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The use of m-sequences in the analysis of visual neurons: Linear receptive field properties

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 and Neuroscience, New York New York University, Center for Neural Science, New York
J. D. Victor
Affiliation:
The Rockefeller University, Laboratory of Biophysics, New York Cornell University Medical College, Department of Neurology and Neuroscience, New York
R. M. Shapley
Affiliation:
The Rockefeller University, Laboratory of Biophysics, New York New York University, Center for Neural Science, New York
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Abstract

We have used Sutter's (1987) spatiotemporal m-sequence method to map the receptive fields of neurons in the visual system of the cat. The stimulus consisted of a grid of 16 X 16 square regions, each of which was modulated in time by a pseudorandom binary signal, known as an m-sequence. Several strategies for displaying the m-sequence stimulus are presented. The results of the method are illustrated with two examples. For both geniculate neurons and cortical simple cells, the measurement of first-order response properties with the m-sequence method provided a detailed characterization of classical receptive-field structures. First, we measured a spatiotemporal map of both the center and surround of a Y-cell in the lateral geniculate nucleus (LGN). The time courses of the center responses was biphasic: OFF at short latencies, ON at longer latencies. The surround was also biphasic—ON then OFF—but somewhat slower. Second, we mapped the response properties of an area 17 directional simple cell. The response dynamics of the ON and OFF subregions varied considerably; the time to peak ranged over more than a factor of two. This spatiotemporal inseparability is related to the cell's directional selectivity (Reid et al., 1987, 1991; McLean & Palmer, 1989; McLean et al., 1994). The detail with which the time course of response can be measured at many different positions is one of the strengths of the m-sequence method.

Keywords

White noiseReverse correlationCatLateral geniculate nucleus (LGN)Visual cortexSimple cell
Type
Research Articles
Information
Visual Neuroscience , Volume 14 , Issue 6 , November 1997 , pp. 1015 - 1027
Copyright
Copyright © Cambridge University Press 1997

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References

Adelson, E.H. & Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America 2, 285–299.Google ScholarPubMed
Albrecht, D.G. & Geisler, W.S. (1991). Motion selectivity and the contrast-response function of simple cells in the visual cortex. Visual Neuroscience 7, 531–546.CrossRefGoogle ScholarPubMed
Alonso, J.M. & Reid, R.C. (1994). Coupling between neighboring LGN cells: Possible implications for simple receptive fields. Society for Neuroscience Abstracts 20, 1476.Google Scholar
Alonso, J.M., Atick, J.J. & Reid, R.C. (1995). The temporal responses of LGN receptive fields studied with white noise. Investigative Ophthalmology and Visual Science (Suppl.) 36, S. 689.Google Scholar
Baseler, H.A., Sutter, E.E., Klein, S.A. & Carney, T. (1994). The topography of visual evoked response properties across the visual field. Electroencephalography and Clinical Neurophysiology 90, 65–81.CrossRefGoogle ScholarPubMed
Benardete, E.A. & Victor, J.D. (1994). An extension of the m-sequence technique for the analysis of multi-input nonlinear systems. In Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks, ed. Pinter, R. & Nabet, B., pp. 87–110. Cleveland, Ohio: CRC Press.Google Scholar
Britten, K.H. (1995). Spatial interactions within monkey middle temporal (MT) receptive fields. Society for Neuroscience Abstracts 21, 663.Google Scholar
Bussgang, J.J. (1952). Crosscorrelation functions of amplitude-distorted Gaussian signals. Technical Report 216, MIT Research Laboratory of Electronics.Google Scholar
Citron, M.C., Kroeker, J.P. & McCann, G.D. (1981). Nonlinear interactions in ganglion cell receptive fields. Journal of Neurophysiology 46, 1161–1176.CrossRefGoogle ScholarPubMed
Dawis, S., Shapley, R., Kaplan, E. & Tranchina, D. (1984). The receptive field organization of X-cells in the cat: Spatiotemporal coupling and asymmetry. Vision Research 24, 549–561.CrossRefGoogle ScholarPubMed
DeAngelis, G.C., Ohzawa, I., Freeman, R.D. (1993 a). Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. Journal of Neurophysiology 69, 1091–1117.CrossRefGoogle ScholarPubMed
DeAngelis, G.C., Ohzawa, I., Freeman, R.D. (1993 b). Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. Journal of Neurophysiology 69, 1118–1135.CrossRefGoogle ScholarPubMed
DeAngelis, G.C., Ohzawa, I., Freeman, R.D. (1995). Receptive-field dynamics in the central visual pathways. Trends in Neuroscience 618, 451–458.CrossRefGoogle Scholar
de Boer, E. & Kuyper, P. (1968). Triggered correlation. IEEE Transactions on Biomedical Engineering 15, 169–179.CrossRefGoogle ScholarPubMed
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, 33–65.CrossRefGoogle ScholarPubMed
Enroth-Cugell, C. & Robson, J.G. (1966). The contrast sensitivity of retinal ganglion cells of the cat. Journal of Physiology 187, 517–552.CrossRefGoogle ScholarPubMed
Golomb, S.W. (1982). Shift Register Sequences, Laguna Hills, California: Aegean Park Press.Google Scholar
Hochstein, S. & Shapley, R.M. (1976). Quantitative analysis of retinal ganglion cell classifications. Journal of Physiology 262, 237–264.CrossRefGoogle ScholarPubMed
Hubel, D.H. (1957). Tungsten microelectrode for recording from single units. Science 125, 549–550.CrossRefGoogle ScholarPubMed
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. Journal of Physiology 160, 106–154.Google Scholar
Jagadeesh, B., Wheat, H.S. & Ferster, D. (1993). Linearity of summation of synaptic potentials underlying direction selectivity in simple cells of the cat visual cortex. Science 262, 1901–1904.CrossRefGoogle ScholarPubMed
Jones, J.P. & Palmer, L.A. (1987). The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1187–1211.CrossRefGoogle ScholarPubMed
Kitano, M., Niiyama, K., Kasamatsu, T, Sutter, E.E. & Norcia, A.M. (1994). Retinotopic and nonretinotopic field potentials in cat visual cortex. Visual Neuroscience 11, 953–977.CrossRefGoogle ScholarPubMed
Levick, W.R. & Thibos, L.H. (1980). Orientation bias of cat retinal ganglion cells. Nature 286, 389–390.CrossRefGoogle ScholarPubMed
Marmarelis, P.Z. & Marmarelis, V.Z. (1978). Analysis of Physiological Systems. New York: Plenum Press.CrossRefGoogle Scholar
McLean, J. & Palmer, L. (1989). Contributions of linear spatiotemporal receptive field structure to velocity selectivity of simple cells in area 17 of cat. Vision Research 29, 675–679.CrossRefGoogle ScholarPubMed
McLean, J., Raab, S. & Palmer, L.A. (1994). Contribution of linear mechanisms to the specification of local motion by simple cells in areas 17 and 18 of the cat. Visual Neuroscience 11, 271–294.CrossRefGoogle 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 Instrumentation 12, 283–292.CrossRefGoogle Scholar
Movshon, J. A., Thompson, I. D. & Tolhurst, D. J. (1978). Spatial summation in the receptive field of simple cells in the cat's striate cortex. Journal of Physiology 283, 53–77.CrossRefGoogle ScholarPubMed
Naka, K.I., Sakuranaga, M. & Ando, Y.I. (1985). White-noise analysis as a tool in vision physiology. In Progress in Clinical and Biological Research. Vol. 176, Contemporary Sensory Neurobiology, ed. Correia, M.J. & Perachio, A.A., pp. 307–322. New York: Alan R. Liss.Google Scholar
Nuttal, A.H. (1957). Invariance of correlation functions under nonlinear transformations. Technical report, MIT Laboratory of Electronics.Google Scholar
Ohzawa, I. & Freeman, R.D. (1986). The binocular organization of simple cells in the cat's visual cortex. Journal of Neurophysiology 56, 221–242.CrossRefGoogle ScholarPubMed
Reid, R.C. & Alonso, J.M. (1995). Specificity of monosynaptic connections from thalamus to visual cortex. Nature 378, 281–284.CrossRefGoogle ScholarPubMed
Reid, R.C. & Shapley, R.M. (1992). The spatial structure of L, M, and S cone inputs to receptive fields in primate lateral geniculate nucleus. Nature 356, 716–718.CrossRefGoogle Scholar
Reid, R.C., Soodak, R.E. & Shapley, R.M. (1987). Linear mechanisms of directional selectivity in simple cells of cat striate cortex. Proceedings of the National Academy of Sciences of the U.S.A. 84, 8740–8744.CrossRefGoogle ScholarPubMed
Reid, R.C., Soodak, R.E. & Shapley, R.M. (1991). Directional selectivity and spatiotemporal structure of receptive fields of simple cells in cat striate cortex. Journal of Neurophysiology 66, 505–529.CrossRefGoogle ScholarPubMed
Sakai, H.M, Naka, K.-I. & Korenberg, M.J. (1990). White-noise analysis in visual neuroscience. Visual Neuroscience 1, 287–296.CrossRefGoogle Scholar
Shapley, R.M. & Victor, J.D. (1981). How the contrast gain control modifies the frequency responses of cat retinal ganglion cells. Journal of Physiology 318, 161–179.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
Soodak, R.E., Shapley, R.M. & Kaplan, E. (1991). Fine structure of receptive-field centers of X and Y cells of the cat. Visual Neuroscience 6, 621–628.CrossRefGoogle ScholarPubMed
Sutter, E.E. (1987). A practical non-stochastic approach to nonlinear time-domain analysis. In Advanced Methods of Physiological Systems Modeling, Vol. I, ed. Marmarelis, V.Los Angeles, California: University of Southern California.Google Scholar
Sutter, E.E. (1992 a). The fast m-transform: A fast computation of cross-correlations with binary m-sequences. SIAM Journal on Computing 20, 686–694.CrossRefGoogle Scholar
Sutter, E.E. (1992 b). A deterministic approach to nonlinear systems analysis. In Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks, ed. Pinter, R. & Nabet, B., pp. 171–220. Cleveland, Ohio: CRC Press.Google Scholar
Sutter, E.E. & Tran, D. (1992). The field topography of ERG components in man—I. The photopic luminance response. Vision Research 32, 433–446.CrossRefGoogle ScholarPubMed
Sutter, E.E. & Vaegan, . (1990). Lateral interaction component and local luminance nonlinearities in the human pattern ERG. Vision Research 30, 659–671.CrossRefGoogle 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, 249–254.CrossRefGoogle ScholarPubMed
Victor, J.D. & Shapley, R.M. (1979). Receptive field mechanisms of cat X and Y retinal ganglion cells. Journal of General Physiology 74, 275–298.CrossRefGoogle Scholar
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, 3068–3972.CrossRefGoogle ScholarPubMed
Victor, J.D. (1987). The dynamics of the cat retinal X cell centre. Journal of Physiology 386, 219–246.CrossRefGoogle ScholarPubMed
Victor, J.D. (1991). Asymptotic approach of generalized orthogonal functional expansions to Wiener kernels. Annals of Biomedical Engineering 19, 383–399.CrossRefGoogle ScholarPubMed
Victor, J.D. (1992). Nonlinear systems analysis in vision: Overview of kernel methods. In Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks, ed. Pinter, R. & Nabet, B., pp. 1–37. Cleveland, Ohio: CRC Press.Google Scholar
Victor, J.D., Purpura, K., Katz, E. & Mao, B. (1994). Population encoding of spatial frequency, orientation, and color in macaque V1. Journal of Neurophysiology 72, 2151–2166.CrossRefGoogle ScholarPubMed
Victor, J.D. & Knight, B.W. (1979). Nonlinear analysis with an arbitrary stimulus ensemble. Quarterly of Applied Mathematics 37, 113–136.CrossRefGoogle Scholar
Vidyasagar, T.R. & Heide, W. (1984). Geniculate orientation biases seen with moving sine-wave gratings: Implications for a model of simple cell afferent connectivity. Experimental Brain Research 57, 196–200.CrossRefGoogle Scholar
Watson, A.B. & Ahumada, A.J. Jr (1985). Models of human visualmotion sensing. Journal of the Optical Society of America 2, 322–342.CrossRefGoogle ScholarPubMed
Weiss, T. (1966). A model of the peripheral auditory system. Kybernetik 3, 153–175.CrossRefGoogle Scholar
Wiener, N. (1958). Nonlinear Problems in Random Theory. New York: Wiley.Google Scholar