Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T21:47:32.843Z Has data issue: false hasContentIssue false
  • English
  • Français

Responses of neurons in macaque MT to stochastic motion signals

Published online by Cambridge University Press:  02 June 2009

Kenneth H. Britten ,
Michael N. Shadlen ,
William T. Newsome  and
J. Anthony Movshon
Kenneth H. Britten
Affiliation:
Department of Neurobiology, Stanford University School of Medicine, Stanford
Michael N. Shadlen
Affiliation:
Department of Neurobiology, Stanford University School of Medicine, Stanford
William T. Newsome
Affiliation:
Department of Neurobiology, Stanford University School of Medicine, Stanford
J. Anthony Movshon
Affiliation:
Howard Hughes Medical Institute, Center for Neural Science, and Department of Psychology, New York University, New York
Get access Rights & Permissions [Opens in a new window]

Abstract

Dynamic random-dot stimuli have been widely used to explore central mechanisms of motion processing. We have measured the responses of neurons in area MT of the alert monkey while we varied the strength and direction of the motion signal in such displays. The strength of motion is controlled by the proportion of spatiotemporally correlated dots, which we term the correlation of the stimulus. For many MT cells, responses varied approximately linearly with stimulus correlation. When they occurred, nonlinearities were equally likely to be either positively or negatively accelerated. We also explored the relationship between response magnitude and response variance for these cells and found, in general agreement with other investigators, that this relationship conforms to a power law with an exponent slightly greater than 1. The variance of the cells' discharge is little influenced by the trial-to-trial fluctuations inherent in our stochastic display, and is therefore likely to be of neural origin. Linear responses to these stochastic motion stimuli are predicted by simple, low-level motion models incorporating sensors having relatively broad spatial and temporal frequency tuning.

Keywords

Visual cortexMotion processingMotion modelsMTDirection selectivityDirection discrimination
Type
Research Articles
Information
Visual Neuroscience , Volume 10 , Issue 6 , November 1993 , pp. 1157 - 1169
Copyright
Copyright © Cambridge University Press 1993

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). Spatio-temporal energy models for the perception of motion. Journal of the Optical Society of America 2, 284–299.CrossRefGoogle Scholar
Albrecht, D. & Hamilton, D.B. (1982). Striate cortex of monkey and cat: Contrast response function. The Journal of Neurophysiology 48, 217–237.CrossRefGoogle ScholarPubMed
Berman, N.J., Douglas, R.J. & Martin, K.A.C. (1992). GABA-mediated inhibition in the neural networks of visual cortex. In Progress in Brain Research, ed. Mize, R.R., Marc, R.E. & Sillito, A.M., pp. 443–476. Amsterdam: Elsevier.Google Scholar
Bonds, A.B. (1991). Temporal dynamics of contrast gain in single cells of the cat striate cortex. Visual Neuroscience 2, 239–255.CrossRefGoogle Scholar
Braun, D., Boman, D. & Hotson, J. (1991). Asymmetries in smooth pursuit do not predict asymmetries in motion perception. Investigative Ophthalmology and Visual Science (Suppl.) 32, 897.Google Scholar
Britten, K.H., Shadlen, M.N., Newsome, W.T. & Movshon, J.A. (1992). The analysis of visual motion: A comparison of neuronal and psychophysical performance. Journal of Neuroscience 12, 4745–4765.CrossRefGoogle Scholar
Crist, C.F., Yamasaki, D.S.G., Komatsu, H. & Wurtz, R.H. (1988). A grid system and a microsyringe for single-cell recording. Journal of Neuroscience Methods 26, 117–122.CrossRefGoogle Scholar
Dean, A.F. (1981). The variability of discharge of simple cells in cat striate cortex. Experimental Brain Research 44, 437–440.CrossRefGoogle ScholarPubMed
Desimone, R. & Ungerleider, L.G. (1986). Multiple visual areas in the caudal superior temporal sulcas of the macaque. Journal of Comparative Neurology 248, 164–189.CrossRefGoogle Scholar
Downing, C.J. & Movshon, J.A. (1989). Spatial and temporal summation in the detection of motion in stochastic random-dot displays. Investigative Ophthalmology and Visual Science (Suppl.) 30, 72.Google Scholar
Dubner, R. & Zeki, S.M. (1971). Response properties and receptive fields of cells in an anatomically defined region of the superior temporal sulcus. Brain Research 35, 528–532.CrossRefGoogle 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, 203–218.CrossRefGoogle ScholarPubMed
Fahle, M. & Poggio, T. (1981). Visual hyperacuity: Spatio-temporal interpolation in human vision. Proceedings of the Royal Society B (London) 213, 451–477.Google Scholar
Gallyas, F. (1979). Silver staining of myelin by means of physical development. Neurological Research 1, 203–209.CrossRefGoogle ScholarPubMed
Green, D.M. & Swets, J.A. (1966). Signal Detection Theory and Psychophysics. New York: John Wiley and Sons, Inc.Google Scholar
Hays, A.V., Richmond, B.J. & Optican, L.M. (1982). A UNIX-based multiple process system for real-time data acquisition and control. WESCON Conference Proceedings 2, 1–10.Google Scholar
Heeger, D.J. (1987). Model for the extraction of image flow. Journal of the Optical Society of America 4, 1455–1471.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 181–198.CrossRefGoogle ScholarPubMed
Hess, R.H., Baker, C.L. & Zihl, J. (1989). The “motion blind” patient: Low-level spatial and temporal filters. Journal of Neuroscience 9, 1628–1640.CrossRefGoogle Scholar
Hiris, E. & Blake, R. (1992). A new perspective on an old phenomenon, the visual motion afteraffect. Investigative Ophthalmology and Visual Science (Suppl.) 33, 1139.Google Scholar
Hoel, P., Port, S. & Stone, C. (1971). Introduction to Statistical Theory. Boston, Massachusetts: Houghton Mifflin Co.Google Scholar
Judge, S.J., Richmond, B.J. & Chu, F.C. (1980). Implantation of magnetic search coils for measurement of eye position: An improved method. Vision Research 20, 535–538.CrossRefGoogle ScholarPubMed
Maunsell, J.H.R. & Newsome, W.T. (1987). Visual processing in monkey extrastriate cortex. Annual Review of Neuroscience 10, 363–401.CrossRefGoogle ScholarPubMed
Maunsell, J.H.R. & Van Essen, D.C. (1983 a). The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. Journal of Neuroscience 3, 2563–2586.CrossRefGoogle ScholarPubMed
Maunsell, J.H.R. & Van Essen, D.C. (1983 b). Functional properties of neurons in the middle temporal visual area (MT) of the macaque monkey: I. Selectivity for stimulus direction, speed, and orientation. Journal of Neurophysiology 49, 1127–1147.CrossRefGoogle ScholarPubMed
Morgan, M.J. & Ward, R. (1980). Conditions for motion flow in dynamic visual noise. Vision Research 20, 431–435.CrossRefGoogle ScholarPubMed
Movshon, J.A., Adelson, E.H., Gizzi, M.S. & Newsome, W.T. (1985). The analysis of moving visual patterns. In Pattern Recognition Mechanisms, ed. Chagas, C, Gattass, R. & Gross, C, pp. 117–151. New York: Springer-Verlag.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
Nakayama, K. (1985). Biological image motion processing: A review. Vision Research 25, 625–660.CrossRefGoogle ScholarPubMed
Newsome, W.T., Britten, K.H. & Movshon, J.A. (1989). Neuronal correlates of a perceptual decision. Nature 341, 52–54.CrossRefGoogle ScholarPubMed
Newsome, W.T., Gizzi, M.S. & Movshon, J.A. (1983). Spatial and temporal properties of neurons in the macaque MT. Investigative Ophthalmology and Visual Science (Suppl.) 24, 106.Google Scholar
Newsome, W.T. & Pare, E.B. (1988). A selective impairment of motion perception following lesions of the middle temporal visual area (MT). Journal of Neuroscience 8, 2201–2211.CrossRefGoogle ScholarPubMed
Ohzawa, I., Sclar, G. & Freeman, R.D. (1985). Contrast gain control in the cat's visual system. Journal of Neurophysiology 54, 651–667.CrossRefGoogle ScholarPubMed
Quick, R.F. (1974). A vector magnitude model of contrast detection. Kybernetik 16, 65–67.CrossRefGoogle ScholarPubMed
Salzman, C.D., Murasugi, C.M., Britten, K.H. & Newsome, W.T. (1992). Microstimulation in visual area MT: Effects on direction discrimination performance. Journal of Neuroscience 12, 2331–2355.CrossRefGoogle ScholarPubMed
Sclar, G., Maunsell, J.H.R. & Lennie, P. (1990). Coding of image contrast in central visual pathways of the macaque monkey. Vision Research 30, 1–10.CrossRefGoogle ScholarPubMed
Snowden, R.J., Treue, S. & Andersen, R.A. (1992). The response of neurons in areas V1 and MT of the alert rhesus monkey to moving random-dot patterns. Experimental Brain Research 88, 389–400.CrossRefGoogle ScholarPubMed
Tolhurst, D.J., Movshon, J.A. & Dean, A.F. (1983). The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research 23, 775–785.CrossRefGoogle Scholar
Vaina, L.M., Lemay, M., Bienfang, D.C., Choy, A.Y. & Nakayama, K. (1990). Intact “biological motion” and “structure from motion” perception in a patient with impaired motion mechanisms: A case study. Visual Neuroscience 5, 363–369.CrossRefGoogle Scholar
Van Santen, J.P.H. & Sperling, G. (1985). Elaborated Reichardt detectors. Journal of the Optical Society of America A 2, 300–321.CrossRefGoogle ScholarPubMed
Vogels, R., Spileers, W. & Orban, G.A. (1989). The response variability of striate cortical neurons in the behaving monkey. Experimental Brain Research 77, 432–436.CrossRefGoogle ScholarPubMed
Watson, A.B. & Ahumada, A.J. Jr, (1983). A look at motion in the frequency domain. In Motion: Perception and Representation, ed. Tsotsos, J.K., pp. 1–10. New York: Association for Computing Machinery.Google Scholar
Watson, A.B. & Ahumada, A.J. Jr, (1985). Model of human visual-motion sensing. Journal of the Optical Society of America 2, 322–341.CrossRefGoogle ScholarPubMed
Williams, D.W. & Sekuler, R. (1984). Coherent global motion percepts from stochastic local motions. Vision Research 24, 55–62.CrossRefGoogle ScholarPubMed
Zeki, S.M. (1974). Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. Journal of Physiology 236, 549–573.CrossRefGoogle ScholarPubMed