Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abeles M (1991) Corticonics: neural circuits of the cerebral cortex. Cambridge University Press, New York
Andersen P, Andersson SA (1968) Physiological basis of the alpha rhythm. Appleton, New York
Bartlett FC (1932) Remembering. Cambridge University Press, New York, 2nd ed. 1967
Bressler SL (1987a) Functional relation of olfactory bulb and cortex. I. Spatial variation of bulbocortical interdependence. Brain Res 409:285–293
Bressler SL (1987 b) Functional relation of olfactory bulb and cortex. II. Model for driving of cortex by bulb. Brain Res 409:239–301
Bressler SL (1990) The gamma wave: a cortical information carrier? Trends Neurosci 13:161–162
Bressler SL, Freeman WJ (1980) Frequency analysis of olfactory system EEG in cat, rabbit and rat. Electroencephalography Clin Neurophysiol 50:19–24
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 60:121–130
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: 62–65
Eeckman FH, Freeman WJ (1990) Correlations between unit firing and EEG in the rat olfactory system. Brain Res 528:238–244
Eeckman FH, Freeman WJ (1991) Asymmetric sigmoid nonlinearity in the rat olfactory system. Brain Res 557:13–21
Elul R (1972) The genesis of the EEG. Int Rev Neurobiol 15:227–272
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 15:218–226
Freeman WJ (1974) Stability characteristics of positive feedback in a neural population. Transactions IEEE Biomed Engin 21:358–364
Freeman WJ (1975) Mass action in the nervous system, Academic Press, New York
Freeman WJ (1979a) Nonlinear gain mediating cortical stimulus-response relations. Biol Cybernet 33:237–247
Freeman WJ (1979 b) Nonlinear dynamics of paleocortex manifested in the olfactory EEG. Biol Cybernet 35:21–37
Freeman WJ (1979 c) EEG analysis gives model of neuronal template-matching mechanism for sensory search with olfactory bulb. Biol Cybernet 35:221–234
Freeman WJ (1987 a) Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biol Cybernet 56:139–150
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 583–664
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 126–143
Freeman WJ (1991a) The physiology of perception. Sci Amer 264:78–85
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 1–4
Freeman WJ (1992 a) Tutorial in neurobiology: From single neurons to brain chaos. Int J Bifurcation Chaos 2:451–482
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 183–199
Freeman WJ, Baird B (1987) Relation of olfactory EEG to behavior: Spatial analysis. Behav Neurosci 101:393–408
Freeman WJ, Grajski KA (1987) Relation of olfactory EEG to behavior: Factor analysis. Behav Neurosci 101:766–777
Freeman WJ, Schneider W (1982) Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors. Psychophysiology 19:44–56
Freeman WJ, van Dijk B (1987) Spatial patterns of visual cortical fast EEG during conditioned reflex in a rhesus monkey. Brain Res 422:267–276
Freeman WJ, Viana Di Prisco G (1986) Relation of olfactory EEG to behavior: Time series analysis. Behav Neurosci 100:753–763
Grajski KA, Breiman L, Viana Di Prisco G, Freeman WJ (1986) Classification of EEG spatial patterns with tree-structured methodology. IEEE Trans Biomed Engin 33:1076–1086
Grajski KA, Freeman WJ (1989) Spatial EEG correlates of non-associative and associative learning in rabbits. Behav Neurosci 103:790–804
Granger R, Ambros-Ingerson J, Lynch G (1989) Derivation of encoding characteristics of layer II cerebral cortex. J Cognit Sci 1:61–87
Gray CM, Koenig P, Engel A, Singer W (1989) Oscillatory responses in cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus properties. Nature 338:334–337
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 73:189–197
Haken H, Stadler M (1990) Synergetics of cognition. Springer-Verlag, Berlin
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architectures of the cat’s visual cortex. J Physiol 160:106–154
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 Press
Koenig P, Schillen TB (1991) Stimulus-dependent assembly formation of oscillatory responses: I. Synchronization. Neural comp 3:155–166
Lashley K (1948) The mechanism of vision. Journal Press, Provincetown MA
Lettvin JY, Maturana HR, McCulloch WS, Pitts WH (1959) What the frog’s eye tells the frog’s brain. Proc Inst Radio Engin 47:1940–1951
Li Z, Hopfield JJ (1989) Modeling the olfactory bulb and its neural oscillatory processings. Biol Cybernet 61:379–392
Liljenstrom H (1991) Modelling the dynamics of olfactory cortex using simplified network units and realistic architecture. Int J Neural Systems 2:1–15
Llinas R (1988) The intrinsic electrophysiological properties of mammalian neurons: Insights into central nervous system function. Science 242:1654–1664
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 1–14
Milner PM (1974) A model for visual shape recognition. Psych Rev 81:521–535
Mountcastle VB (1957) Modality and topographic properties of single neurons of cat’s somatic cortex. J Neurophysiol 20:408–434
Rall W, Shepherd GM (1968) Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J Neurophysiol 31:884–915
Skarda CA, Freeman WJ (1987) How brains make chaos to make sense of the world. Behav Brain Sci 10:161–195
Thompson JMT, Stewart HB (1988) Nonlinear dynamics and chaos. Wiley, New York
Tovee MJ, Rolls EJ (1992) The functional nature of neuronal oscillations. Trends Neurosci 15:187
Tsuda I (1991) Chaotic itinerancy as a dynamical basis of hermeneutics in brain and mind. World Futures 32:167–184
Viana Di Prisco G (1984) Hebb synaptic plasticity. Prog Neurobiol 22:89–102
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 238–249
Wilson MA, Bower JM (1992) Cortical oscillations and temporal interactions in a computer simulation of piriform cortex. J Neurophysiol 67:981–995
Yao Y, Freeman WJ, Burke B, Yang Q (1991) Pattern recognition by a distributed neural network: An industrial application. Neural Networks 4:103–121
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Freeman, W.J., Barrie, J.M. (1994). Chaotic Oscillations and the Genesis of Meaning in Cerebral Cortex. In: Buzsáki, G., Llinás, R., Singer, W., Berthoz, A., Christen, Y. (eds) Temporal Coding in the Brain. Research and Perspectives in Neurosciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-85148-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-85148-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-85150-6
Online ISBN: 978-3-642-85148-3
eBook Packages: Springer Book Archive