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Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification

Published online by Cambridge University Press:  02 June 2009

Wilson S. Geisler  and
Duane G. Albrecht
Wilson S. Geisler
Affiliation:
Department of Psychology and Center for Vision and Image Sciences, University of Texas, Austin
Duane G. Albrecht
Affiliation:
Department of Psychology and Center for Vision and Image Sciences, University of Texas, Austin
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Abstract

A descriptive function method was used to measure the detection, discrimination, and identification performance of a large population of single neurons recorded from within the primary visual cortex of the monkey and the cat, along six stimulus dimensions: contrast, spatial position, orientation, spatial frequency, temporal frequency, and direction of motion. First, the responses of single neurons were measured along each stimulus dimension, using analysis intervals comparable to a normal fixation interval (200 ms). Second, the measured responses of each neuron were fitted with simple descriptive functions, containing a few free parameters, for each stimulus dimension. These functions were found to account for approximately 90% of the variance in the measured response means and response standard deviations. (A detailed analysis of the relationship between the mean and the variance showed that the variance is proportional to the mean.) Third, the parameters of the best-fitting descriptive functions were utilized in conjunction with Bayesian (optimal) decision theory to determine the detection, discrimination, and identification performance for each neuron, along each stimulus dimension. For some of the cells in monkey, discrimination performance was comparable to behavioral performance; for most of the cells in cat, discrimination performance was better than behavioral performance. The behavioral contrast and spatial-frequency discrimination functions were similar in shape to the envelope of the most sensitive cells; they were also similar to the discrimination functions obtained by optimal pooling of the entire population of cells. The statistics which summarize the parameters of the descriptive functions were used to estimate the response of the visual cortex as a whole to a complex natural image. The analysis suggests that individual cortical neurons can reliably signal precise information about the location, size, and orientation of local image features.

Keywords

Primate visual cortexSingle neuron electrophysiologyBehavioral discrimination performancePattern recognitionBayesian analysis
Type
Research Articles
Information
Visual Neuroscience , Volume 14 , Issue 5 , September 1997 , pp. 897 - 919
Copyright
Copyright © Cambridge University Press 1997

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References

Albrecht, D.G. (1995). Visual cortex neurons in monkey and cat: Effect of contrast on the spatial and temporal phase transfer functions. Visual Neuroscience 12, 1191–1210.CrossRefGoogle ScholarPubMed
Albrecht, D.G., Farrar, S.B. & Hamilton, D.B. (1984). Spatial contrast adaptation characteristics of neurones recorded in the cat's visual cortex. Journal of Physiology 347, 713–739.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
Albrecht, D.G. & Geisler, W.S. (1994). Visual cortex neurons in monkey and cat: Contrast response nonlinearities and stimulus selectivity. In Computational Vision Based on Neurobiology, ed. Lawton, T, Vol. 2054, pp. 12–31. Bellingham, Washington: SPIE.CrossRefGoogle Scholar
Albrecht, D.G. & Hamilton, D.B. (1982). Striate cortex of monkey and cat: Contrast response function. Journal of Neurophysiology 48, 217–237.CrossRefGoogle ScholarPubMed
Barlow, H.B. (1972). Single units and sensation: A neuron doctrine for perceptual psychology? Perception 1, 371–394.CrossRefGoogle ScholarPubMed
Barlow, H.B. (1995). The neuron doctrine in perception. In The Cognitive Neurosciences, ed. Gazzanioa, M.S., pp. 415–435. Cambridge, Massachusetts: The MIT Press.Google Scholar
Barlow, H.B., Kaushal, T.P., Hawken, M. & Parker, A.J. (1987). Human contrast discrimination and the threshold of cortical neurons. Journal of the Optical Society of America A 4, 2366–2371.CrossRefGoogle ScholarPubMed
Blake, R., Holopigian, K. & Wilson, H.R. (1986). Spatial-frequency discrimination in cats. Journal of the Optical Society of America A 3, 1443–1449.CrossRefGoogle ScholarPubMed
Blake, R. & Petrakis, I. (1984). Contrast discrimination in the cat. Behavioral Brain Research 12, 155–162.CrossRefGoogle ScholarPubMed
Bradley, A. & Ohzawa, I. (1986). A comparison of contrast detection and discrimination. Vision Research 26, 991–997.CrossRefGoogle ScholarPubMed
Bradley, A., Skottun, B.C., Ohzawa, I., Sclar, G. & Freeman, R.D. (1985). Neurophysiological evaluation of the differential response model for orientation and spatial-frequency discrimination. Journal of the Optical Society of America A 2, 1607–1610.CrossRefGoogle ScholarPubMed
Campbell, F.W., Cooper, G.F. & Enroth-Cugell, C. (1968). The spatial selectivity of visual cells of the cat. Journal of Physiology 203, 223–235.CrossRefGoogle Scholar
Campbell, F.W., Cooper, G.F., Robson, J.G. & Sachs, MB. (1969). The spatial selectivity of visual cells of the cat and the squirrel monkey. Journal of Physiology 204, 120–121P.Google Scholar
Chandler, J.P. (1969). STEPIT—Finds local minima of a smooth function of several parameters. Behavioral Science 14, 81–82.Google Scholar
Curcio, C.A. & Allen, K.A. (1990). Topography of ganglion cells in human retina. Journal of Comparative Neurology 300, 5–25.CrossRefGoogle ScholarPubMed
De Valois, R.L., Abramov, I. & Mead, W.R. (1967). Single cell analysis of wavelength discrimination at the lateral geniculate nucleus in the macaque. Journal of Neurophysiology 30, 415–433.CrossRefGoogle ScholarPubMed
De Valois, R.L., Albrecht, D.G. & Thorell, L.G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research 22, 545–559.CrossRefGoogle ScholarPubMed
De Valois, R.L. & De Valois, K.K. (1988). Spatial Vision. New York: Oxford.Google Scholar
Foley, J.M. (1994). Human luminance pattern-vision mechanisms: Masking experiments require a new model. Journal of the Optical Society of America A 11, 1710–1719.CrossRefGoogle ScholarPubMed
Foley, J.M. & Legge, G.E. (1981). Contrast detection and near-threshold discrimination in human vision. Vision Research 21, 1041–1053.CrossRefGoogle ScholarPubMed
Gallant, J.L., Conner, C.E., Drury, H. & Van Essen, D.C. (1995). Neural responses in monkey visual cortex during free viewing of natural scenes: Mechanisms of response suppression. Investigative Ophthalmology and Visual Science (Suppl.) 36/4, S1052.Google Scholar
Geisler, W.S. (1984). Physical limits of acuity and hyperacuity. Journal of the Optical Society of America A 1, 775–782.CrossRefGoogle ScholarPubMed
Geisler, W.S. (1989). Sequential ideal-observer analysis of visual discriminations. Psychological Review 96, 267–314.CrossRefGoogle ScholarPubMed
Geisler, W.S. & Albrecht, D.G. (1995). Bayesian analysis of identification in monkey visual cortex: Nonlinear mechanisms and stimulus certainty. Vision Research 35, 2723–2730.CrossRefGoogle ScholarPubMed
Geisler, W.S., Albrecht, D.G., Salvi, R.J. & Saunders, S.S. (1991). Discrimination performance of single neurons: Rate and temporal-pattern information. Journal of Neurophysiology 66, 334–361.CrossRefGoogle ScholarPubMed
Geisler, W.S. & Banks, M.S. (1995). Visual performance. In Handbook of Optics, ed. Bass, M., pp. 25.1–25.55. New York: McGraw-Hill.Google Scholar
Green, D.M. & Swets, J.A. (1974). Signal Detection Theory and Psy-chophysics. New York: Krieger.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.CrossRefGoogle ScholarPubMed
Hawken, M.J. & Parker, A.J. (1990). Detection and discrimination mechanisms in the striate cortex of the Old-World monkey. In Vision: Coding and Efficiency, ed. Blakemore, C, pp. 103–116. Cambridge, Massachusetts: Cambridge University Press.Google Scholar
Heeger, D.J. (1991). Computational model of cat striate physiology. In Computational Model of Visual Perception, ed. Landy, M.S. & Movshon, A., pp. 119–133. Cambridge, Massachusetts: The MIT press.Google Scholar
Heeger, D.J. (1992 a). Half-squaring in responses of cat striate cells. Visual Neuroscience 9, 427–443.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1992 b) Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 191–197.Google ScholarPubMed
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
Hubel, D.H. & Wiesel, T.N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology (London) 195, 215–243.CrossRefGoogle ScholarPubMed
Kiper, D.C. & Kiorpes, L. (1994). Suprathreshold contrast sensitivity in experimentally strabismic monkeys. Vision Research 34, 1575–1583.CrossRefGoogle ScholarPubMed
Legge, G.E. & Foley, J.M. (1980). Contrast masking in human vision. Journal of the Optical Society of America 70, 1458–1470.CrossRefGoogle ScholarPubMed
Legge, G.E. & Kersten, D. (1987). Contrast discrimination in peripheral vision. Journal of the Optical Society of America A 4, 1594–1598.CrossRefGoogle ScholarPubMed
Mood, A.M. & Gaybill, F.A. (1963). Introduction to the Theory of Statistics (second ed.). San Francisco, California: McGraw-Hill.Google Scholar
Orban, G.A. (1984). Neuronal Operations in the Visual Cortex. New York: Springer-Verlag.CrossRefGoogle Scholar
Palmer, L.A., Jones, J.P. & Stepnoski, R.A. (1991). Striate receptive fields as linear filters: Characterization in two dimensions of space. In The Neural Basis of Visual Function, ed. Leventhal, A.G., pp. 246–265. Boston, Massachusetts: CRC Press.Google Scholar
Parker, A.J., & Hawken, M.J. (1985). The capabilities of cortical cells in spatial-resolution tasks. Journal of the Optical Society of America A 2, 1101–1114.CrossRefGoogle ScholarPubMed
Parker, A.J. & Newsome, W.T. (1998). Sense and the single neuron: Probing the physiology of perception. Annual Review of Neuroscience 21 (in press).CrossRefGoogle ScholarPubMed
Pelli, D.G. (1985). Uncertainty explains many aspects of visual contrast detection and discrimination. Journal of the Optical Society of America A 2, 1508–1532.CrossRefGoogle ScholarPubMed
Richter, E.S. & Yager, D. (1984). Spatial-frequency difference thresholds for central and peripheral viewing. Journal of the Optical Society of America A 1, 1136–1139.CrossRefGoogle ScholarPubMed
Robson, J.G. (1975). Receptive fields: Spatial and intensive representation of the the visual image. In Handbook of Perception Vol. 5: Vision, ed. Carterette, E.C. & Friedman, M.P., pp. 81–112. New York: Academic Press.Google Scholar
Sclar, G. (1987). Expression of “retinal” contrast gain control by neurons of the cat's lateral geniculate nucleus. Experimental Brain Research 66, 589–596.CrossRefGoogle ScholarPubMed
Sclar, G. & Freeman, R.D. (1982). Orientation selectivity in the cat's striate cortex is invariant with contrast. Experimental Brain Research 46, 457–461.CrossRefGoogle Scholar
Seay, C.A. & Geisler, W.S. (1995). Signal detection analysis of contrast matching and discrimination. Investigative Ophthalmology & Visual Science Supplement 36/7, S903.Google Scholar
Seay, C.A., Geisler, W.S. & Albrecht, D.G. (1996). A neuron sampling model of spatial vision. Investigative Ophthalmology & Visual Science Supplement 37/3, S233.Google Scholar
Shapley, R.M. & Lennie, P. (1985). Spatial frequency analysis in the visual system. Annual Review of Neuroscience 8, 547–583.CrossRefGoogle ScholarPubMed
Shapley, R.M. & Victor, J.D. (1979). The contrast gain control of the cat retina. Vision Research 19, 431–434.CrossRefGoogle ScholarPubMed
Snowden, R.J., Treue, S. & Andersen, R.A. (1992). The response of neurons in areas VI and MT of the alert rhesus monkey to moving random dot patterns. Experimental Brain Research 88, 389–400.CrossRefGoogle Scholar
Softky, W.R. & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps. Journal of Neuroscience 13, 334–350.CrossRefGoogle ScholarPubMed
Talbot, W.R, Darian-Smith, I., Kornhuber, H.H. & Mountcastle, V.B. (1968). The sense of flutter-vibration: Comparison of human capacity with response patterns of mechano-receptive afferents from the monkey hand. Journal of Neurophysiology 31, 301–334.CrossRefGoogle Scholar
Tolhurst, D.J., Movshon, J.A. & Dean, A.F. (1983). The statistical reliability of signals in single neurons in the cat and monkey visual cortex. Vision Research 23, 775–785.CrossRefGoogle Scholar
Vocels, R., Spileers, W. & Orban, G.A. (1989). The response variability of striate cortical neurons in the behaving monkey. Experimental Brain Research 77, 432–436.Google Scholar
Watson, A.B. (1983). Detection and recognition of simple spatial forms. In Physical and Biological Processing of Images, ed. Braddick, O.J. & Sleigh, A.C., pp. 110–114. Berlin: Springer-Verlag.Google Scholar
Watson, A.B. (1986). Temporal sensitivity. In Handbook of Perception and Human Performance, ed. Boff, K.R., Kaufman, L. & Thomas, J.P., pp. 1–43. New York: John Wiley and Sons.Google Scholar