Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T00:07:14.932Z Has data issue: false hasContentIssue false
  • English
  • Français

An adaptive Reichardt detector model of motion adaptation in insects and mammals

Published online by Cambridge University Press:  02 June 2009

Colin W.G. Clifford ,
Michael R. Ibbotson  and
Keith Langley
Colin W.G. Clifford
Affiliation:
Department of Psychology, University College London, Gower Street, London WC1E 6BT, UK Centre for Visual Science, Australian National University, Canberra, Australia
Michael R. Ibbotson
Affiliation:
Developmental Neurobiology, Research School of Biological Sciences, Australian National University, Canberra ACT 2601, Australia
Keith Langley
Affiliation:
Department of Psychology, University College London, Gower Street, London WC1E 6BT, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

There are marked similarities in the adaptation to motion observed in wide-field directional neurons found in the mammalian nucleus of the optic tract and cells in the insect lobula plate. However, while the form and time scale of adaptation is comparable in the two systems, there is a difference in the directional properties of the effect. A model based on the Reichardt detector is proposed to describe adaptation in mammals and insects, with only minor modifications required to account for the differences in directionality. Temporal-frequency response functions of the neurons and the model are shifted laterally and compressed by motion adaptation. The lateral shift enhances dynamic range and differential motion sensitivity. The compression is not caused by fatigue, but is an intrinsic property of the adaptive process resulting from interdependence of temporal-frequency tuning and gain in the temporal filters of the motion detectors.

Keywords

Motion adaptationComputational modelReichardt detector
Type
Research Articles
Information
Visual Neuroscience , Volume 14 , Issue 4 , July 1997 , pp. 741 - 749
Copyright
Copyright © Cambridge University Press 1997

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

Attneave, F. (1954). Informational aspects of visual perception. Psychological Review 61, 183–193.CrossRefGoogle ScholarPubMed
Barlow, H.B. (1961). The coding of sensory messages. In Current Problems in Animal Behaviour, ed. Thorpe, W.H. & Zangwill, O.L., pp. 331–360. New York: Cambridge University Press.Google Scholar
Borst, A. & Bahde, S. (1986). What kind of movement detector is triggering the landing response of the housefly? Biological Cybernetics 55, 59–69.CrossRefGoogle Scholar
Borst, A. & Egelhaaf, M. (1987). Temporal modulation of luminance adapts time constant of fly movement detectors. Biological Cybernetics 56, 209–215.CrossRefGoogle Scholar
Borst, A. & Egelhaaf, M. (1989). Principles of visual motion detection. Trends in Neurosciences 12, 297–306.CrossRefGoogle ScholarPubMed
Clarkson, P.M. (1993). Optimal and Adaptive Signal Processing. Optimal and Adaptive Signal Processing. Boca Raton, Florida: CRC press.Google Scholar
Clifford, C.W.G. & Langley, K. (1996 a). Psychophysics of motion adaptation parallels insect electrophysiology. Current Biology 6, 1340–1342.CrossRefGoogle ScholarPubMed
Clifford, C.W.G. & Langley, K. (1996 b). A model of temporal adaptation in fly motion vision. Vision Research 36, 2595–2608.CrossRefGoogle Scholar
De Ruyter van Steveninck, R.R., Zaagman, W.H. & Masterbroek, H.A.K. (1986). Adaptation of transient responses of a movementsensitive neuron in the visual system of the blowfly Calliphora erythrocephala. Biological Cybernetics 54, 223–236.CrossRefGoogle Scholar
Egelhaaf, M. & Borst, A. (1989). Transient and steady-state response properties of movement detectors. Journal of the Optical Society of America A 6, 116–127.CrossRefGoogle ScholarPubMed
Egelhaaf, M., Borst, A. & Reichardt, W. (1989). Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly's nervous system. Journal of the Optical Society of America A, 6, 1070–1087.CrossRefGoogle ScholarPubMed
Giaschi, D., Douglas, R., Marlin, S. & Cynader, M. (1993). The time course of direction-selective adaptation in simple and complex cells in cat striate cortex. Journal of Neurophysiology 70, 2024–2034.CrossRefGoogle ScholarPubMed
Greenlee, M.W. & Heitger, F. (1988). The functional role of contrast adaptation. Vision Research 28, 791–797.CrossRefGoogle ScholarPubMed
Hammond, P., Mouat, G.S.V. & Smith, A.T. (1988). Neural correlates of motion after-effects in cat striate cortical neurones: Monocular adaptation. Experimental Brain Research 72, 1–20.CrossRefGoogle ScholarPubMed
Ibbotson, M.R., Clifford, C.W.G. & Mark, R.F. (1997). Adaptation enhances the dynamic range of directional neurons in the nucleus of the optic tract. Journal of Neurophysiology (in press).Google Scholar
Ibbotson, M.R. & Mark, R.F. (1996). Impulse responses distinguish two classes of directional motion-sensitive neurons in the nucleus of the optic tract Journal of Neurophysiology 75, 996–1007.CrossRefGoogle ScholarPubMed
Ibbotson, M.R., Mark, R.F. & Maddess, T. (1994). Spatiotemporal response properties of direction-selective neurons in the nucleus of the optic tract and the dorsal terminal nucleus of the wallaby, Macropus eugenii. Journal of Neurophysiology 72, 2927–2943.CrossRefGoogle ScholarPubMed
Ibbotson, M.R. & Maddess, T. (1994). The effects of adaptation to visual stimuli on the velocity of subsequent ocular following responses. Experimental Brain Research 99, 148–154.CrossRefGoogle ScholarPubMed
Laughlin, S.B. (1989). Coding efficiency and design in visual processing. In Facets of Vision, ed. Stavenga, D.G. & Hardie, R.C., pp. 213–234. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Maddess, T., Dubois, R.A. & Ibbotson, M.R. (1991). Response properties and adaptation of neurones sensitive to image motion in the butterfly Papilio Aegeus. Journal of Experimental Biology 161, 171–199.CrossRefGoogle Scholar
Maddess, T. & Ibbotson, M.R. (1992). Human ocular response following responses are plastic: Evidence for control by temporal frequency-dependent cortical adaptation. Experimental Brain Research 91, 525–538.CrossRefGoogle ScholarPubMed
Maddess, T. & Laughlin, S.B. (1985). Adaptation of the motion sensitive neuron H1 is generated locally and governed by contrast frequency. Proceedings of the Royal Society B (London) 225, 251–275.Google Scholar
Maddess, T., McCourt, M.E., Blakesee, B. & Cunningham, R.B. (1988). Factors governing the adaptation of cells in area-17 of the cat visual cortex. Biological Cybernetics 59, 229–236.CrossRefGoogle ScholarPubMed
Ohzawa, I., Sclar, R.D. & Freeman, R.D. (1982). Contrast gain control in the cat visual cortex. Nature 298, 266–268.CrossRefGoogle ScholarPubMed
Reichardt, W. (1961). Autocorrelation, a principle for evaluation of sensory information by the central nervous system. In Principles of Sensory Communication, ed. Rosenblith, W.A., pp. 303–317. New York: Wiley.Google Scholar
Shi, Jian & Horridge, G.A. (1991). The HI neuron measures change in velocity irrespective of contrast frequency, mean velocity or velocity modulation frequency. Proceedings of The Royal Society B (London) 331, 205–211.Google Scholar
Srinivasan, M.V. (1983). The impulse response of a movement detecting neuron and its interpretation. Vision Research 23, 659–663.CrossRefGoogle ScholarPubMed
van Santen, J.P.H. & Sperling, G. (1985). Elaborated Reichardt detectors. Journal of the Optical Society of America A 2, 300–321.CrossRefGoogle ScholarPubMed
Vautin, R.G. & Berkley, M.A. (1977). Responses of single cells in cat visual cortex to prolonged stimulus movement: Neural correlates of visual after-effects. Journal of Neurophysiology 40, 1051–1065.CrossRefGoogle Scholar
Zanker, J.M. (1995). Of models and men: Mechanisms of human motion perception. In Early Vision and Beyond, ed. Papathomas, T.V., Gorea, A. & Chubb, C., pp. 156–165. Cambridge, Massachusetts: MIT Press.Google Scholar