Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T06:16:00.497Z Has data issue: false hasContentIssue false
  • English
  • Français

Contribution of linear mechanisms to the specification of local motion by simple cells in areas 17 and 18 of the cat

Published online by Cambridge University Press:  02 June 2009

J. McLean ,
S. Raab  and
L. A. Palmer
J. McLean
Affiliation:
Department of Neuroscience and Mahoney Institute of Neurological Sciences, University of Pennsylvania, Philadelphia
S. Raab
Affiliation:
Department of Neuroscience and Mahoney Institute of Neurological Sciences, University of Pennsylvania, Philadelphia
L. A. Palmer
Affiliation:
Department of Neuroscience and Mahoney Institute of Neurological Sciences, University of Pennsylvania, Philadelphia
Get access Rights & Permissions [Opens in a new window]

Abstract

A reverse correlation technique, which permits estimation of three-dimensional first-order properties of receptive fields (RFs), was applied to simple cells in areas 17 and 18 of cat. Two classes of simple cells were found. For one class, the spatial and temporal RF characteristics were Separable, i.e. they could be synthesized as the product of spatial and temporal weighting functions. RFs in the other class were Inseparable, i.e. bright and dark subregions comprising each field were obliquely oriented in space-time. Based on a linear superposition model, these observations led to testable hypotheses: (1) simple cells with separable space-time characteristics should be speed but not direction selective and (2) simple cells with inseparable space-time characteristics should be direction selective and the optimal velocity of moving stimuli should be predictable from the slope of the oriented subregions. These hypotheses were tested by comparing responses to moving bars with those predicted by application of the convolution integral. Linear predictions accounted for waveforms of responses to moving bars in detail. For cells with oriented space-time characteristics, the preferred direction was always predicted correctly and the optimal speed was predicted quite well. Most cells with separable space-time characteristics were not direction selective as predicted. The major discrepancies between measured and predicted behavior were twofold. First, 8/32 cells with separable space-time RFs were direction selective. Second, predicted directional indices were weakly correlated with actual measurements. These conclusions hold for simple cells in both areas 17 and 18. The major difference between simple RFs in these areas is the coarser spatial scale seen in area 18. These results demonstrate a significant linear contribution to the speed and direction selectivity of simple cells in areas 17 and 18. Where additional, nonlinear mechanisms are inferred, they appear to act synergistically with the linear mechanism.

Keywords

Visual cortexSimple cellsDirection selectivityLinear mechanisms
Type
Research Articles
Information
Visual Neuroscience , Volume 11 , Issue 2 , March 1994 , pp. 271 - 294
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). Spatioteraporal energy models for the perception of motion. Journal of the Optical Society of America A2, 284–299.CrossRefGoogle 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
Baker, C.L. & Cynader, M.S. (1988). Space-time separability of direction selectivity in cat striate cortex neurons. Vision Research 28, 239–246.Google Scholar
Barlow, H.B. & Levick, W.R. (1965). The mechanisms of directionally selective units in rabbit’s retina. Journal of Physiology (London) 178, 477–504.CrossRefGoogle ScholarPubMed
Braddick, O. (1974). A short-range process in apparent motion. Vision Research 14, 519–527.CrossRefGoogle ScholarPubMed
Braddick, O. (1980). Low-level and high-level processes in apparent motion. Philosophical Transactions of the Royal Society B (London) 290, 137–151.Google ScholarPubMed
Burr, D.C., Ross, J. & Morrone, M.C. (1986). Seeing objects in motion. Proceedings of the Royal Society B (London) 227, 249–265.Google ScholarPubMed
Dean, A.F. & Tolhurst, D.J. (1986). Factors influencing the temporal phase of response to bar and grating stimuli for simple cells in the cat striate cortex. Experimental Brain Research 62, 143–151.Google Scholar
Deangelis, G.C., Ohzawa, I. & Freeman, R.D. (1993). 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.Google Scholar
Deboer, E. & Kuyper, P. (1968). Triggered correlation. IEEE Transactions Bio-medical Engineering 15, 169–179.CrossRefGoogle Scholar
Eggermont, J.J., Johannesma, P.I.M. & Aertsen, A.M.H.J. (1983). Reverse correlation methods in auditory research. Quarterly Reviews of Biophysics 16, 341–414.Google Scholar
Einstein, G., Davis, T.L. & Sterling, P. (1987). Ultrastructure of synapses from the A-laminae of the lateral geniculate nucleus in layer IV of the cat striate cortex. Journal of Comparative Neurology 260, 63–75.CrossRefGoogle ScholarPubMed
Emerson, R.C., Bergen, J.R. & Adelson, E.H. (1992). Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Research 32, 203–218.CrossRefGoogle ScholarPubMed
Emerson, R.C. & Citron, M.C. (1988). How linear and nonlinear mechanisms contribute to directional selectivity in simple cells of cat striate cortex. Investigative Ophthalmology and Visual Science (Suppl.) 29, 23.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, 33–65.Google Scholar
Emerson, R.C. & Coleman, L. (1981). Does image movement have a special nature for neurons in the cat’s striate cortex? Investigative Ophthalmology and Visual Science (Suppl.) 20, 766–783.Google Scholar
Emerson, R.C. & Gerstein, G.L. (1977). Simple striate neurons in the cat. II. Mechanisms underlying directional asymmetry and directional selectivity. Journal of Neurophysiology 40, 136–155.CrossRefGoogle ScholarPubMed
Emerson, R.C., Korenberg, J. & Citron, M.C. (1989). Identification of intensive nonlinearities in cascade models of visual cortex and its relation to cell classification. In Advanced Methods of Physiological System Modelling, ed. Marmarelis, V., pp. 97–111. New York: Plenum.CrossRefGoogle Scholar
Ferster, D. (1986). Orientation selectivity of synaptic potentials in neurons of cat visual cortex. Journal of Neuroscience 6, 1284–1301.CrossRefGoogle Scholar
Ferster, D. (1987). Origin of orientation-selective EPSPs in simple cells of cat visual cortex. Journal of Neuroscience 7, 1780–1791.Google Scholar
Ferster, D. (1988). Spatially opponent excitation and inhibition in simple cells of the cat visual cortex. Journal of Neuroscience 8, 1172–1180.Google Scholar
Ferster, D. (1990 a). X-and Y-mediated synaptic potentials in neurons of areas 17and 18 of cat visual cortex. Visual Neuroscience 4, 115–133.CrossRefGoogle Scholar
Ferster, D. (1990 b). X-and Y-mediated current sources in areas 17 and 18 of cat visual cortex. Visual Neuroscience 4, 135–145.CrossRefGoogle ScholarPubMed
Ferster, D. & Jagadeesh, B. (1991). Nonlinearity of spatial summation in simple cells of areas 17 and 18 of cat visual cortex. Journal of Neuroscience 66, 1667–1679.Google ScholarPubMed
Ferster, D. & Lindstrom, S. (1983). An intracellular analysis of geniculo-cortical connectivity in area 17 of the cat. Journal of Physiology (London) 342, 181–216.Google Scholar
Glezer, V.D., Tsherbach, T.A., Gauselman, V.E. & Bondarko, V.M. (1982). Spatio-temporal organization of receptive fields of the cat striate cortex. Biological Cybernetics 43, 35–49.CrossRefGoogle ScholarPubMed
Goodwin, A.W., Henry, G.H. & Bishop, P.O. (1975). Direction selectivity of simple striate cells: Properties and mechanism. Journal of Neurophysiology 38, 1500–1523.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
Heeger, D.J. (1992). Half-squaring in responses of cat striate cells. Visual Neuroscience 9, 427–443.Google Scholar
Heggelund, P. (1986). Quantitative studies of enhancement and suppression zones in the receptive field of simple cells in cat striate cortex. Journal of Physiology 373, 293–310.Google Scholar
Hubel, D.H. & Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology (London) 160, 106–154.CrossRefGoogle ScholarPubMed
Jones, J.P. & Palmer, L.A. (1987 a). The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1187–1211.Google Scholar
Jones, J.P. & Palmer, L.A. (1987 b). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1233–1258.Google Scholar
Levick, W.R. (1972). Another tungsten microelectrode. Medical and Biological Engineering 10, 510–515.CrossRefGoogle ScholarPubMed
Mastronarde, D.N. (1987 a). Two classes of single-input X-cells in cat lateral geniculate nucleus. I. Receptive-field properties and classification of cells. Journal of Neurophysiology 57, 357–380.Google Scholar
Mastronarde, D.N. (1987 b). Two classes of single-input X-cells in cat lateral geniculate nucleus. II. Retinal inputs and the generation of receptive-field properties. Journal of Neurophysiology 57, 381–413.Google Scholar
McLean, J. & Palmer, L.A. (1988). Contribution of linear mechanisms to direction selectivity of simple cells in areas 17 and 18 of the cat. Investigative Ophthalmology and Visual Science (Suppl.) 29, 23.Google Scholar
McLean, J. & Palmer, L.A. (1989). Contribution 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. & Palmer, L.A. (1994). Organization of simple cell responses in the three-dimensional frequency domain. Visual Neuroscience 11, 295–306.Google Scholar
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978 a). 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
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978 b). Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. Journal of Physiology 283, 53–77.CrossRefGoogle ScholarPubMed
Mullikin, W.H., Jones, J.P. & Palmer, L.A. (1984). Periodic simple cells in cat area 17. Journal of Neurophysiology 52, 372–387.CrossRefGoogle ScholarPubMed
Orban, G.A. (1984). Neuronal Operations in the Visual Cortex. Berlin: Springer-Verlag.Google Scholar
Orban, G.A., Kennedy, H. & Maes, H. (1981 a). Response to movement of neurons in area 17 and 18 of the cat: Direction selectivity. Journal of Neurophysiology 45, 1059–1073.Google Scholar
Orban, G.A., Kennedy, H. & Maes, H. (1981 b). Response to movement of neurons in area 17 and 18 of the cat: Velocity sensitivity. Journal of Neurophysiology 45, 1043–1058.Google Scholar
Palmer, L.A. & Davis, T.L. (1981). Receptive-field structure in cat striate cortex. Journal of Neuro0physiology 46, 260–276.CrossRefGoogle ScholarPubMed
Palmer, L.A., Jones, J.P. & Gottschalk, A.G. (1987). Constraints on the estimation of spatial receptive-field profiles of simple cells in visual cortex. In Advanced Methods of Physiological System Modelling, ed. Marmarelis, V., pp. 205–216. Los Angeles, CA: Biomedical Simulations Resource.Google Scholar
Pollen, D.A., Gaska, J.P. & Jacobson, L.D. (1989). Physiological constraints on models of visual cortical function. In Models of Brain Function, ed. Cotterill, R.M.J., pp. 115–135. Cambridge, U.K.: Cambridge University Press.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, 101–118.Google Scholar
Raab, S.S., McLean, J., Stepnoski, R.A. & Palmer, L.A. (1988). Simple cell behavior is predicted by a push-pull model incorporating cospatial lateral geniculate (LGN) cell input. Society for Neuroscience Abstracts 14, 899.Google Scholar
Reichardt, W. (1961). Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. In Sensory Communication, ed. Rosenblith, W.A., pp. 307–317. New York: Wiley.Google 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.Google Scholar
Reid, R.C., Soodak, R.E. & Shapley, R.M. (1991). Direction selectivity and spatiotemporal structure of receptive fields of simple cells in cat striate cortex. Journal of Neurophysiology 66, 505–529.CrossRefGoogle ScholarPubMed
Saul, A.B. & Humphrey, A.L. (1990 a). Evidence of lagged-type geniculate input to visual cortex. Society for Neuroscience Abstracts 16, 1218.Google Scholar
Saul, A.B. & Humphrey, A.L. (1990 b). Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. Journal of Neurophysiology 64, 206–224.CrossRefGoogle ScholarPubMed
Schiller, P.H., Sandell, J.H. & Maunsell, J.H.R. (1986). Functions of the ON and OFF channels of the visual system. Nature 322, 824–825.Google Scholar
Sherk, H. & Horton, J.C. (1984). Receptive-field properties in cat’s area 17 in the absence of ON-center geniculate input. Journal of Neuroscience 4, 381–393.Google Scholar
Sillito, A.M. (1975). The contribution of inhibitory mechanisms to the receptive-field properties of neurons in the striate cortex of cat. Journal of Physiology (London) 250, 305–329.CrossRefGoogle Scholar
Skottun, B.C., Devalois, R.L., Grosof, D.H., Movshon, A.J., Albrecht, D.G. & Bonds, A.B. (1991). Classifying simple and complex cells on the basis of response modulation. Visual Research 31, 1079–1086.Google ScholarPubMed
Sterling, P. (1983). Microcircuitry of the cat retina. Annual Review of Neuroscience 6, 149–185.Google Scholar
Tolhurst, D.J. & Dean, A.F. (1990). The effects of contrast on the linearity of spatial summation of simple cells in the cat’s striate cortex. Experimental Brain Research 79, 582–588.Google Scholar
Tusa, R.J., Palmer, L.A. & Rosenquist, A.C. (1981). Multiple cortical visual areas: Visual field topography in the cat. In Cortical Sensory Organization, Vol. 2, Multiple Visual Areas, ed. Woolsey, C.N., pp. 1–31. The Humana Press.Google Scholar
Watson, A.B. & Ahumada, A.J. (1983). A look at motion in the frequency domain. NASA Technical Memorandum 84, 352.Google Scholar
Watson, A.B. & Ahumada, A.J. (1985). Model of human visual-motion sensing. Journal of the Optical Society of America A 2, 1124–1132.Google Scholar
Watson, A.B., Ahumada, A.J. & Farrell, J.E. (1986). Window of visibility: A psychophysical theory of fidelity in time-sampled visual motion displays. Journal of the Optical Society of America A 3, 300–307.CrossRefGoogle Scholar