Experimental Brain Research

, Volume 80, Issue 1, pp 129–134 | Cite as

An analysis of neural spike-train distributions: determinants of the response of visual cortex neurons to changes in orientation and spatial frequency

  • D. Berger
  • K. Pribram
  • H. Wild
  • C. Bridges
Article

Summary

A previously unexploited method of examining neural spike-trains was applied to data obtained from cells in the visual cortex. Distributions of interspike intervals recorded extracellularly from cat visual cortex under four conditions were analyzed. Stimuli were gratings differing in orientation and spatial frequency. The probability density function of first passage time for a random walk with drift process, which is defined by its barrier height and drift coefficient, was used to characterize the generating process of axonal discharge under resting and stimulus conditions. Drift coefficient and barrier height were derived from the sample mean and standard deviation of the measured inter-spike intervals. For cells with simple receptive fields, variations in the drift coefficient were produced by changes in orientation and spatial frequency. Variations in barrier height were produced only by changes in orientation of the stimulus.

Key words

Visual cortex Spike train analysis Cat 

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References

  1. Daugman JG (1988) Complete discrete 2-D Gabor transforms by neural networks for image analysis land compression. IEEE Trans Acoustics, Speech, Signal Prøcessing 7:1169–1179Google Scholar
  2. De Valois RL, De Valois KK (1988) Spatial vision. Oxford University Press, New YorkGoogle Scholar
  3. Gerstein GL, Mandelbrot B (1964) Random walk models for the spike activity of a single neuron. Biophys J 4:41–68Google Scholar
  4. Harrison JM (1985) Brownian motion and stochastic flow systems. John Wiley, New YorkGoogle Scholar
  5. Johnson NL, Kotz S (1970) Distributions in statistics: continuous univariate distributions 1. Houghton Mifflin, BostonGoogle Scholar
  6. Karlin S, Taylor HM (1975) A first course in stochastic processes. Academic Press, New YorkGoogle Scholar
  7. Kryukov VI (1976) Wald's identity and random walk models for neuron firing. Adv Appl Prob 8:257–277Google Scholar
  8. Lansky P (1983) Inferences for the diffusion models of neuronal activity. Math Biosci 67:247–260Google Scholar
  9. Lansky P, Radil T (1987) Statistical inference of spontaneous neuronal discharge patterns. I. Single neuron. Biol Cybern 55:299–311Google Scholar
  10. Lansky P, Lanska V (1987) Diffusion approximation of the neural model with synaptic reversal potentials. Biol Cybern 56:19–26Google Scholar
  11. Marcelja S (1980) Mathematical description of the responses of simple cortical cells. J Opt Soc 70:1297–1300Google Scholar
  12. Pribram KH, Carlton EH (1986) Holonomic brain theory in imaging and object perception. Acta Psychologica 63:175–210Google Scholar
  13. Pribram KH, Lassonde MC, Ptito M (1981) Classification of receptive field properties. Exp Brain Res 43:119–130Google Scholar
  14. Ramoa AS, Shadlen M, Skottun BC, Freeman RD (1986) A comparison of inhibition in orientation and spatial frequency selectivity of cat visual cortex. Nature 321:237–239Google Scholar
  15. Sillito AM, Kemp JA, Milson JA, Berardi N (1980) A re-evaluation of the mechanisms underlying simple cell orientation selectivity. Brain Res 194:517–520Google Scholar
  16. Tuckwell HC (1976) On the first-exit time problem for temporally homogeneous Markov processes. J Appl Prob 13:39–48Google Scholar
  17. Tuckwell HC, Cope DK (1980) Accuracy of neuronal interspike times calculated from a diffusion approximation. J Theor Biol 83:377–387Google Scholar
  18. Tuckwell HC (1986) Stochastic equations for nerve membrane potential. J Theor Neurobiol 5:87–99Google Scholar
  19. Wasan MT (1969) First passage time distribution of Brownian motion with positive drift (inverse Gaussian distributions). Queen's Paper in Pure and Applied Mathematics no 19. Queen's University, Kingston, Ontario, CanadaGoogle Scholar
  20. Yang GL, Chen TC (1978) On statistical methods in neuronal spike-train analysis. Math Biosci 38:1–34Google Scholar

Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • D. Berger
    • 1
  • K. Pribram
    • 1
  • H. Wild
    • 1
  • C. Bridges
    • 1
  1. 1.Center for Brain Research and Informational Sciences, Radford UniversityRadfordUSA

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