Hostname: page-component-77f85d65b8-g98kq Total loading time: 0 Render date: 2026-03-27T19:42:53.799Z Has data issue: false hasContentIssue false
  • English
  • Français

Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex

Published online by Cambridge University Press:  02 June 2009

R.C. Reid ,
J.D. Victor  and
R.M. Shapley
R.C. Reid
Affiliation:
The Rockefeller University, Laboratory of Biophysics, New York Cornell University Medical College, Department of Neurology, New York New York University, Center for Neural Science, New York
J.D. Victor
Affiliation:
Cornell University Medical College, Department of Neurology, New York
R.M. Shapley
Affiliation:
The Rockefeller University, Laboratory of Biophysics, New York New York University, Center for Neural Science, New York
Get access Rights & Permissions [Opens in a new window]

Abstract

We have studied the responses of striate cortical neurons to stimuli whose contrast is modulated in time by either a single sinusoid or by the sum of eight sinusoids. The sum-of-sinusoids stimulus resembles white noise and has been used to study the linear and nonlinear dynamics of retinal ganglion cells (Victor et al., 1977). In cortical neurons, we have found different linear and second-order responses to single-sinusoid and sum-of-sinusoids inputs. Specifically, while the responsivity near the optimal temporal frequency is lower for the sum-of-sinusoids stimulus, the responsivity at higher temporal frequencies is relatively greater. Along with this change in the response amplitudes, there is a systematic change in the time course of responses. For complex cells, the integration time, the effective delay due to a combination of actual delays and low-pass filter stages, changes from a median of 85 ms with single sinusoids to 57 ms with a sum of sinusoids. For simple cells, the integration times for single sinusoids range from 44–100 ms, but cluster tightly around 40 ms for the sum-of-sinusoids stimulus. The change in time constant would argue that the increased sensitivity to high frequencies cannot be explained by a static threshold, but must be caused by a fundamental alteration in the response dynamics. These effects are not seen in the retina (Shapley & Victor, 1981) and are most likely cortical in origin.

Keywords

Visual cortexCatDynamicsNonlinearIntegration time

Information

Type
Research Articles
Information
Visual Neuroscience , Volume 9 , Issue 1 , July 1992 , pp. 39 - 45
Copyright
Copyright © Cambridge University Press 1992

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.)

Article purchase

Temporarily unavailable

References

Brenner, D., Shapley, R. & Kaplan, E. (1981). Temporal integration in the visual system of the cat and human. Society for Neuroscience Abstracts 7, 59, 16.Google Scholar
Dawis, S., Shapley, R., Kaplan, E. & Tranchina, D. (1984). The receptive field organization of X-cells in the cat: Spatio-temporal coupling and asymmetry. Vision Research 24, 549–561.CrossRefGoogle Scholar
Dean, A.F., Tolhurst, D.J. & Walker, N.S. (1982). Non-linear temporal summation by simple cells in cat striate cortex demonstrated by failure of superposition. Experimental Brain Research 45, 456–458.CrossRefGoogle ScholarPubMed
Derrington, A.M. & Lennie, P. (1984). Spatial And Temporal Contrast Sensitivity Of Neurons In Lateral Geniculate Nucleus Of Macaque. Journal of Physiology 357, 219–240.CrossRefGoogle ScholarPubMed
Foster, K.H., Gaska, J.P., Nagler, M. & Pollen, D.A. (1985). Spatial and temporal frequency selectivity of neurons in visual cortical areas V1 and V2 of the macaque monkey. Journal of Physiology 365, 331–363.Google Scholar
Hamilton, D.B., Albrecht, D.G. & Geisler, W.S. (1989). Visual cortical receptive fields in monkey and cat: Spatial and temporal phase transfer function. Vision Research 29, 1285–1308.Google Scholar
Henry, G.H., Harvey, A.R. & Lund, J.S. (1979). The afferent connections and laminar distribution of cells in the cat striate cortex. Journal of Comparative Neurology 187, 725–744.Google Scholar
Hubel, D.H. (1957). Tungsten microelectrode for recording from single units. Science 125, 459–550.CrossRefGoogle ScholarPubMed
Ikeda, H. & Wright, M.J. (1975). Spatial and temporal properties of ‘sustained’ and ‘transient’ neurones in area 17 of the cat's visual cortex. Experimental Brain Research 22, 363–383.Google Scholar
Lee, B.B., Martin, P.R. & Valberg, A. (1989). Sensitivity of macaque retinal ganglion cells to chromatic flicker. Journal of Physiology 414, 223–243.Google Scholar
Milkman, N., Schick, G., Rossetto, M., Ratliff, F., Shapley, R. & Victor, J. (1980). A two-dimensional computer-controlled visual stimulator. Behavior Research Methods and 'nstrumentation 12, 283–292.CrossRefGoogle Scholar
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978a). Receptive field organization of complex cells in the cat's striate cortex. Journal of Physiology 283, 79–99.CrossRefGoogle ScholarPubMed
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978b). Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat's visual cortex. Journal of Physiology 283, 101–120.CrossRefGoogle ScholarPubMed
Ogawa, T., Ito, S. & Kato, H. (1981). Membrane characteristics of visual cortical neurons in in vitro slices. Brain Research 226, 315–319.CrossRefGoogle ScholarPubMed
Ohzawa, I., Sclar, G. & Freeman, R.D. (1985). Contrast gain-control in the cat's visual system. Journal of Neurophysiology 54, 651–667.CrossRefGoogle ScholarPubMed
Reid, R.C. & Shapley, R.M. (1988). Complex temporal stimuli increase relative sensitivity of cat striate cortical neurons to high temporal frequencies. Investigative Ophthalmology and Vision Science (Suppl.) 29, 23.Google Scholar
Reid, R.C., Soodak, R.E. & Shapley, R.M. (1991). Directional selectivity and the spatiotemporal structure of the receptive fields of simple cells in cat striate cortex. Journal of Neurophysiology 66, 505–529.CrossRefGoogle ScholarPubMed
Shapley, R.M. & Victor, J.D. (1978). The effect of contrast on the transfer properties of cat retinal ganglion cells. Journal of Physiology 285, 275–298.CrossRefGoogle ScholarPubMed
Shapley, R.M. & Victor, J.D. (1981). How the contrast gain control modifies the frequency response of cat retinal ganglion cells. Journal of Physiology, 318, 161–179.Google Scholar
Spitzer, H. & Hochstein, S. (1985). Simple and complex-cell response dependences on stimulation parameters. Journal of Neurophysioiogy,53, 1244–1265.CrossRefGoogle ScholarPubMed
Stafstrom, C.E., Schwindt, P.C., Flatman, J.A. & Crill, W.E. (1984). Properties of subthreshold response and action potential recorded in layer V neurons from cat sensorimotor cortex in vitro. Journal of Neurophysiology 52, 244–263.Google Scholar
Stratford, K., Mason, A., Larkman, A., Major, G. & Jack, J. (1990). The modelling of pyramidal neurones in the visual cortex. In The Computing Neuron, ed. Durbin, R., Miall, C. & Mitchison, G., pp. 296–321. Wokingham, England: Addison-Wesley.Google Scholar
Tolhurst, D.J., Walker, N.S., Thompson, I.D. & Dean, A.F. (1980). Non-linearities of temporal summation in neurones in area 17 of the cat. Experimental Brain Research 38, 431–435.CrossRefGoogle ScholarPubMed
Victor, J.D., Shapley, R.M. & Knight, B.W. (1977). Nonlinear analysis of cat retinal ganglion cells in the frequency domain. Proceedings of the National Academy of Sciences of the U.S.A. 74, 3068–3072.Google Scholar
Victor, J.D. (1987). The Dynamics Of The Cat Retinal X Cell Centre. Journal of Physiology 386, 219–246.Google Scholar