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Linking minds and brains

Published online by Cambridge University Press:  21 August 2013

HORACE BARLOW*
Affiliation:
Department of Physiology, Neuroscience and Development, University of Cambridge, Cambridge, UK
*
*Address correspondence to: Horace Barlow, Department of Physiology, Neuroscience and Development, University of Cambridge, Cambridge, CB2 3EG UK. E-mail: [email protected]

Abstract

When I first came across William James’ dictum that “ … this sense of sameness is the very keel and backbone of our thinking,” I thought he had foreseen the importance of cross-correlation in the brain, and told myself to find out how he had reached this conclusion. When I finally did this a year or two ago, I slowly came to realize that I had completely misunderstood him; from the full quote it is absolutely clear that his dictum cannot be referring to the process by which a cortical simple cell responds selectively to the orientation of features in a visual image, as I had originally supposed. If one translates the original dictum into two more prosaic modern versions, his version would say: “Our minds could not think at all without neural circuits in our brains that compute auto-correlations,” but in my mistaken interpretation the last word would be “cross-correlations.” Others may have made the same mistake, but the difference is profound, and finding what he really meant has been a revelation to me. This essay explains the revelation, describes how to determine experimentally whether the brain does auto- or cross-correlation, and gives the result of preliminary experiments showing clearly that it does both. A revised view of the visual cortex as autocorrelator as well as cross-correlator claims to tell us what complex cells in the visual cortex do, and it assigns a role to its columnar structure that is as important to fulfilling that role as the concept of the receptive field has been to understanding the simple cells’ fulfillment of theirs. The new view has compelling features, broad implications, and suggests a plausible model of how neural circuits in the cortex achieve thought, but it needs further testing.

Type
Retrospective and prospective analyses of linking propositions
Copyright
Copyright © Cambridge University Press 2013 

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Footnotes

This paper is based on a talk with the title “Making sense of V1” given at the Symposium on “Turing Enduring: Information Processing by Brains and Machines,” organized by Columbia, New York and Rockefeller Universities and taking place at Rockefeller University on Thursday December 13, 2012.

References

Ahmad, K.M., Klug, K., Herr, S., Sterling, P. & Schein, S. (2003). Cell density ratios in a foveal patch in macaque retina. Visual Neuroscience 20, 189209.CrossRefGoogle Scholar
Allman, J.M. (1990). The origin of the neocortex. Neuroscience 2, 257262.Google Scholar
Allman, J.M. (1999). Evolving Brains. New York: Scientific American Library.Google Scholar
Barlow, H.B. (1953). Summation and inhibition in the frog’s retina. The Journal of Physiology 119, 6988.CrossRefGoogle ScholarPubMed
Barlow, H.B. (1961). Possible principles underlying the transformations of sensory messages. In Sensory Communication, Chapter 13, ed. Rosenblith, W., pp. 217234. Cambridge, MA: MIT Press.Google Scholar
Barlow, H.B. (1979). Reconstructing the visual image in space and time. Nature 279, 189190.CrossRefGoogle ScholarPubMed
Barlow, H.B. (1990). A theory about the functional role and synaptic mechanism of visual after-effects. In Vision: Coding and Efficiency, ed. Blakemore, C.B., Cambridge: Cambridge University Press.Google Scholar
Barlow, H.B. (2001). Redundancy reduction revisited. Network: Computation in Neural Systems 12, 241253.CrossRefGoogle ScholarPubMed
Barlow, H.B. & Berry, D.L. (2011). Cross- and auto-correlation in early vision. Proceedings Biological Sciences/The Royal Society 278, 20692075.CrossRefGoogle ScholarPubMed
Barlow, H.B. & Földiák, P. (1989). Adaptation and decorrelation in the cortex. In The Computing Neuron, ed. Miall, C., Durbin, R.M. & Mitchison, G.J., pp. 5472. Wokingham, England: Addison-Wesley.Google Scholar
Beaulieu, C., Kisvarday, Z., Somogyi, P., Cynader, M. & Cowey, A. (1992). Quantitative distribution of GABA-immunopositive and -immunonegative neurons and synapses in the monkey striate cortex (area17). Cerebral Cortex 2, 295309.CrossRefGoogle Scholar
Blasdel, G.G. & Salama, G. (1986). Voltage sensitive dyes reveal a modular organization in monkey striate cortex. Nature 321, 579585.CrossRefGoogle ScholarPubMed
Brindley, G.S. (1960). Physiology of the retina and visual pathway. In Monographs of the Physiological Society, No. 6, London: Arnold.Google Scholar
Brindley, G.S. (1970). Physiology of the Retina and Visual Pathways (2nd ed.). Baltimore, MD: Williams & Wilkins.Google Scholar
Burr, D.C. (1979). Acuity for apparent vernier offset. Vision Research 19, 835837.CrossRefGoogle ScholarPubMed
Carandini, M. (2006). What simple and complex cells compute. The Journal of Physiology 577, 463466.CrossRefGoogle ScholarPubMed
Carandini, M., Demb, J.B., Mante, V., Tolhurst, D.J., Dan, Y., Olshausen, B.A., Gallant, J.L. & Rust, N.C. (2005). Do we know what the early visual system does? The Journal of Neuroscience 25, 1057710597.CrossRefGoogle Scholar
Carandini, M. & Heeger, D.J. (2012). Normalization as a canonical neural computation. Nature Reviews. Neuroscience 13, 5162.CrossRefGoogle Scholar
Carandini, M., Heeger, D.J. & Movshon, J.A. (1997). Linearity and normalization in simple cells of the macaque primary visual cortex. The Journal of Neuroscience 17, 86218644.CrossRefGoogle ScholarPubMed
Cherici, C., Kuang, X., Poletti, M. & Rucci, M. (2012). Precision of sustained fixation in trained and untrained observers. Journal of Vision 12, 116.CrossRefGoogle ScholarPubMed
Chow, K.L., Blum, J.S. & Blum, R.A. (1950). Cell ratios on the thalamo-cortical visual system of macaca mulata. The Journal of Comparative Neurology 92, 227239.CrossRefGoogle Scholar
Connolly, M. & Van Essen, D. (1984). The representation of the visual fields in parvicellular and magnocellular layers of the lateral geniculate nucleus in the macaque monkey. The Journal of Comparative Neurolology 226, 544564.CrossRefGoogle ScholarPubMed
Craik, K.J.W. (1943) The Nature of Explanation. Cambridge: Cambridge University Press.Google Scholar
Crick, F.H.C., Marr, D.C. & Poggio, T. (1981). An information processing approach to understanding the visual cortex. In The Organization of the Cerebral Cortex; Proceedings of a Neurosciences Research Program Colloquium, ed. Schmitt, F.O., pp. 505534. Cambridge, MA: MIT Press.Google Scholar
Glass, L. (1969). Moirée effect from random dots. Nature 223, 578580.CrossRefGoogle Scholar
Hartline, H.K. (1938). The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. American Journal of Physiology 121, 400415.CrossRefGoogle Scholar
Hassenstein, B. & Reichardt, W. (1956). Systemtheoretische analyse der zeit-, reihenfolgen- and vorzeichenauswertung bei der bewegungsperzeption des russelkafers chlorophanus. Z. Naturforsch. 11b, 513524.CrossRefGoogle Scholar
Hawken, M.J. & Parker, A.J. (1991). Spatial receptive field organisation in monkey V1 and its relationship to the cone mosaic. In Computational Models of Visual Processing, ed. Landy, M.S. & Movshon, J.A., pp. 8393. Cambridge, MA: The MIT Press.Google Scholar
Heeger, D.J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 181197.CrossRefGoogle ScholarPubMed
Herrick, C.J. (1926). Brains of Rats and Men. Chicago, IL: The University of Chicago Press.Google Scholar
Herrick, C.J. (1928). An Introduction to Neurology. Philadelphia, PA: W.B.Saunders Co.Google Scholar
Hubel, D.H. (1988). Eye, Brain, and Vision. New York: Freeman.Google Scholar
Hubel, D.H. & Wiesel, T.N. (1959). Receptive fields of single neurones in the cat’s striate cortex. The Journal of Physiology 148, 574591.CrossRefGoogle ScholarPubMed
Hubel, D.H. & Wiesel, T.N. (1962). Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. The Journal of Physiology 160, 106154.CrossRefGoogle ScholarPubMed
Hübener, M., Shoham, D., Grinvald, A. & Bonhoeffer, T. (1997). Spatial relationships among three columnar systems in cat area 17. The Journal of Neuroscience 17, 92709284.CrossRefGoogle ScholarPubMed
James, W. (1890). The Principles of Psychology. Cambridge, MA: Harvard University Press.Google Scholar
Jerison, H.I. (1973). Evolution of the Brain and its Intelligence. New York: Academic Press.Google Scholar
Kuang, X., Poletti, M., Victor, J.D. & Rucci, M. (2012). Temporal encoding of spatial information during active visual fixation. Current Biology: CB 22, 510514.CrossRefGoogle ScholarPubMed
Kuffler, S.W. (1953). Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology 16, 3768.CrossRefGoogle ScholarPubMed
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Fransisco, CA: W H Freeman.Google Scholar
Morgan, M. & Thompson, I.D. (1975). Apparent motion and the Pulfrich effect. Perception 4, 3.CrossRefGoogle ScholarPubMed
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978). Receptive field organisation of complex cells in the cat’s striate cortex. The Journal of Physiology 283, 7999.CrossRefGoogle ScholarPubMed
Reichardt, W. (1961). Autocorrelation, a principle for the evaluation of sensory information. In Sensory Communication, ed. Rosenblith, W.A., pp. 303317. New York: Wiley.Google Scholar
Shannon, C.E. & Weaver, W. (1949). The Mathematical Theory of Communication. Urbana, IL: The University of Illinois Press.Google Scholar
Smith, M.A., Bair, W. & Movshon, J.A. (2002). Signals in macaque striate cortical neurons that support the perception of Glass patterns. The Journal of Neuroscience 22, 83348345.CrossRefGoogle ScholarPubMed
Spitzer, H. & Hochstein, S. (1985). A complex cell model. Journal of Neurophysiology 53, 12661286.CrossRefGoogle Scholar
Teller, D.Y. (1980). Locus questions in visual science. In Visual Coding and Adaptability, ed. Harris, C., Hillsdale, NJ: Erlbaum Associates.Google Scholar
Teller, D.Y. (1984). Linking propositions. Vision Research 24, 12331246.CrossRefGoogle ScholarPubMed
Teller, D.Y. & Pugh, E.N. Jr. (1983). Linking propositions in color vision. In Colour Vision: Physiology and Psychophysics, ed. Mollon, J.D. & Sharpe, L.T., London: Academic Press.Google Scholar
Tolman, E.C. (1948). Cognitive maps in rats and men. Psychological Review 55, 189208.CrossRefGoogle ScholarPubMed
Westheimer, G. & McKee, C. (1975). Visual acuity in the presence of retinal-image motion. Journal of the Optical Society of America 65, 847850.CrossRefGoogle ScholarPubMed
Wilson, H.R. & Wilkinson, F. (1998). Detection of global structure in Glass patterns: Implications for form vision. Vision Research 38, 29332947.CrossRefGoogle ScholarPubMed