Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T09:35:26.141Z Has data issue: false hasContentIssue false

Harmonic basis functions for spatial coding in the cat striate cortex

Published online by Cambridge University Press:  02 June 2009

V. D. Glezer
Affiliation:
Laboratory of Vision Physiology, I. P. Pavlov Institute of Physiology, Nab. Makarova, Leningrad, USSR
V. V. Yakovlev
Affiliation:
Laboratory of Vision Physiology, I. P. Pavlov Institute of Physiology, Nab. Makarova, Leningrad, USSR
V. E. Gauzelman
Affiliation:
Laboratory of Vision Physiology, I. P. Pavlov Institute of Physiology, Nab. Makarova, Leningrad, USSR

Abstract

The number of subregions in the activity profiles of simple cells varies in different cells from 2–8; that is, the number of cycles in the weighting function varies from 1–4. The distribution of receptive-field (RF) sizes at eccentricities of 0-6 deg are clustered at half-octave intervals and form a discrete distribution with maxima at 0.62, 0.9, 1.24, 1.8, 2.48, and 3.4 deg. The spatial frequencies to which the cells are tuned are also clustered at half-octave intervals, forming a discrete distribution peaking at 0.45, 0.69, 0.9, 1.35, 1.88, 2.7, 3.8, and 5.6 cycles/deg. If we divide the RF sizes by the size of the period of the subregions, then the average indices of complexity (really existing) or the number of cycles in the weighting function form (after normalization) the sequences: 1, 1.41, 2.0, 2.9, 4.15.

The relation between the bandwidth of the spatial-frequency characteristic and the optimal spatial frequency is in accordance with predictions of the Fourier hypothesis. The absolute bandwidth does not change with the number of cycles/module. This means that inside the module the absolute bandwidth does not change with the number of the harmonic. The results allow us to suggest the following. A module of the striate cortex, which is a group of cells with RFs of equal size projected onto the same area of central visual field, accounts for the Fourier description of the image. The basis functions of the module are composed of four harmonics only, irrespective of size and position of the module.

Besides linear cells (sinusoidal and cosinusoidal elements), the module contains nonlinear cells, performing a nonlinear summation of the responses of sinusoidal and cosinusoidal elements. Such cells are characterized by an index of complexity which is more than the number of cycles in the weighting function and by marked overlap of ON and OFF zones. The analysis of organization suggests that the cells can measure the amplitude and phase of the stimulus.

Type
Research Articles
Copyright
Copyright © Cambridge University Press 1989

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

Andrews, B.S. & Pollen, D.A. (1979). Relationship between spatial-frequency selectivity and receptive-field profile of simple cells. Journal of Physiology 287, 163176.CrossRefGoogle ScholarPubMed
Blakemore, C.B. & Campbell, F.W. (1969). On the existence in the human visual system of neurons selectively sensitive to the orientation and size of retinal images. Journal of Physiology 203, 237260.CrossRefGoogle Scholar
Camarda, R. M., Peterhans, E. & Bishop, P.O. (1985 a). Spatial organization of subregions in receptive fields of simple cells in cat striate cortex as revealed by stationary flashing bars and moving edges. Experimental Brain Research 60, 136150.Google ScholarPubMed
Camarda, R.M., Peterhans, E. & Bishop, P.O. (1985 b). Simple cells in cat striate cortex: responses to stationary flashing and moving light bars. Experimental Brain Research 60, 151158.Google ScholarPubMed
Campbell, F.W. & Robson, J.G. (1968). Application of Fourier analysis to the visibility of gratings. Journal of Physiology 157, 551566.CrossRefGoogle Scholar
Campbell, F.W. & Shelepin, Y.E. (1987). The method of evaluation of two dimensional images by visual system. Licence USSR N 1277454. Priority 08.01.85, N 3906913/14. Published in Bulletin N 29.Google Scholar
Cavanagh, P. (1985). Local LOG polar frequency analysis in the striate cortex as a basis for size and orientation invariance. In Models of Visual Cortex, ed. Rose, D. & Dobson, V.A., pp. 8495. Chichester, England: John Wiley & Sons.Google Scholar
Field, D.J. & Nachmias, J. (1984). Phase reversal discrimination. Vision Research 24, 333340.CrossRefGoogle ScholarPubMed
Field, D.J. & Tolhurst, D. (1986). The structure and symmetry of simple-cell receptive-field profiles in the cat visual cortex. Proceedings of the Royal Society (London) 228, 379400.Google Scholar
Ginsburg, A. (1976). The perception of visual form: a two-dimensional filter analysis. In Information Processing in Visual System, ed. Glezer, V., pp. 4651. Leningrad.Google Scholar
Glezer, V.D. (1985 a). Spatial and spatial-frequency characteristics of receptive fields of the visual cortex and piecewise Fourier analysis. In Models of the Visual Cortex, ed. Rose, D. & Dobson, V.A., pp. 265272. Chichester, England: John Wiley & Sons.Google ScholarPubMed
Glezer, V.D. (1985 b). Vision and Mind. Leningrad: Nauka. (In Russian).Google Scholar
Glezer, V.D. (1987). Modules and basis functions of the visual cortex. Perception 16, 226.Google Scholar
Glezer, V.D., Gauzelman, V.E. & Yakovlev, V.V. (1989). Spatial organization of subfields in receptive fields of cells in cat striate cortex. Vision Research 29, 777788.CrossRefGoogle ScholarPubMed
Glezer, V.D., Ivanov, V.A., Cooperman, A.M. & Tscherbach, T.A. (1975). An investigation of complex and hypercomplex receptive fields in the visual cortex of the cat. Vision Research 16, 789797.CrossRefGoogle Scholar
Glezer, V.D., Ivanov, V.A. & Tscherbach, T.A. (1973). Investigation of complex and hypercomplex receptive fields of visual cortex of the cat as spatial frequency filters. Vision Research 13, 18751904.CrossRefGoogle ScholarPubMed
Glezer, V.D., Tscherbach, T.A., Gauzelman, V.E. & Bondarko, V.M. (1980). Linear and nonlinear properties of simple and complex receptive fields in area 17 of the cat visual cortex: a model of the fields. Biological Cybernetics 37, 195208.CrossRefGoogle Scholar
Glezer, V.D., Tscherbach, T.A., Gauzelman, V.E. & Bondarko, V.M. (1982). Spatio-temporal organization of receptive fields of the cat striate cortex. Biological Cybernetics 43, 3549.CrossRefGoogle ScholarPubMed
Glezer, V.D., Yakovlev, V.V. & Gauzelman, V.E. (1987). Modules and basis functions of the cat's visual cortex. Sensornye sistemy 1, 8392. (In Russian).Google Scholar
Heggelund, P. (1986). Quantitative studies of the discharge fields of single cells in cat striate cortex. Journal of Physiology 373, 277292.CrossRefGoogle ScholarPubMed
Hochstein, S. & Shapley, R.M. (1976). Quantitative analysis of retinal ganglion cell classification. Journal of Physiology 262, 237264.CrossRefGoogle Scholar
Hubel, D.H. & Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology 160, 106154.CrossRefGoogle ScholarPubMed
Julesz, B. (1974). Two-dimensional spatial-frequency tuned channels in visual perception. In Signal Analysis and Pattern Recognition in Biomedical Engineering, ed. Jubar, G.F., pp. 177196. Haifa, Israel.Google Scholar
Kulikowski, J.J. & Vidyasagar, T.R. (1986). Space and spatial frequency: analysis and representation in the macaque striate cortex. Experimental Brain Research 64, 518.CrossRefGoogle ScholarPubMed
Lawden, M.C. (1983). An investigation of the ability of the human visual system to encode spatial phase relationships. Vision Research 23, 14511463.CrossRefGoogle ScholarPubMed
Maske, R., Yamane, S. & Bishop, P.O. (1985). Simple and B-cells in cat striate cortex. Complementarity of responses to moving light and dark bars. Journal of Neurophysiology 53, 670685.CrossRefGoogle ScholarPubMed
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978 a). Spatial summation in the receptive fields of simple cells in the cat striate cortex. Journal of Physiology 283, 5362.CrossRefGoogle Scholar
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978 b). Receptive-field organization of complex cells in the cat's striate cortex. Journal of Physiology 283, 7999.CrossRefGoogle ScholarPubMed
Mullikin, W.H., Jones, J.P. & Palmer, L.A. (1984). Periodic simple cells in cat area 17. Journal of Neurophysiology 52, 372387.CrossRefGoogle ScholarPubMed
Nielsen, K.R.K., Watson, A.B., Ahumada, A.J., Jr. (1985). Application of a computable model of human spatial vision to phase discrimination. Journal of the Optical Society of America 2, 16001606.CrossRefGoogle ScholarPubMed
Palmer, L.A. & Davis, T.L. (1981). Receptive-field structure in cat striate cortex. Journal of Neurophysiology 46, 260276.CrossRefGoogle ScholarPubMed
Pollen, D.A. & Feldon, S.E. (1979). Spatial periodicities of periodic complex cells in the visual cortex cluster at one-half octave intervals. Investigative Ophthalmology and Visual Science 18, 429434.Google ScholarPubMed
Pollen, D.A. & Ronner, S.F. (1981). Phase relationships between adjacent simple cells in the cat. Science 212, 14091411.CrossRefGoogle Scholar
Prazdnikova, N.V., Glezer, V.D., Danilova, V.F. & Nemurene, G.V. (1985). Two types of visual recognition and its localization in the brain of the cat. In Vision of Organisms and Robots, Vilnius, pp. 195196. (In Russian).Google Scholar
Robson, J.G. (1975). Receptive fields: spatial and intensive representations of the visual image. In Handbook of Perception, V, pp. 81112. New York: Academic Press.Google Scholar
Robson, J.G., Tolhurst, D.J., Freeman, R.D. & Ohzawa, J. (1988). Simple cells in the visual cortex of the cat can be narrowly tuned for spatial frequency. Visual Neuroscience 1, 415419.CrossRefGoogle ScholarPubMed
Spitzer, H. & Hochstein, S. (1985). A complex-cell receptive-field model. Journal of Neurophysiology 53, 12661286.CrossRefGoogle ScholarPubMed
Thompson, T.D. & Tolhurst, D.J. (1980). Optimal spatial frequency of neighbouring neurones in the cat's visual cortex. Journal of Physiology, 300, 5758.Google Scholar
Tyler, C.W. (1978). Selectivity for spatial frequency and bar width in cat visual cortex. Vision Research 18, 121122.CrossRefGoogle ScholarPubMed
Van Essen, D.G. (1979). Visual areas of the mammalian cerebral cortex. Annual Review of Neuroscience 2, 227263.CrossRefGoogle ScholarPubMed
Venes, J.L., Collins, W.F. & Taub, A. (1971). Nitrous oxide: an anaesthetic for experiments in cats. American Journal of Physiology 220, 20282031.CrossRefGoogle ScholarPubMed
Vol, I.A. & Pavlovskaya, M.B. (1986). The correlation between the nearness of the Fourier spectra of the images and errors in recognition. Physiology of Man 12, 400406.Google Scholar
Yakovlev, V.V. (1983). The difference in description of visual image in the postparietal and inferotemporal cortex of the monkeys. Doklady Akademii Nauk SSSR Seriya Biologiya 270, 754757. (In Russian).Google Scholar
Yakovlev, V.V., Glezer, V.D. & Gauzelman, V.E. (1987). Linear and nonlinear simple cells in the visual cortex of the cat. Sensory Systems 1, 293298. (In Russian).Google Scholar
Yakubovitch, V.A. (1965). Some general theoretical principles of organization of recognition systems. Computer Technic 4, 171. (In Russian).Google Scholar
Yule, G.U. & Kendall, M.G. (1950). An Introduction to the Theory of Statistics. London: Griffin and Co., Ltd.Google Scholar
Zhong-Di, Wang, (1981). Harmonic analysis with real function of frequency 2: Periodic and bonded cases. Applied Mathematics and Computers 9, 153163.Google Scholar