Hostname: page-component-7c8c6479df-p566r Total loading time: 0 Render date: 2024-03-28T03:33:48.828Z Has data issue: false hasContentIssue false

Space-time spectra of complex cell filters in the macaque monkey: A comparison of results obtained with pseudowhite noise and grating stimuli

Published online by Cambridge University Press:  02 June 2009

James P. Gaska
Affiliation:
Department of Neurology, University of Massachusetts Medical School, Worcester
Lowell D. Jacobson
Affiliation:
Department of Neurology, University of Massachusetts Medical School, Worcester
Hai-Wen Chen
Affiliation:
Department of Neurology, University of Massachusetts Medical School, Worcester
Daniel A. Pollen
Affiliation:
Department of Neurology, University of Massachusetts Medical School, Worcester

Abstract

White noise stimuli were used to estimate second-order kernels for complex cells in cortical area VI of the macaque monkey, and drifting grating stimuli were presented to the same sample of neurons to obtain orientation and spatial-frequency tuning curves. Using these data, we quantified how well second-order kernels predict the normalized tuning of the average response of complex cells to drifting gratings.

The estimated second-order kernel of each complex cell was transformed into an interaction function defined over all spatial and temporal lags without regard to absolute position or delay. The Fourier transform of each interaction function was then computed to obtain an interaction spectrum. For a cell that is well modeled by a second-order system, the cell’s interaction spectrum is proportional to the tuning of its average spike rate to drifting gratings. This result was used to obtain spatial-frequency and orientation tuning predictions for each cell based on its second-order kernel. From the spatial-frequency and orientation tuning curves, we computed peaks and bandwidths, and an index for directional selectivity.

We found that the predictions derived from second-order kernels provide an accurate description of the change in the average spike rate of complex cells to single drifting sine–wave gratings. These findings are consistent with a model for complex cells that has a quadratic spectral energy operator at its core but are inconsistent with a spectral amplitude model.

Type
Research Articles
Copyright
Copyright © Cambridge University Press 1994

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.)

References

Adelson, E.H. & Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A2, 284289.Google Scholar
Albrecht, D.G. & Hamilton, D.B. (1982). Striate cortex of monkey and cat: Contrast response function. Journal of Neurophysiology 48, 217237.Google Scholar
Baker, C.L. & Cynader, M.S. (1986). Spatial receptive field properties of direction-selective neurons in the cat striate cortex. Journal of Neurophysiology 55, 11361152.Google Scholar
Bonds, A.B. (1989). Role of inhibition in specification of orientation selectivity of cells in the cat striate cortex. Visual Neuroscience 2, 4155.Google Scholar
Chen, H.-W., Jacobson, L.D., Gaska, J.P. & Pollen, D.A. (1993). Cross-correlation analyses of nonlinear systems with spatiotempo-ral inputs. IEEE Transactions on Biomedical Engineering 40, 11021113.Google Scholar
DeValois, K.K. & Tootell, R.B. (1983). Spatial-frequency-specific inhibition in cat striate cortex cells. Journal of Physiology (London) 336, 359376.Google Scholar
DeValois, R.L., Albrecht, D.G. & Thorell, L.G. (1982). Spatial frequency selectivity of cells in the macaque visual cortex. Vision Research 22, 545560.Google Scholar
DeValois, R.L., Thorell, L.G. & Albrecht, D.G. (1985). Periodicity of striate-cortex-cell receptive fields. Journal of the Optical Society of America A2, 11151123.Google Scholar
Emerson, R.C., Bergen, J.R. & Adelson, E.H. (1992 a). Direction-ally selective complex cells and the computation of motion energy in cat visual cortex. Vision Research 32, 203218.Google Scholar
Emerson, R.C., Korenberg, M.J. & Citron, M.C. (1992 b). Identification of complex-cell intensive nonlinearities in a cascade model of cat visual cortex. Biological Cybernetics 66, 291300.Google Scholar
Emerson, R.C., Citron, M.C., Vaughn, W.J. & Klein, S.A. (1987). Nonlinear directionally selective subunits in complex cells of cat striate cortex. Journal of Neurophysiology 58, 3365.Google Scholar
Emerson, R.C., Korenberg, M.J. & Citron, M.C. (1989). Identification of intensive nonlinearities in a cascade model of visual cortex and its relation to cell classification. In Advanced Methods of Physiological System Modeling, ed. Marmarelis, V.Z., pp 97111. New York: Plenum Press.Google Scholar
Gilbert, C.D. & Wiesel, T.N. (1990). The influence of contextual stimuli on the orientation selectivity of cells in the primary visual cortex of cat. Vision Research 30, 16891701.Google Scholar
Glezer, V.D., Tscherbach, T.A., Gauselman, V.E. & Bondarko, V.E. (1980). Linear and nonlinear properties of simple and complex cell receptive fields in area 17 of the cat visual cortex. Biological Cybernetics 43, 3549.Google Scholar
Good, I.J. (1971). The relationship between two fast Fourier transforms. IEEE Transactions 20, 310317.Google Scholar
Hammond, P. & MacKay, D.M. (1981). Modulatory influences of moving textured backgrounds on responsiveness of simple cells in feline striate cortex. Journal of Physiology (London) 319, 431442.Google Scholar
Heeger, D.J. (1991). Nonlinear model of the neural responses in cat visual cortex. In Computational Models of Visual Processing, ed. Landy, M. & Movshon, J.A., pp 119133. Cambridge, Massachusetts: MIT Press.Google Scholar
Heeger, D.J. (1992 a). Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 181197.Google Scholar
Heeger, D.J. (1992 b). Half-squaring in responses of cat striate cells. Visual Neuroscience 9, 427443.Google Scholar
Hubel, D. & Wiesel, T. (1962). Receptive fields, binocular interaction, and functional architecture in cat's visual cortex. Journal of Physiology (London) 160, 106154.Google Scholar
Jacobson, L.D., Gaska, J.P., Chen, H.-W. & Pollen, D.A. (1993). Structural testing of multi-input linear-nonlinear cascade models for cells in the macaque striate cortex. Vision Research 33, 609626.Google Scholar
Lee, Y.W. & Schetzen, M. (1965). Measurement of the Wiener kernels of a nonlinear system by cross-correlation. International Journal of Control 2, 237254.Google Scholar
Maffei, L. & Fiorenttni, A. (1973). The visual cortex as a spatial frequency analyzer. Vision Research 13, 12551267.Google Scholar
Maffei, L. & Fiorenttni, A. (1976). The unresponsive regions of visual cortical receptive fields. Vision Research 16, 11311139.Google Scholar
Marmarelis, P.Z. & Marmarelis, V.Z. (1978). Analysis of Physiological Systems: The White Noise Approach. New York: Plenum Press.Google Scholar
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978). Receptive field organization of complex cells in the cat’s striate cortex. Journal of Physiology (London) 283, 7999.Google Scholar
Nelson, J.I. & Frost, B.J. (1978). Orientation-selective inhibition from beyond the classic visual receptive field. Brain Research 139, 359365.Google Scholar
Ohzawa, I., DeAngelis, G.C. & Freeman, R.D. (1990). Stereoscopic depth discrimination in the visual cortex: Neurons ideally suited as disparity detectors. Science 249, 10371041.Google Scholar
Pollen, D.A., Andrews, B.W. & Feldon, S.E. (1978). Spatial frequency selectivity of periodic complex cells in the visual cortex of the cat. Vision Research 18, 907916.Google Scholar
Pollen, D.A., Gaska, J.P. & Jacobson, L.D. (1988). Responses of simple and complex cells to compound sine–wave gratings. Vision Research 28, 2539.Google Scholar
Pollen, D.A. & Ronner, S.F. (1982). Spatial computation performed by simple and complex cells in the visual cortex of the cat. Vision Research 22, 101118.Google Scholar
Pollen, D.A. & Ronner, S.F. (1983). Visual cortical neurons as localized spatial frequency filters. IEEE Transactions on Systems, Man, and Cybernetics 13, 907916.Google Scholar
Press, W.H., Flannery, B.P., Teukolsky, S.A. & Vetterling, W.T. (1988). Numerical Recipes in C: The Art of Scientific Computing, Cambridge, Massachusetts: Cambridge University Press.Google Scholar
Reid, R.C., Victor, J.D. & Shapley, R.M. (1992). Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex. Visual Neuroscience 9, 3945.Google Scholar
Rybicki, G.B., Tracy, D.M. & Pollen, D.A. (1972). Complex cell response depends on interslit spacing. Nature New Biology 240, 7778.Google Scholar
Sclar, G., Maunsell, J.H.R. & Lennle, P. (1990). Coding of image contrast in central visual pathways of the macaque monkey. Vision Research 30, 110.Google Scholar
Skottun, B.C., DeValois, R.L., Grosof, D.H., Movshon, J.A., Albrecht, D.G. & Bonds, A.B. (1991). Classification of simple and complex cells on the basis of response modulation. Vision Research 31, 10791086.Google Scholar
Spitzer, H. & Hochstein, S. (1985 a). Simple- and complex-cell response dependencies on stimulation parameters. Journal of Neurophysiology 53, 12441265.Google Scholar
Spitzer, H. & Hochstein, S. (1985 b). A complex-cell receptive field model. Journal of Neurophysiology 53, 12661286.Google Scholar
Szulborski, R.G. & Palmer, L.A. (1990). The two-dimensional spatial structure of nonlinear subunits in the receptive fields of complex cells. Vision Research 30, 249254.Google Scholar
Szulborski, R.G. & Palmer, L.A. (1991). Linear behavior of complex cell subunits in cat striate cortex. Investigative Ophthalmology and Visual Science (Suppl.) 32, 1253.Google Scholar
Wiener, N. (1958). Nonlinear Problems in Random Theory. New York: The Technology Press of MIT and Wiley.Google Scholar
Wong-Riley, M.T.T. (1979). Changes in the visual system of monocu-larly sutured and enucleated cat demonstrated with cytochrome oxidase histochemistry. Brain Research 171, 1128.Google Scholar