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Organization of simple cell responses in the three-dimensional (3-D) frequency domain

Published online by Cambridge University Press:  02 June 2009

J. McLean  and
L. A. Palmer
J. McLean
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
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Abstract

The amplitude spectra of simple cells in areas 17 and 18 were estimated in two and three dimensions (2–D and 3–D) using drifting sinusoidal gratings. In 2–D, responses were sampled with 16 x 16 resolution in spatial and temporal frequency at the optimal orientation. In 3–D, responses were sampled with 12 x 12 x 10 resolution in spatial frequency, orientation, and temporal frequency. For 45/50 cells studied, the spatial attributes of the receptive fields (RFs) were independent of temporal frequency except for a scale factor. The five exceptions to this general finding could be described as follows: For four area 17 cells, responses in the null direction increased with temporal frequency, reducing direction selectivity. For one area 18 cell, the optimal spatial frequency increased with temporal frequency and vice versa. The 2–D discrete Fourier transform was applied to all of the estimated amplitude spectra assuming zero spatial and temporal phase. These transforms were compared with the results of first-order reverse correlations as described in the previous paper (McLean et al., 1994). Direction selective cells exhibited excitatory subregions that were obliquely oriented in space-time in both the raw correlation data and inverse transforms of the spectral data. The slopes of the subregions found in these two measures were highly correlated. Direction indices obtained from space and frequency domain measures were comparable. We demonstrate that the spectral response profiles of most simple cells are aligned with the coordinate axes in frequency domain. That is, they may be considered one-quadrant separable, suggesting that these cells are not velocity tuned per se, but are tuned for spatiotemporal frequency. The spectral bandwidth establishes the range of velocities to which these cells will respond. These findings are consistent with the one-quadrant separability constraint of linear quadrature models. We conclude that most simple cells perform as roughly linear filters in two dimensions of space and time.

Keywords

Visual cortexSpatial frequencyTemporal frequencyDirection selectivity
Type
Research Articles
Information
Visual Neuroscience , Volume 11 , Issue 2 , March 1994 , pp. 295 - 306
Copyright
Copyright © Cambridge University Press 1994

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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 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
Bisti, S., Carmignoto, G., Galli, L. & Maffei, L. (1985). Spatial-frequency characteristics of neurones on area 18 in the cat: Dependence on the velocity of the visual stimulus. Journal of Physiology 359, 259–268.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 cells classification. In Advanced Methods of Physiological System Modelling, ed. Marmarelis, V., pp. 97–111. New York: Plenum.CrossRefGoogle Scholar
Foster, K.H., Gaska, J.P., Nagler, M. & Pollen, D.A. (1985). Spatial-and temporal-frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. Journal of Physiology 365, 331–363.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1987). Model for the extraction of image flow. Journal of the Optical Society of America A4, 1455–1471.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1992). Half-squaring in responses of cat striate cells. Visual Neuroscience 9, 427–443.CrossRefGoogle ScholarPubMed
Holub, R.A. & Morton-Gibson, M. (1981). Response of visual cortical neurons of the cat to moving sinusoidal gratings: Response-contrast functions and spatiotemporal interactions. Journal of Neurophysiology 46, 1244–1259.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
Jones, J.P. & Palmer, L.A. (1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1233–1258.CrossRefGoogle ScholarPubMed
Jones, J.P., Stepnoski, R.A. & Palmer, L.A. (1987). The two-dimensional spectral structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1212–1232.CrossRefGoogle ScholarPubMed
Kulikowski, J.J. & Bishop, P.O. (1981). Linear analysis of the responses of simple cells in the cat visual cortex. Experimental Brain Research 44, 386–400.CrossRefGoogle ScholarPubMed
Mclean, J. & Palmer, L.A. (1989). Responses of simple cells in areas 17 and 18 of the cat in the spatiotemporal-frequency domain. Investigative Ophthalmology and Visual Science (Suppl.) 30, 111.Google Scholar
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
Movshon, J.A., Newsome, W.T., Gizzi, M.S. & Levitt, J.B. (1988). Spatio-temporal tuning and speed sensitivity in macaque visual cortical neurons. Investigative Ophthalmology and Visual Science (Suppl.) 29, 327.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
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
Saul, A.B. & Humphrey, A.L. (1989). Phase differences in the cat LGN and cortical direction selectivity. Society for Neuroscience Abstracts 15, 1394.Google Scholar
Saul, A.B. & Humphrey, A.L. (1990). Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. Journal of Neurophysiology 64, 206–224.CrossRefGoogle ScholarPubMed
Saul, A.B. & Humphrey, A.L. (1992). Temporal-frequency tuning of direction selectivity in cat visual cortex. Visual Neuroscience 8, 365–372.CrossRefGoogle ScholarPubMed
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. Vision Research 31, 1079–1086.CrossRefGoogle ScholarPubMed
Tolhurst, D.J. & Movshon, J.A. (1975). Spatial and temporal contrast sensitivity of striate cortical neurones. Nature 257, 674–675.CrossRefGoogle ScholarPubMed
Watson, A.B. & Ahumada, A.J. (1983). A look at motion in the frequency domain. NASA Technical Memorandum 84352.Google Scholar
Watson, A.B. & Ahumada, A.J. (1985). Model of human visual motion sensing. Journal of the Optical Society of America A2, 322–342.CrossRefGoogle ScholarPubMed