Chaotic Oscillations and the Genesis of Meaning in Cerebral Cortex

  • W. J. Freeman
  • J. M. Barrie
Part of the Research and Perspectives in Neurosciences book series (NEUROSCIENCE)


Single neurons generate action potentials that express their output in pulse frequencies, so that sensory stimuli can be microscopically expressed as spatial patterns of phase-locked firing of “feature detector” neurons. The visual, auditory, somatic, and olfactory cortices generate dendritic potentials that oscillate at frequencies from 1-100 Hz. These waves reveal macroscopic activity arising from synaptic interactions of millions of neurons. They share a spatially coherent oscillation as a “carrier,” by which spatial patterns of amplitude modulation (AM) are transmitted in distinctive configurations, when subjects receive sensory stimuli they have learned to discriminate. These spatial AM patterns are unique to each subject, are not invariant with respect to stimuli, and cannot be derived from the stimuli by logical operations. The carrier is aperiodic, usually dispersed over a wide spectral range. Our simulations of the carrier indicate that its dynamics is chaotic, and that sequential patterns are freshly constructed during perception, because chaotic systems can create as well as destroy information. The entire experience of a subject, which is embedded in synaptic connections in cortex that were modified during learning, can be brought instantly to bear at each state transition by which a new construction is initiated. It is suggested that “feature binding” revealed by microscopic recording is related to the formation of a “chaotic construct” early in the process of perception.


Spatial Pattern Olfactory Bulb Unconditioned Stimulus Olfactory System Feature Binding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Abeles M (1991) Corticonics: neural circuits of the cerebral cortex. Cambridge University Press, New YorkCrossRefGoogle Scholar
  2. Andersen P, Andersson SA (1968) Physiological basis of the alpha rhythm. Appleton, New YorkGoogle Scholar
  3. Bartlett FC (1932) Remembering. Cambridge University Press, New York, 2nd ed. 1967Google Scholar
  4. Bressler SL (1987a) Functional relation of olfactory bulb and cortex. I. Spatial variation of bulbocortical interdependence. Brain Res 409:285–293PubMedCrossRefGoogle Scholar
  5. Bressler SL (1987 b) Functional relation of olfactory bulb and cortex. II. Model for driving of cortex by bulb. Brain Res 409:239–301Google Scholar
  6. Bressler SL (1990) The gamma wave: a cortical information carrier? Trends Neurosci 13:161–162PubMedCrossRefGoogle Scholar
  7. Bressler SL, Freeman WJ (1980) Frequency analysis of olfactory system EEG in cat, rabbit and rat. Electroencephalography Clin Neurophysiol 50:19–24CrossRefGoogle Scholar
  8. Eckhorn R, Bauer R, Jordan W, Brosch M, Kruse W, Munk M, Reitboeck HJ (1988) Coherent oscillations: a mechanism of feature linking in the visual cortex? Biol Cybernet 60:121–130CrossRefGoogle Scholar
  9. Edelman JA, Freeman WJ (1990) Simulation and analysis of a model of mitral-granule cell population interactions in the mammalian olfactory bulb. Proc Intl Joint Conference Neural Networks I: 62–65Google Scholar
  10. Eeckman FH, Freeman WJ (1990) Correlations between unit firing and EEG in the rat olfactory system. Brain Res 528:238–244PubMedCrossRefGoogle Scholar
  11. Eeckman FH, Freeman WJ (1991) Asymmetric sigmoid nonlinearity in the rat olfactory system. Brain Res 557:13–21PubMedCrossRefGoogle Scholar
  12. Elul R (1972) The genesis of the EEG. Int Rev Neurobiol 15:227–272CrossRefGoogle Scholar
  13. Engel AK, Koenig P, Kreiter AK, Schulen TB, Singer W (1992) Temporal coding in the visual cortex: new vistas on integration in the nervous system. Trends Neurosci 15:218–226PubMedCrossRefGoogle Scholar
  14. Freeman WJ (1974) Stability characteristics of positive feedback in a neural population. Transactions IEEE Biomed Engin 21:358–364CrossRefGoogle Scholar
  15. Freeman WJ (1975) Mass action in the nervous system, Academic Press, New YorkGoogle Scholar
  16. Freeman WJ (1979a) Nonlinear gain mediating cortical stimulus-response relations. Biol Cybernet 33:237–247CrossRefGoogle Scholar
  17. Freeman WJ (1979 b) Nonlinear dynamics of paleocortex manifested in the olfactory EEG. Biol Cybernet 35:21–37CrossRefGoogle Scholar
  18. Freeman WJ (1979 c) EEG analysis gives model of neuronal template-matching mechanism for sensory search with olfactory bulb. Biol Cybernet 35:221–234CrossRefGoogle Scholar
  19. Freeman WJ (1987 a) Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biol Cybernet 56:139–150CrossRefGoogle Scholar
  20. Freeman WJ (1987 b) Techniques used in the search for the physiological basis of the EEG. In: Gevins A, Remond A (eds). Handbook of electroencephalography & clinical neurophysiology. Vol 3A, Part 2, Ch. 18. Elsevier, Amsterdam, pp 583–664Google Scholar
  21. Freeman WJ (1990) On the problem of anomalous dispersion in chaoto-chaotic phase transitions of neural masses, and its significance for the management of perceptual information in brains. In: Haken H, Stadler M (eds.) Synergetics of cognition. Vol 45, Springer-Verlag, Berlin, pp 126–143Google Scholar
  22. Freeman WJ (1991a) The physiology of perception. Sci Amer 264:78–85PubMedCrossRefGoogle Scholar
  23. Freeman WJ (1991b) Development of a new science of brain dynamics with guidance from the theory of nonlinear dynamics and chaos. Proc 8th Int Conference Biomagnetism, Muenster, Germany, pp 1–4Google Scholar
  24. Freeman WJ (1992 a) Tutorial in neurobiology: From single neurons to brain chaos. Int J Bifurcation Chaos 2:451–482CrossRefGoogle Scholar
  25. Freeman WJ (1992b) Predictions on neocortical dynamics derived from studies in paleocortex In: Basar, E. and Bullock, TH (eds.) Induced rhythms of the brain. Birkhaeuser, Cambridge, MA, pp 183–199Google Scholar
  26. Freeman WJ, Baird B (1987) Relation of olfactory EEG to behavior: Spatial analysis. Behav Neurosci 101:393–408PubMedCrossRefGoogle Scholar
  27. Freeman WJ, Grajski KA (1987) Relation of olfactory EEG to behavior: Factor analysis. Behav Neurosci 101:766–777PubMedCrossRefGoogle Scholar
  28. Freeman WJ, Schneider W (1982) Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors. Psychophysiology 19:44–56PubMedCrossRefGoogle Scholar
  29. Freeman WJ, van Dijk B (1987) Spatial patterns of visual cortical fast EEG during conditioned reflex in a rhesus monkey. Brain Res 422:267–276PubMedCrossRefGoogle Scholar
  30. Freeman WJ, Viana Di Prisco G (1986) Relation of olfactory EEG to behavior: Time series analysis. Behav Neurosci 100:753–763PubMedCrossRefGoogle Scholar
  31. Grajski KA, Breiman L, Viana Di Prisco G, Freeman WJ (1986) Classification of EEG spatial patterns with tree-structured methodology. IEEE Trans Biomed Engin 33:1076–1086CrossRefGoogle Scholar
  32. Grajski KA, Freeman WJ (1989) Spatial EEG correlates of non-associative and associative learning in rabbits. Behav Neurosci 103:790–804PubMedCrossRefGoogle Scholar
  33. Granger R, Ambros-Ingerson J, Lynch G (1989) Derivation of encoding characteristics of layer II cerebral cortex. J Cognit Sci 1:61–87Google Scholar
  34. Gray CM, Koenig P, Engel A, Singer W (1989) Oscillatory responses in cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus properties. Nature 338:334–337PubMedCrossRefGoogle Scholar
  35. Gray CM, Skinner JE (1988) Field potential response changes in the rabbit olfactory bulb accompany behavioral habituation during repeated presentation of unreinforced odors. Exp Brain Res 73:189–197PubMedCrossRefGoogle Scholar
  36. Haken H, Stadler M (1990) Synergetics of cognition. Springer-Verlag, BerlinGoogle Scholar
  37. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architectures of the cat’s visual cortex. J Physiol 160:106–154PubMedGoogle Scholar
  38. Kammen DM, Hohnes PJ, Koch C (1989) Cortical architecture and oscillations in neural networks: Feedback versus local coupling. In: Cotterill RMJ (ed.) Models of brain function. Cambridge University PressGoogle Scholar
  39. Koenig P, Schillen TB (1991) Stimulus-dependent assembly formation of oscillatory responses: I. Synchronization. Neural comp 3:155–166CrossRefGoogle Scholar
  40. Lashley K (1948) The mechanism of vision. Journal Press, Provincetown MAGoogle Scholar
  41. Lettvin JY, Maturana HR, McCulloch WS, Pitts WH (1959) What the frog’s eye tells the frog’s brain. Proc Inst Radio Engin 47:1940–1951Google Scholar
  42. Li Z, Hopfield JJ (1989) Modeling the olfactory bulb and its neural oscillatory processings. Biol Cybernet 61:379–392CrossRefGoogle Scholar
  43. Liljenstrom H (1991) Modelling the dynamics of olfactory cortex using simplified network units and realistic architecture. Int J Neural Systems 2:1–15CrossRefGoogle Scholar
  44. Llinas R (1988) The intrinsic electrophysiological properties of mammalian neurons: Insights into central nervous system function. Science 242:1654–1664PubMedCrossRefGoogle Scholar
  45. Meyer-Kress G, Barczys C, Freeman WJ (1991) Attractor reconstruction from eventrelated multi-electrode EEG data. Holden AV (ed.) Proc. Intern. Symposium Mathematical Approaches to Brain Functioning Diagnostics (IBRO) Singapore, World Scientific, pp 1–14Google Scholar
  46. Milner PM (1974) A model for visual shape recognition. Psych Rev 81:521–535CrossRefGoogle Scholar
  47. Mountcastle VB (1957) Modality and topographic properties of single neurons of cat’s somatic cortex. J Neurophysiol 20:408–434PubMedGoogle Scholar
  48. Rall W, Shepherd GM (1968) Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J Neurophysiol 31:884–915PubMedGoogle Scholar
  49. Skarda CA, Freeman WJ (1987) How brains make chaos to make sense of the world. Behav Brain Sci 10:161–195CrossRefGoogle Scholar
  50. Thompson JMT, Stewart HB (1988) Nonlinear dynamics and chaos. Wiley, New YorkGoogle Scholar
  51. Tovee MJ, Rolls EJ (1992) The functional nature of neuronal oscillations. Trends Neurosci 15:187CrossRefGoogle Scholar
  52. Tsuda I (1991) Chaotic itinerancy as a dynamical basis of hermeneutics in brain and mind. World Futures 32:167–184CrossRefGoogle Scholar
  53. Viana Di Prisco G (1984) Hebb synaptic plasticity. Prog Neurobiol 22:89–102CrossRefGoogle Scholar
  54. von der Malsburg C (1983) How are nervous structures organized? In: Basar E, Flohr H, Haken H, Mandell AJ (eds.) Synergetics of the brain. Springer-Verlag, Berlin, pp 238–249Google Scholar
  55. Wilson MA, Bower JM (1992) Cortical oscillations and temporal interactions in a computer simulation of piriform cortex. J Neurophysiol 67:981–995PubMedGoogle Scholar
  56. Yao Y, Freeman WJ, Burke B, Yang Q (1991) Pattern recognition by a distributed neural network: An industrial application. Neural Networks 4:103–121CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • W. J. Freeman
  • J. M. Barrie

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