• DocumentCode
    397745
  • Title

    A Volterra approach to dynamic modeling of the visual cortex

  • Author

    Joseph, Jenner J. ; Ghosh, Bijoy K.

  • Author_Institution
    Dept. of Syst. Sci. & Math., Washington Univ., St. Louis, MO, USA
  • Volume
    4
  • fYear
    2003
  • fDate
    4-6 June 2003
  • Firstpage
    3579
  • Abstract
    The visual cortex of a freshwater turtle, when stimulated by a pattern of light, produces waves of activity that have been both recorded experimentally and simulated using a model cortex. The observed wave can be encoded using principal component analysis with respect to a set of spatial and temporal basis functions. The encoding process generates a vector time series of coefficients (temporal strands) in a suitable lower dimensional beta-space. The goal of this paper is to reproduce the temporal strands as an output of a non-linear, time-invariant, input-output dynamical system. The main result of this paper is to show that Volterra series can be used effectively for this purpose. We show this by conducting two separate simulations. In the first simulation, we stimulate the cortex with a flash of light that is spatially uniform but temporally varies cosinusoidally. In the second simulation, we consider input stimulations that are spatially restricted from the left, right, and center of the visual field. The simulations have been conducted by considering a single flash or a pair of flashes simultaneously applied to any of the above three locations of the visual field. Temporally, the flashes continue to vary cosinusoidally within a range of frequencies or frequency pairs.
  • Keywords
    Volterra series; neurophysiology; physiological models; principal component analysis; visual perception; Volterra approach; beta-space; dynamic modeling; encoding process; flashing light; input-output dynamical system; light patterns; model cortex; neuroscience; nonlinear dynamical system; spatial basis function; temporal basis function; temporal strand; time-invariant dynamical system; varies cosinusoidally; vector time series; visual cortex; visual field; Brain modeling; Encoding; Frequency; Kernel; Mathematical model; Neuroscience; Nonlinear systems; Principal component analysis; USA Councils; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 2003. Proceedings of the 2003
  • ISSN
    0743-1619
  • Print_ISBN
    0-7803-7896-2
  • Type

    conf

  • DOI
    10.1109/ACC.2003.1244104
  • Filename
    1244104