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Computing relative motion with complex cells

Published online by Cambridge University Press:  02 June 2005

BABETTE K. DELLEN ,
JOHN W. CLARK  and
RALF WESSEL
BABETTE K. DELLEN
Affiliation:
Department of Physics, Washington University in Saint Louis, One Brookings Drive, St. Louis
JOHN W. CLARK
Affiliation:
Department of Physics, Washington University in Saint Louis, One Brookings Drive, St. Louis
RALF WESSEL
Affiliation:
Department of Physics, Washington University in Saint Louis, One Brookings Drive, St. Louis
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Abstract

Contextual influences shape our perception of local visual stimuli. Relative-motion stimuli represent an important contextual influence, yet the mechanism subserving relative-motion computation remains largely unknown. In the present work, we investigated the responses of an established model for simple and complex cells to relative-motion stimuli. A straightforward mathematical analysis showed that relative-motion computation is inherent in the nonlinear transformation of the complex-cell model. Tuning to relative velocity is achieved by applying a temporal filter to the complex-cell response. The mathematical inference is supported by simulations that quantitatively reproduce measured complex-cell responses in both cat and monkey to a variety of relative-motion stimuli. Importantly, the posited mechanism for cortical computation of relative motion does not require an intermediate neural representation of local velocities and does not require lateral or feedback interactions within a network.

Keywords

Contextual influencesEnergy modelRelative motionSimple and complex cellsVisual cortex
Type
Research Article
Information
Visual Neuroscience , Volume 22 , Issue 2 , March 2005 , pp. 225 - 236
Copyright
© 2005 Cambridge University Press

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References

REFERENCES

Adelson, E.H. & Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A 2, 284–299.CrossRefGoogle Scholar
Bradley, D.C., Chang, G.C., & Andersen, R.A. (1998). Encoding of three-dimensional structure-from-motion by primate area MT neurons. Nature 392, 714–717.Google Scholar
Cao, A. & Schiller, P.H. (2003). Neural responses to relative speed in the primary visual cortex of rhesus monkey. Visual Neuroscience 20, 77–84. [Data is taken from Fig. 3A. Note that there has been a misprint in the paper. The label on the x-axis should be log 2(Vb /Vt) (private correspondence with authors).]CrossRefGoogle Scholar
Chance, F.S., Nelson, S.B., & Abbott, L.F. (1999). Complex cells as cortically amplified simple cells. Nature Neuroscience 2, 277–282.Google Scholar
Davidson, R.M. & Bender, D.B. (1991). Selectivity for relative motion in the monkey superior colliculus. Journal of Neurophysiology 65, 1115–1133.Google Scholar
Dayan, P. & Abbott, L.F. (2002). Neural encoding II: Reverse correlation and visual receptive fields. In Theoretical Neuroscience, ed. Sejnowski, T.J. & Poggio, T., pp. 62–80. Cambridge, Massachusetts: MIT Press.
DeAngelis, G.C., Ohzawa, I., & Freeman, R.D. (1993). 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.Google Scholar
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, 201–218.Google Scholar
Frost, B.J. & Nakayama, K. (1983). Single visual neurons code opposing motion independent of direction. Science 220, 744–745.CrossRefGoogle Scholar
Gruesser, O.J. (1971). A quantitative analysis of spatial summation of excitation and inhibition within the receptive field of retinal ganglion cells of cats. Vision Research (Suppl.) 3, 103–127.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 (London) 160, 106–154.CrossRefGoogle Scholar
Lamme, V.A.F. (1995). The neurophysiology of figure-ground segregation in primary visual cortex. Journal of Neuroscience 15, 1605–1615.Google Scholar
Li, C.Y., Lei, J.J., & Yao, H.S. (1999). Shift in speed selectivity of visual cortical neurons: A neural basis of perceived motion contrast. Proceedings of the National Academy of Sciences of the U.S.A. 96, 4052–4056.CrossRefGoogle Scholar
Movshon, J., Thompson, I., & Tolhurst, D. (1978a). Spatial summation in the receptive fields of simple cells in the cat's striate cortex. Journal of Physiology 283, 53–77.Google Scholar
Movshon, J., Thompson, I., & Tolhurst, D. (1978b). Receptive field organization of complex cells in the cat's striate cortex. Journal of Physiology 283, 79–99.Google Scholar
Movshon, J.A., Thompson, I.D., & Tolhurst, D.J. (1978c). Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat's visual cortex. Journal of Physiology 283, 101–120.Google Scholar
Nakayama, K. (1985). Biological image processing: A review. Vision Research 25, 625–50.CrossRefGoogle Scholar
Orban, G.A., Gulyas, B., & Vogels, R. (1987). Influence of a moving textured background on direction selectivity of cat striate neurons. Journal of Neurophysiology 57, 1792–1812.Google Scholar
Poggio, T., Reichhardt, W., & Hausen, K. (1981). A neuronal circuitry for relative movement discrimination by the visual system of the fly. Naturwissenschaften 67, 443–446.CrossRefGoogle Scholar
Qian, N. & Mikaelian, S. (2000). Relationship between phase and energy methods for disparity computation. Neural Computation 12, 279–292.CrossRefGoogle Scholar
Reichhardt, W., Egelhaaf, M., & Guo, A. (1989). Processing of figure and background motion in the visual system of the fly. Biological Cybernetics 61, 327–345.CrossRefGoogle Scholar
Ringach, D.L. (2002). Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. Journal of Neurophysiology 88, 455–463.Google Scholar
Skottun, B.C., De Valois, R.L., Grosof, D.H., Movshon, J.A., Albrecht, D.G., & Bonds, A.B. (1991). Classifying simple and complex cells on the basis of response modulation. Vision Research 31, 1079–1086.Google Scholar
Snowden, R.J., Treue, S., Erickson, R.G., & Anderson, R.A. (1991). The response of area MT and V1 neurons to transparent motion. Journal of Neuroscience 11, 2768–2785.Google Scholar
Sun, H.J., Zhao, J., Southall, T.L., & Xu, B. (2002). Contextual influences on the directional responses of tectal cells in pigeons. Visual Neuroscience 19, 133–144.CrossRefGoogle Scholar