Brain and Mind

, Volume 1, Issue 3, pp 327–349

Maps of Surface Distributions of Electrical Activity in Spectrally Derived Receptive Fields of the Rat's Somatosensory Cortex

  • Joseph S. King
  • Mix Xie
  • Bibo Zheng
  • Karl H. Pribram


This study describes the results of experiments motivated by an attempt to understand spectral processing in the cerebral cortex (DeValois and DeValois, 1988; Pribram, 1971, 1991). This level of inquiry concerns processing within a restricted cortical area rather than that by which spatially separate circuits become synchronized during certain behavioral and experiential processes. We recorded neural responses for 55 locations in the somatosensory (barrel) cortex of the rat to various combinations of spatial frequency (texture) and temporal frequency stimulation of their vibrissae. The recordings obtained from single and multi-unit bursts of spikes were mapped as surface distributions of local dendritic potentials. The distributions showed a variety of patterns that are asymmetric with respect to the spatial and temporal parameters of stimulation, and were, therefore, not simply reflecting whisker flick rate. Next, a simulation of our results showed that these surface distributions of local dendritic potentials can be described by Gabor-like functions much as in the visual system. The results provide support for a model of distributed cortical processing that imposes a physiologically derived frame (the limited extent of a dendritic patch) and an anatomically derived (axonal) sampling of the distributed process. This combination provides a complex Gabor wavelet that encodes phase, which is necessary to processing such details as edges and texture in a scene. The synchronization across cortical areas that make the Gabor wavelet processes within restricted cortical areas available to one another (the binding problem) proceed at a ''higher order'' level of integration. Both levels of distributed processing accomplish computation in the conjoint spacetime and spectral domain.

Gabor wavelets holography phase space receptive fields 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahissar, E., Alkon, G., Zacksenhouse, M. and Haidarliu, S., 1996: Cortical somatosensory oscillators and the decoding of vibrissal touch, Abstracts of the Society for Neuroscience 26th Annual Meeting 22(1), 18, Society for Neuroscience, Proc Abst 16.5, Washington, D.C.Google Scholar
  2. Armstrong-James, M., 1995: The nature and plasticity of sensory processing within adult rat barrel cortex, Journal of Neurophysiology 41(3), 333–373.Google Scholar
  3. Barcala, L.A., Nicolelis, M.A.L. and Chapin, J.K., 1993: Quantifying the connectivity properties underlying the dynamics of the rodent trigeminal network (Abstract), Society For Neuroscience Abstracts: 23rd Annual Meeting 19(1).Google Scholar
  4. Barrett, T.W., 1973: Comparing the efficiency of sensory systems: A biophysical approach, Journal of Biological Physics 1(3), 175–192.CrossRefGoogle Scholar
  5. Barrett, T.W., 1969: The cortex as inferferometer: The transmission of amplitude, frequency, and phase in cortical structures, Neuropsychologia 7, 135–148.CrossRefGoogle Scholar
  6. Bell, A.J. and Sejnowski, T.J., 1996: Learning the higher-order structure of natural sound, Computation in Neural Systems 7, 261–266.CrossRefGoogle Scholar
  7. Bovik, A.C., Clark,M. and Geisler,W.S., 1990: Multichannel texture analysis using localized spatial filters, IEEE Trans. Pattern Analysis and Machine Intelligence 12(1), 55–73.CrossRefGoogle Scholar
  8. Bracewell, R.N., 1989: The Fourier transform, Scientific American, 86–95.Google Scholar
  9. Bressler, S., 1994: Dynamic self organization in the brain as observed by transient cortical coherence, in K.H. Pribram (ed.), Origins: Brain and Self Organization, Lawrence Erlbaum Associates, Inc., Hillsdale, NJ.Google Scholar
  10. Carlton, E.H., 1988: Connection between internal representation of rigid transformation and cortical activity paths, Biological Cybernetics 59, pp. 419–429.PubMedCrossRefGoogle Scholar
  11. Carvell, G.E. and Simons, D.J., 1990: Biometric analyses of vibrissal tactile discrimination in the rat, Journal of Neuroscience 10, 2638–2648.PubMedGoogle Scholar
  12. Chapin, J.K., Markowitz, R.S. and Nicolelis, M.A.L., 1996: Simultaneous neuronal ensemble recordings at multiple trigeminal system levels: selective cortical responsiveness to active discriminative whisking, Abstracts of the Society for Neuroscience 26th Annual Meeting 22(1), 18. Society for Neuroscience, Proc Abst 16.6, Washington, D.C.Google Scholar
  13. Chapin, J.K. and Nicolelis,M.A.L., 1995: Beyond single unit recording: Characterizing neural information in networks of simultaneously recorded neurons, in J.S. King and K.H. Pribram (eds), Scale in Conscious Experience: Is the Brain too Important to be Left to Specialists to Study?, Lawrence Erlbaum Associates, New Jersey, pp. 133–153.Google Scholar
  14. Churchland, P.S., 1986: Neurophilosophy: Toward a Unified Science of the Mind/Brain, MIT Press, Cambridge.Google Scholar
  15. Crick, F.H.C., 1994: The Astonishing Hypothesis: The Scientific Search for the Soul, Charles Scribner's Sons, New York.Google Scholar
  16. Daugman, J.G., 1993: Quadriture-phase simple-cell pairs are appropriately described in complex analytic form, Journal of the Optical Society of American 10(7), 375–377.CrossRefGoogle Scholar
  17. Daugman, J.G., 1990: An information-theoretic view of analog representation in striate cortex, in E. Schwartz (ed.), Computational Neuroscience, MIT Press, Cambridge, MA.Google Scholar
  18. Daugman, J.G., 1985: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, Journal of the Optical Society of America 2(7), 1,160–1,169.Google Scholar
  19. Daugman, J.G., 1980: Two-dimensional spectral analysis of cortical receptive field profile, Vision Research 20, 847–856.PubMedCrossRefGoogle Scholar
  20. DeValois, R.L. and DeValois, K.K., 1988: Spatial Vision (Oxford Psychology Series No. 14), Oxford University Press, New York.Google Scholar
  21. Favorov, O.V. and Kelly, D.O., 1994a: Minicolumnar organization within somatosensory cortical segregates: I. Development of afferent connections, Cerebral Cortex 4(4), 408–427.PubMedGoogle Scholar
  22. Favorov, O.V. and Kelly, D.O., 1994b: Minicolumnar organization within somatosensory cortical segregates: II. Emergent functional properties, Cerebral Cortex 4(4), 428–442.PubMedGoogle Scholar
  23. Fitzgerald, R., 1999: Phase synchronization may reveal communication pathways in brain activity, Physics Today, March 18, 1999, pp. 17–19.Google Scholar
  24. Gabor, D., 1948: A new microscopic principle, Nature 161, 777–778.PubMedGoogle Scholar
  25. Gabor, D., 1946: Theory of communication, Journal of the Institute of Electrical Engineers 93, 429–441.Google Scholar
  26. Gaska, J.P., Jacobson, L.D., Chen, H.W. and Pollen, D.A., 1994: Space-time spectra of complex cell filters in the macaque monkey: A comparison of results obtained with pseudowhite noise and grating stimuli, Visual Neuroscience II, 805–821.Google Scholar
  27. Georgopoulos, A.P., Taira, M. and Lukasin, A., 1993: Cognitive neurophysiology of the motor cortex, Science 260, 47–52.PubMedGoogle Scholar
  28. Glezer, V.D., 1995: Vision and Mind, Lawrence Erlbaum Associates, Inc., Mahwah, NJ.Google Scholar
  29. Hashemiyoon, R. and Chapin, J., 1996: What visual stimulus parameters control the amplitude and spatiotemporal phase patterning of oscillations in the subcortical visual system? in Abstracts of the Society for Neuroscience 26th Annual Meeting 22(2), 1605. Society for Neuroscience, Proc Abst 631.2, Washington, D.C.Google Scholar
  30. Jones, J.P. and Palmer, L.A., 1987: An evaluation of the two-dimensional Gabor filter model of simple receptive fields in the cat striate cortex, Journal of Neurophysiology 58, 1,233–1,258.Google Scholar
  31. Julez, B. and Pennington, K.S., 1965: Equidistributed information mapping: An analogy to holograms and memory, Journal of the Optical Society of America 55, 605.Google Scholar
  32. Kamen, E.W., 1990: Introduction to Signals and Systems (2nd ed.), MacMillian, Englewood Cliffs.Google Scholar
  33. King, J.S., SantaMaria, M.P., Hovis, S. and Pribram, K.H., in Prep.: An analysis of unit responses in the barrel cortex of rate to “passive” and “active” vibrissal stimulation.Google Scholar
  34. Kjaer, T.W., Hertz, J.A. and Richmond, B.J., 1994: Decoding cortical neuronal signals: Network models, information estimation and spatial tuning, Journal of Computational Neuroscience 1, 109–139.PubMedCrossRefGoogle Scholar
  35. Kuffler, S.W., 1953: Discharge patterns and functional organization of mammalian retina, Journal of Neurophysiology 16, 37–69.PubMedGoogle Scholar
  36. Lachaux, J., Rodriguez, E., Martinerie, J. and Varela, F., in press: Measuring phase-synchrony in brain signals, Human Brain Mapping.Google Scholar
  37. Lades, M., Vorbruggen, J.C., Buhmann, J., Lange, J., v.d. Malsburg, C., Wurtz, R.P. and Konen, W., 1993: Distortion invariant object recognition in the dynamic link architecture, IEEE Transactionson Computers 42(3), 300–311.CrossRefGoogle Scholar
  38. Lassonde, M., Ptito, M. and Pribram, K., 1981: Intracerebral influences on the microstructure of receptive fields of cat visual cortex, Experimental Brain Research 43, 131–144.CrossRefGoogle Scholar
  39. Lee, T.S., 1996: Image representation usng 2D Gabor wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 959–971.CrossRefGoogle Scholar
  40. Lee, T.S., Mumford, D. and Yuille, A.L., 1992: Texture segmentation by minimizing vector-valued energy functionals: The couple-membrane model, in G. Sandini (ed.), Lecture Notes in Computer Science 588, 165–173, Computer Vision ECCV’ 92, Springer-Verlag.Google Scholar
  41. Leith, E.N. and Upatnicks, J., 1965: Photography by laser, Scientific American 212, 24–35.CrossRefGoogle Scholar
  42. Marcelja, S., 1980: Mathematical description of the responses of simple cortical cells, Journal of the Optical Society of America 70, 1,297–1,300.CrossRefGoogle Scholar
  43. McLaughlin, D.F., Sonty, R.V. and Juliano, S.L., 1996: Multiple representations of ferret forepaw revealed by cortical evoked potentials: normal and reorganized cortex, Abstracts of the Society for Neuroscience 26th Annual Meeting 22(1), 18. Society for Neuroscience, Proc Abst 16.7, Washington, D.C.Google Scholar
  44. Motter, B.C., Steinmetz, M.A., Duffy, C.J. and Mountcastle, V.B., 1987: Functional properties of parietal visual neurons: Mechanisms of directionality along a single axis, Journal of Neuroscience 7(1), 154–176.PubMedGoogle Scholar
  45. Nicolelis, M.A.L., Carswell, B., Oliveira, L.M.O., Ghazanfar, A.A., Chapin, J.K., Lin, R.C.S., Nelson, R.J. and Kaas, J.H., 1996: Long-term simultaneous recordings of neuronal ensembles across multiple cortical areas in behaving primates, Abstracts of the Society for Neuroscience 26th Annual Meeting 22(3),2023. Society for Neuroscience, Proc Abst 795.10, Washington, D.C.Google Scholar
  46. Okajima, K., 1998: Two-dimensional Gabor-type receptive field as derived by mutual information maximization, Neural Networks 11, 441–447.PubMedCrossRefGoogle Scholar
  47. Openheim, A.V. and Schafer, R.W., 1989: Discrete Time Signal Processing, Prentice Hall, Englewood Cliffs.Google Scholar
  48. Paradisio, M.A., Kim, W. and Nayak, S., 1996: Cortical representation of surface brightness: influences from beyond the classical receptive field, Abstracts of the Society for Neuroscience 26th Annual Meeting 22(2), 951. Society for Neuroscience, Proc Abst 376.6, Washington, D.C.Google Scholar
  49. Pollen, D.A., 1971: How does the striate cortex begin the reconstruction of the visual world? Science 173, 74–77.PubMedGoogle Scholar
  50. Pollen, D.A., and Gaska, J.P., 1997: Vision, visual cortex and conjoint space-spatial frequency analysis, in G. Adelman and B Smith (eds), Encyclopedia of Neuroscience.Google Scholar
  51. Pollen, D.A. and Ronner, S.F., 1981: Phase relationship between adjacent simple cells in the visual cortex, Science 212, 1409–1411.PubMedGoogle Scholar
  52. Pollen, D.A. and Taylor, J.H., 1974: The striate cortex and the spatial analysis of visual space, in F.O. Schmitt and F.G. Worden (eds), The Neurosciences Third Study Program, The MIT Press, Cambridge, pp. 239–247.Google Scholar
  53. Pribram, K.H., 1998: Afterword. Brain and Values: Is a Biological Science of Values Possible, Lawrence Erlbaum Associates, Inc., Mahwah, Jew Jersey, pp. 551–558.Google Scholar
  54. Pribram, K.H., 1997: The deep and surface structure of memory and conscious learning: Toward a 21st century model, in Robert L. Solso (ed.), Mind and Brain Sciences in The 21st Century, MIT Press, Cambridge, pp. 127–156.Google Scholar
  55. Pribram, K.H., 1991: Brain and Perception: Holonomy and Structure in Figural Processing, Lawrence Erlbaum Associates, New Jersey.Google Scholar
  56. Pribram, K.H., 1971: Languages of the Brain: Experimental Paradoxes and Principles in Neuropsychology, Prentice-Hall, Englewood Cliffs, NJ; Brooks/Cole 1977, Monterey, CA; Brandon House, 1982, New York.Google Scholar
  57. Pribram, K.H., 1966: Some dimensions of remembering: Steps toward a neuropsychological model of memory, in J. Gaito (ed.), Macromolecules and behavior, Academic Press, New York, pp. 165–187.Google Scholar
  58. Pribram, K.H., 1969: Four R's of remembering, in K.H. Pribram (ed.), The Biology of Learning, Harcort, Brace & World, New York, pp. 191–225.Google Scholar
  59. Pribram, K.H. and Carlton, E.H., 1986: Holonomic brain theory in imaging and object perception, Acta Psychologica 63, 175–210.PubMedCrossRefGoogle Scholar
  60. Pribram, K.H., Newer, M. and Baron, R.J., 1973: The holographic hypothesis of memory structure in brain function and perception, in R.C. Atkinson, D.H. Krantz, R.C. Luce and P. Suppes (eds), Contemporal Developments in Mathematical Psychology, W.H. Freeman, New York, pp. 416–457.Google Scholar
  61. Richmond, B.J. and Optican, L.M., 1987: Temporal encoding of two-dimensional patterns of single units in primate inferior temporal cortex. II. Quantifications of response waveform, Journal of Neurophysiology 57(1), 147–161.PubMedGoogle Scholar
  62. Robson, J.G., 1975: Receptive field: Neural representation of the spatial and intensive attributes of the visual image, in EC Carterette (ed.), Handbook of Perception, Vol. V, Seeing, Academic Press, New York, pp. 81–116Google Scholar
  63. Saul, A.B. and Humphrey, A.L., 1992a: Evidence of input from lagged cells in the lateral geniculate nucleus to simple cells in cortical Area 17 of the cat, Journal of Neurophysiology 68(4), 1190–1208.PubMedGoogle Scholar
  64. Saul, A.B. and Humphrey, A.L., 1992b: Temporal-frequency tuning of direction selectivity in cat visual cortex, Visual Neuroscience 8, 365–372.PubMedCrossRefGoogle Scholar
  65. Saul, A.B. and Humphrey, A.L., 1990: Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus, Journal of Neurophysiology 64(1), 206–224.PubMedGoogle Scholar
  66. Shepherd, G.M., Brayton, R.K., Miller, J.P., Segey, I., Rindsel, J. and Rall, W., 1985: Signal enhancement in distal cortical dendrites by means of interactions between active dendritic spines, Proceedings of the National Academy of Sciences 82, pp. 2192–2195.CrossRefGoogle Scholar
  67. Simons, D.J., 1995: Neuronal integration in the somatosensory whisker/barrel cortex, in E.G. Jones and A. Peters (eds), Cerebral Cortex, 11, Plenum Press, New York.Google Scholar
  68. Simons, D.J., 1978: Response properties of virbrissa units in rat SI somatosensory neocortex, Journal of Neurophysiology 41(3), 263–297.Google Scholar
  69. Spinelli, D.N. and Pribram, K.H., 1967: Changes in visual recovery functions and unit activity produced by frontal and temporal cortex stimulation, Electronenceph. Clin. Neurophysiol 22, 143–149.CrossRefGoogle Scholar
  70. Steinmetz, M.A., Motter, B.C., Duffy, C.J. and Mountcastle, V.B., 1987: Functional properties of parietal visual neurons: radial organization of directionalities within the visual field, The Journal of Neuroscience 7(1), 177–191.PubMedGoogle Scholar
  71. Van Heerden, P.J., 1970a: Models for the brain, Nature 225, 177–178.PubMedCrossRefGoogle Scholar
  72. Van Heerden, P.J., 1970b: Models for the brain, Nature 227, 410–411.PubMedCrossRefGoogle Scholar
  73. Van Heerden, P.J., 1968: The Foundations of Empirical Knowledge, N.V. Uitgeverij-Wassenaar, The Netherlands.Google Scholar
  74. Van Heerden, P.J., 1963: A new method of storing and retrieving information, Applied Optics 2, 387–392.Google Scholar
  75. Verzeano, M., Laufer, M., Spear, P. and McDonald, S., 1970: The activity of neuronal networks in the thalamus of the monkey, in K.H. Pribram and D.E. Broadbent (eds), Biology of Memory, Academic, New York, pp. 239–271.Google Scholar
  76. Vidyasagar, T.R. and Henry, G.H., 1996: Spatially selective attention gates neuronal responses in macaque, Abstracts of the Society for Neuroscience 26th Annual Meeting, Vol. 22, Part 2. Society for Neuroscience, Proc Abst 376.13, Washington, D.C., p. 953.Google Scholar
  77. Von der Heydt, R., Peterhans, E. and Duersteler, M.R., 1992: Periodic-pattern-selective cells in monkey visual cortex, Journal of Neuroscience 12, 1416–1434.PubMedGoogle Scholar
  78. Willshaw, D.J., Buneman, O.P. and Longuet-Higgins, H.C., 1969: Non-holographic associative memory, Nature 222, 960–962.PubMedCrossRefGoogle Scholar
  79. Xie, M., Pribram, K.H. and King, J., 1994: Are Neural Spike Trains Deterministically Chaotic or Stochastic Processes?, in Origins: Brain & Self Organization, Lawrence Erlbaum Associates, Inc., New Jersey, pp. 253–267.Google Scholar
  80. Zeevi, Y.Y. and Daugman, J.G., 1981: Some psychophysical aspects of visual processing of displayed information, Proceedings of the Image II Conference, Phoenix, AZ.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Joseph S. King
    • 1
  • Mix Xie
    • 2
  • Bibo Zheng
    • 2
  • Karl H. Pribram
    • 3
    • 4
  1. 1.Center for Brain ResearchRadford UniversityRadfordU.S.A.
  2. 2.GE Medical SystemsUSA
  3. 3.Stanford University
  4. 4.Georgetown UniversityUSA

Personalised recommendations