1987: Computer-assisted EEG diagnosis: pattern-recognition techniques, in F.H. Lopes da Silva (ed.), Electroencephalography: 1 Basic Principles, Clinical Applications and Related Fields, Urban & Schwarzenberg, pp. 900-919.Google Scholar
  131. Luria, A.R., 1980: Higher Cortical Functions in Man Google Scholar
  132. Kluwer Lutzengerger, W., 1997: EEG alpha dynamics as viewed from EEG dimension dynamics, Int. J. Psychophysiol. 26(1-3), 273-283.Google Scholar
  133. Lutzenberger, W., Preissl, H. and Pulvermuller, F., 1995: Fractal dimension of electroencephalographic time series and underlying brain processes, Biol. Cybern. 73, 477-487.Google Scholar
  134. Mangan, B.B., 1991: Meaning and the structure of consciousness: An essay in psycho-aesthetics, Unpublished Ph.D. thesis, University of California, Berkley.Google Scholar
  135. Mangan, B.B., 1993a: Taking phenomenology seriously: The “fringe” and its implications for cognitive research, Conscious. Cogn. 2, 89-108.Google Scholar
  136. Mangan B.B., 1993b: Some philosophical and empirical implications of the fringe, Conscious. Cogn. 2, 142-154.Google Scholar
  137. Manmaru, S. and Matsuura, M., 1989: Quantification of benzodiazepine-induced topographic EEG changes by a computerized wave form recognition method: Application of a principle component analysis, Electroencephalogr. Clin. Neurophysiol. 72, 126-132.Google Scholar
  138. Matousek, M., Wackermann, J. and Palus, P., 1995: Global dimensional complexity of the EEG in healthy volunteers, Neuropsychobiology 31(1), 47-52.Google Scholar
  139. Mittenthal, J.E., Clarke, B., Waddell, T.G. and Fawcett. G., 2001: A new method for assembling metabolic networks, with application to the Krebs Citric Acid Cycle, J. Theor. Biol. 208, 361-382.Google Scholar
  140. Nanez, P.L., 1981: Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press, New York.Google Scholar
  141. Nunez, P.L., 1995: Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, New York.Google Scholar
  142. Nunez, P.L., 2000: Toward a quantitative description of large-scale neocortical dynamic function and EEG, Behav. Brain Sci. 23(3), 371-437.Google Scholar
  143. Palmer, S.E., 1999: Vision Science: Photons to Phenomenology, MIT Press, Cambridge, MA.Google Scholar
  144. Perruchet, P. and Vinter, A., 2000: Thinking learning differently: The self-organizing consciousness (SOC) model, Conscious. Cogn. 9(2), S32-33.Google Scholar
  145. Pribram, K., 1991: Brain and Perception: Holonomy and Structure in Figural Processing, Erlbaum, Hillsdale, NJ.Google Scholar
  146. Prigogin, I. and Stengers, I., 1986: The Order from Chaos, Progress, Moscow (in Russian).Google Scholar
  147. Ramachandran, V.S. and Blakeslee, S., 1998: Phantoms in the Brain: Probing the Mysteries of the Human Mind, Willian Morrow, New York.Google Scholar
  148. Revonsuo, A., 1999: Binding and the phenomenal unity of consciousness, Conscious Cogn. 8, 173-185.Google Scholar
  149. Revonsuo, A., 2000a: Binding and the unity of consciousness, Conscious. Cogn. 9(2), S16-17.Google Scholar
  150. Revonsuo, A., 2000b: Prospects for a scientific research program on consciousness, in T. Metzinger (ed.), Neural Correlates of Consciousness, MIT Press, Cambridge, MA.Google Scholar
  151. Revousuo, A., 2001: Can functional brain imaging discover consciousness in the brain? J. Conscious. Studies 8(3), 3-23.Google Scholar
  152. Revonsuo, A. and Salmivalli, C., 1995: A content analysis of bizarre elements in dreams, Dreaming 5(3), 169-187.Google Scholar
  153. Rosenthal, D., 2000: Consciousness and the philosophy of mind, Conscious Cogn. 9(2), S14-16.Google Scholar
  154. Sams, M., Aulanko, R., Hamalainen, M., Hari, R., Lounasmaa, O.V., Lu, S.T. and Simola, J., 1991: Seeing speech: Visual information from lip movements modifies activity in the human auditory cortex, Neurosci. Lett. 127(1), 141-145.Google Scholar
  155. Sato, W., Kochiyama, T., Yoshikawa, S. and Matsumura, M., 2001: Emotional expression boosts early visual processing of the face: ERP recording and its decomposition by independent component analysis, Cog. Neurosci. Neuropsychol. 12(4), 709-714.Google Scholar
  156. Schillen, T.B. and König, P., 1994: Binding by temporal structure in multiple feature domains of an oscillatory neuronal network, Biol. Cybern. 70, 397-405.Google Scholar
  157. Scott, A.C., 1995: Stairway to the Mind, Springer-Verlag, New York.Google Scholar
  158. Searle, L.R., 1980: Minds, brains and programs, Behav. Brain Sci. 3, 417-457.Google Scholar
  159. Searle, J.R., 1997: The Mystery of Consciousness, New York Review, New York.Google Scholar
  160. Searle, J.R., 2000: Consciousness, Annu. Rev. Neurosci. 23, 557-579.Google Scholar
  161. Sechenov, I.M., 1956: The Reflexes of the Human Brain. Selected Works, Moscow, Vol. 1 (in Russian).Google Scholar
  162. Senn, W., Markram, H. and Tsodyks, M., 2001: An algorithm for modifying neurotransmitter release probability based on pre-and post-synaptic spike timing, Neural Comput. 13(1), 35-67.Google Scholar
  163. Shishkin, S.L., Brodsky, B.E., Darkhovsky, B.S. and Kaplan, A.Ya., 1997: EEG as a non-stationary signal: an approach to analysis based on non-parametric statistics, Human Physiol. (Fiziologia Cheloveka) 23(4), 124-126 (in Russian).Google Scholar
  164. Shishkin, S.L., Darkhovsky, B.S., Fingelkurts, Al.A., Fingelkurts, An.A. and Kaplan A.Ya., 1998: Interhemisphere synchrony of short-term variations in human EEG alpha power correlates with self-estimates of functional state, in Proc. 9-th World Congress of Psychophysiology (Tvaormin, Sicily), Italy, pp. 133.Google Scholar
  165. Shvirkov, V.B., 1995: Introduction in objective psychology. Neurological base of Mind, Isdatelstvo Inst. Psyshology Russ. Acad. Sci, Moscow (in Russian).Google Scholar
  166. Silberstein, R.B., 1995: Neuromodulation of neocortical dynamics, in P.L. Nunez (ed.), Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, pp. 591-627.Google Scholar
  167. Singer, W., 1993: Synchronization of cortical activity and its putative role in information processing and learning, Annu. Rev. Physiol. 55, 349-374.Google Scholar
  168. Singer, W., 1999: Time as coding space? Curr. Opin. Neurobiol. 9, 189-194.Google Scholar
  169. Singer, W., Engel, A.K., Kreiter, A.K., Munk, M.H.J., Neuenschwander, S. and Roelfsema, P.R., 1997: Neuronal assemblies: Necessity, significance, and detectability, Trends Cogn. Sci. 1, 252-261.Google Scholar
  170. Smolensky, P., 1990: Tensor product variable binding and the representation of symbolic structures in connectionist systems, Artif. Intell. 46, 159-216.Google Scholar
  171. Sporns, O., Gally, J.A, Reeke, G.N., Jr. and Edelman, G.M., 1989: Re-entrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity, Proc. Natl. Acad. Sci. USA 86, 7265-7269.Google Scholar
  172. Squire, L.R., 1992: memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans, Psychol. Rev. 99, 195-231.Google Scholar
  173. Stephans, G.L. and Graham, G., 2000: When Self-Consciousness Breaks: Alien Voices and Inserted Thoughts, MIT Press, Cambridge, MA.Google Scholar
  174. Sturm, A.K. and König, P., 2001: Mechanisms to synchronize neuronal Activity, Biol. Cybern. 84, 153-172.Google Scholar
  175. Suzuki, W.A., Zola-Morgan, S., Squire, L.R. and Amaral, D.G., 1993: Lesions of the perirhinal and parahippocampal cortices in the monkey produce long-lasting memory impairment in the visual and tactile modalities, J. Neurosci. 13, 2430-2451.Google Scholar
  176. Szentagothai, J., 1978: The neuron network of the cerebral cortex: a functional interpretation, Proc. R. Soc. Lond. B. Biol. Sci. 201, 219-248.Google Scholar
  177. Tallon-Baudry, C., Bertrand, O., Delpuech, C. and Permier, J., 1997: Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans, J. Neurosci. 17(2), 722-734.Google Scholar
  178. Teng, E. and Squire, L.R., 1999: Memory for places learned long ago is intact after hippocampal damage, Nature 400, 675-677.Google Scholar
  179. Tononi, G. and Edelman, G.M., 1998: Consciousness and complexity, Science 282, 1846-1851.Google Scholar
  180. Tononi, G., Sporns, O. and Edelman, G.M., 1992: Reentry and the problem of integrating multiple cortical areas: Simulation of dynamic integration in the visual system, Cereb. Cortex 2, 310-335.Google Scholar
  181. Tononi, G., Sporns, O. and Edelman, G.M., 1994: Ameasure for brain complexity: relating functional segregation and integration in the nervous system, Proc. Natl. Acad. Sci. USA 91, 5033-5037.Google Scholar
  182. Tononi, G., Edelman, G.M. and Sporns, O., 1998: Complexity and coherency: integrating information in the brain, Trends Cogn. Sci. 2(12), 474-484.Google Scholar
  183. Trujillo, T., 2000: Temporal synchronization: A possible mechanism for the binding together of the conscious self, Conscious. Cogn. 9(2), S36.Google Scholar
  184. Ts'o, D.Y. and Gilbert, C.D., 1988: The organization of chromatic and spatial interactions in the primate striate cortex, J. Neurosci. 8, 1712-1727.Google Scholar
  185. Tsuda, I., 2001: Towards an interpretation of dynamic neural activity in terms of chaotic dynamical systems, Behav. Brain Sci. 24(4) (in press).Google Scholar
  186. Turrigano, G.G., 1999: Homeostatic plasticity in neuronal networks: the more things change, the more they stay the same, Trends Neurosci. 22, 221-227.Google Scholar
  187. Uhtomskyi, A.A., 1978: Selected Works, Leningrad (in Russian).Google Scholar
  188. van Gelder, T., 1990: Compositionality: A connectionist variation on a classical theme, Cognit. Sci. 14, 355-384.Google Scholar
  189. Wada, M., Ogawa, T., Sonoda, H. and Sato, K., 1996: Development of relative power contribution ratio of the EEG in normal children: A multivariate autoregressive modelling approach, Electroencephalogr. Clin. Neurophysiol. 98, 69-75.Google Scholar
  190. Whittlesea, B.W., Jacoby, L.L. and Girard, K., 1990: Illusions of immediate memory: Evidence of an attributional basis for feeling of familiarity and perceptual quality, J. Mem. Lang. 29, 716-732.Google Scholar
  191. Wise, S.P. Boussaoud, D, Johnson, P.B. and Caminiti, R., 1997: Premotor and parietal cortex: corticocortical connectivity and combinatorial computations, Annu. Rev. Neurosci. 20, 25-42.Google Scholar
  192. Wright, J.J. and Liley, D.T.J., 1996: Dynamics of the brain at global and microscopic scales: Neural networks and the EEG, Behav. Brain Sci. 19(2), 285-320.Google Scholar
  193. Wright, J.J., Bourke, P.D. and Chapman, C.L., 2000: Synchronous oscillation in the cerebral cortex and object coherence: simulation of basic electrophysiological findings, Biol. Cybern. 83, 341-353.Google Scholar
  194. Zeki, S., 1990: The motion pathways of the visual cortex, in C. Blakemore (ed.), Vision: Coding and Efficiency, Cambridge Univ. Press, Cambridge, UK, pp. 321-345.Google Scholar
  195. Zeki, S., 2001: Localization and globalization in conscious vision, Annu. Rev. Neurosci. 24, 57-86.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Andrew A. Fingelkurts
    • 1
    • 1
  • Alexander A. Fingelkurts
    • 1
  1. 1.Research Group of Cognitive Science and Technology, Laboratory ofComputational EngineeringHelsinki University of TechnologyFinland

Personalised recommendations