Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-07-06T14:42:22.374Z Has data issue: false hasContentIssue false
  • English
  • Français

Brain neural activity patterns yielding numbers are operators, not representations

Published online by Cambridge University Press:  27 August 2009

Walter J. Freeman  and
Robert Kozma
Walter J. Freeman
Affiliation:
Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA 94720-3206. dfreeman@berkeley.edu
Robert Kozma
Affiliation:
Department of Mathematics, University of Memphis, Memphis, TN 38152-3240. rkozma@memphis.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

We contrapose computational models using representations of numbers in parietal cortical activity patterns (abstract or not) with dynamic models, whereby prefrontal cortex (PFC) orchestrates neural operators. The neural operators under PFC control are activity patterns that mobilize synaptic matrices formed by learning into textured oscillations we observe through the electroencephalogram from the scalp (EEG) and the electrocorticogram from the cortical surface (ECoG). We postulate that specialized operators produce symbolic representations existing only outside of brains.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 32 , Issue 3-4 , August 2009 , pp. 336 - 337
Copyright
Copyright © Cambridge University Press 2009

Access options

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

References

Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T. & Bullmore, E. (2006) Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences USA (PNAS) 103:19518–23.CrossRefGoogle ScholarPubMed
Freeman, W. J. (2009) The neurobiological infrastructure of natural computing: Intentionality. New Mathematics and Natural Computing 5(1):19–29.CrossRefGoogle Scholar
Freeman, W. J., Ahlfors, S. P. & Menon, V. (2009) Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition. International Journal of Psychophysiology. 73(1):43–52.CrossRefGoogle ScholarPubMed
Freeman, W. J., Burke, B. C. & Holmes, M. D. (2003) A periodic phase re-setting in scalp EEG of beta-gamma oscillations by state transitions at alpha-theta rates. Human Brain Mapping 19(4):248–72.CrossRefGoogle Scholar
Gerstmann, I. (1958) Psychological and phenomenological aspects of disorders of the body image. Journal of Nervous and Mental Disease 126(6):499–512. Available at: http://www.ninds.nih.gov/disorders/gerstmanns/gerstmanns.htmCrossRefGoogle ScholarPubMed
Kozma, R. & Freeman, W. J. (2008) Intermittent spatio-temporal desynchronization and sequenced synchrony in ECoG signals. Chaos 18:037131.CrossRefGoogle ScholarPubMed
Merleau-Ponty, M. (1942/1963) The structure of behavior, trans. Fischer, A. L.. Beacon.Google Scholar
Pockett, S., Bold, G. E. J. & Freeman, W. J. (2009) EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies. Clinical Neurophysiology 120(4):695–708.CrossRefGoogle Scholar
Ruiz, Y., Li, G., Freeman, W. F. & Gonzalez, E. (in press) Detecting stable phase structures on EEG signals to classify brain activity amplitude patterns. Journal of Zhejiang University.Google Scholar
von Neumann, J. (1958) The computer and the brain. Yale University Press.Google Scholar
Yakovlev, P. I. (1962) Morphological criteria of growth and maturation of the nervous system in man. Research Publications of the Association for Research in Nervous and Mental Diseases (ARNMD) 39:3–46.Google ScholarPubMed