Hostname: page-component-77c89778f8-7drxs Total loading time: 0 Render date: 2024-07-17T02:42:30.654Z Has data issue: false hasContentIssue false
  • English
  • Français

Gain, noise, and contrast sensitivity of linear visual neurons

Published online by Cambridge University Press:  02 June 2009

Andrew B. Watson
Andrew B. Watson
Affiliation:
Vision Group, NASA Ames Research Center, Moffett Field
Get access Rights & Permissions [Opens in a new window]

Abstract

Contrast sensitivity is a measure of the ability of an observer to detect contrast signals of particular spatial and temporal frequencies. A formal definition of contrast sensitivity that can be applied to individual linear visual neurons is derived. A neuron is modeled by a contrast transfer function and its modulus, contrast gain, and by a noise power spectrum. The distributions of neural responses to signal and blank presentations are derived, and from these, a definition of contrast sensitivity is obtained. This formal definition may be used to relate the sensitivities of various populations of neurons, and to relate the sensitivities of neurons to that of the behaving animal.

Keywords

Contrast sensitivityMaintained dischargeNoiseSignal detection theoryLinear systemsPower spectrum
Type
Research Articles
Information
Visual Neuroscience , Volume 4 , Issue 2 , February 1990 , pp. 147 - 157
Copyright
Copyright © Cambridge University Press 1990

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

Barlow, H.B. & Levick, W.R. (1969). Three factors affecting the reliable detection of light by retinal ganglion cells of the cat. Journal of Physiology 200, 1–24.CrossRefGoogle ScholarPubMed
Cornsweet, T.N. (1962). The staircase method in psychophysics. American Journal of Psychology 75, 485–491.CrossRefGoogle Scholar
Dean, A.F. (1981). The variability of discharge of simple cells in the cat striate cortex. Experimental Brain Research 44, 437–440.CrossRefGoogle ScholarPubMed
Derrington, A.M. & Lennie, P. (1982). The influence of temporal frequency and adaptation level on receptive-field organization of retinal ganglion cells in cat. Journal of Physiology 333, 343–366.CrossRefGoogle ScholarPubMed
Derrington, A.M. & Lennie, P. (1984). Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. Journal of Physiology (London) 357, 219–240.CrossRefGoogle ScholarPubMed
Enroth-Cugell, C., Robson, J.G., Schweitzer-Tong, D. & Watson, A.B. (1983). Spatio-temporal interactions in cat retinal ganglion cells showing linear spatial summation. Journal of Physiology (London) 341, 279–307.CrossRefGoogle ScholarPubMed
Frishman, L.J. & Levine, M.W. (1983). Statistics of the maintained discharge of cat retinal ganglion cells. Journal of Physiology 339, 475–494.CrossRefGoogle ScholarPubMed
Green, D.M. & Swets, J.A. (1966). Signal Detection Theory and Psychophysics. New York: Wiley.Google Scholar
Green, D.M. & Luce, R.D. (1975). Parallel psychometric functions from a set of independent detectors. Psychological Review 82, 483–486.CrossRefGoogle 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 (10), 1285–1308.CrossRefGoogle ScholarPubMed
Hawken, M.J. & Parker, A.J. (1984). Contrast sensitivity and orientation selectivity in lamina IV of the striate cortex of old world monkeys. Experimental Brain Research 54, 367–372.CrossRefGoogle ScholarPubMed
Levine, M.W. & Troy, J.B. (1986). The variability of the maintained discharge of cat dorsal lateral geniculate cells. Journal of Physiology 375, 339–359.CrossRefGoogle ScholarPubMed
MacGregor, R.J. & Lewis, E.R. (1977). Neural Modeling. New York: Plenum Press.CrossRefGoogle Scholar
Nachmias, J. (1981). On the psychometric function for contrast detection. Vision Research 21(2), 215–223.CrossRefGoogle ScholarPubMed
Papoulis, A. (1965). Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill.Google Scholar
Parzen, E. (1962). Stochastic Processes. San Francisco: Holden Day.Google Scholar
Pelli, D.G. (1986). Uncertainty explains many aspects of visual contrast detection and discrimination. Journal of the Optical Society of America A 2(9), 1508–1532.CrossRefGoogle Scholar
Quick, R.F. (1974). A vector magnitude model of contrast detection. Kybernetik 16, 65–67.CrossRefGoogle ScholarPubMed
Robson, J.G. & Graham, N. (1981). Probability summation and regional variation in contrast sensitivity across the visual field. Vision Research 21, 409–418.CrossRefGoogle ScholarPubMed
Robson, J.G. & Troy, J.B. (1987). Nature of the maintained discharge of Q, X, and Y retinal ganglion cells in the cat. Journal of the Optical Society of America A 4, 2301–2307.CrossRefGoogle Scholar
Rodieck, R.W. (1967). Maintained activity of cat retinal ganglion cells. Journal of Neurophysiology 30, 1043.CrossRefGoogle ScholarPubMed
Stremler, F.G. (1982). Introduction to Communication Systems (2 ed.). Reading, Massachusetts: Addison-Wesley.Google Scholar
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(8), 775–785.CrossRefGoogle Scholar
Tolhurst, D.J., Movshon, J.A. & Thompson, I.D. (1981). The dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast. Experimental Brain Research 41, 414–419.Google ScholarPubMed
Troy, J.B. (1983 a). Spatial contrast sensitivities of X- and Y-type neurones in the cat's dorsal lateral geniculate nucleus. Journal of Physiology 344, 399–417.CrossRefGoogle ScholarPubMed
Troy, J.B. (1983 b). Spatio-temporal interaction in neurones of the cat's dorsal lateral geniculate nucleus. Journal of Physiology 344, 419–432.CrossRefGoogle ScholarPubMed
Watson, A.B. (1979). Probability summation over time. Vision Research 19, 515–522.CrossRefGoogle ScholarPubMed
Watson, A.B. (1990). Theoretical constraints on the contrast sensitivity of linear cortical neurons (in preparation).CrossRefGoogle Scholar
Watson, A.B. & Fitzhugh, A. (1990). The method of constant stimuli is inefficient. Perception and Psychophysics 47(1), 87–91.CrossRefGoogle ScholarPubMed
Watson, A.B. & Pelli, D.G. (1983). QUEST: A Bayesian adaptive psychometric method. Perception and Psychophysics 33(2), 113–120.CrossRefGoogle Scholar
Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics 18, 292–297.CrossRefGoogle Scholar
Wetherill, G.B. & Levitt, H. (1965). Sequential estimation of points on a psychometric function. British Journal of Mathematical Statistics 18, 1–10.CrossRefGoogle ScholarPubMed
Wolfram, S. (1988). Mathematica: A System for Doing Mathematics by Computer. New York: Addison-Wesley.Google Scholar