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The computational basis of an identified neuronal circuit for elementary motion detection in dipterous insects

Published online by Cambridge University Press:  01 July 2004

CHARLES M. HIGGINS ,
JOHN K. DOUGLASS  and
NICHOLAS J. STRAUSFELD
CHARLES M. HIGGINS
Affiliation:
Arizona Research Laboratories, Division of Neurobiology, University of Arizona, Tucson Department of Electrical and Computer Engineering, University of Arizona, Tucson
JOHN K. DOUGLASS
Affiliation:
Arizona Research Laboratories, Division of Neurobiology, University of Arizona, Tucson
NICHOLAS J. STRAUSFELD
Affiliation:
Arizona Research Laboratories, Division of Neurobiology, University of Arizona, Tucson
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Abstract

Based on comparative anatomical studies and electrophysiological experiments, we have identified a conserved subset of neurons in the lamina, medulla, and lobula of dipterous insects that are involved in retinotopic visual motion direction selectivity. Working from the photoreceptors inward, this neuronal subset includes lamina amacrine (α) cells, lamina monopolar (L2) cells, the basket T-cell (T1 or β), the transmedullary cell Tm1, and the T5 bushy T-cell. Two GABA-immunoreactive neurons, the transmedullary cell Tm9 and a local interneuron at the level of T5 dendrites, are also implicated in the motion computation. We suggest that these neurons comprise the small-field elementary motion detector circuits the outputs of which are integrated by wide-field lobula plate tangential cells. We show that a computational model based on the available data about these neurons is consistent with existing models of biological elementary motion detection, and present a comparable version of the Hassenstein-Reichardt (HR) correlation model. Further, by using the model to synthesize a generic tangential cell, we show that it can account for the responses of lobula plate tangential cells to a wide range of transient stimuli, including responses which cannot be predicted using the HR model. This computational model of elementary motion detection is the first which derives specifically from the functional organization of a subset of retinotopic neurons supplying the lobula plate. A key prediction of this model is that elementary motion detector circuits respond quite differently to small-field transient stimulation than do spatially integrated motion processing neurons as observed in the lobula plate. In addition, this model suggests that the retinotopic motion information provided to wide-field motion-sensitive cells in the lobula is derived from a less refined stage of processing than motion inputs to the lobula plate.

Keywords

Directional selectivityElementary motion detectionLobula plate tangential cellsVisual motionInsects
Type
Research Article
Information
Visual Neuroscience , Volume 21 , Issue 4 , July 2004 , pp. 567 - 586
Copyright
2004 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(2), 284–299.CrossRefGoogle Scholar
Barlow, H.B. & Levick, W.R. (1965). The mechanism of directionally selective units in rabbit's retina. Journal of Physiology (London) 178, 477–504.CrossRefGoogle Scholar
Bausenwein, B. & Fischbach, K.-F. (1992). Separation of functional pathways in the fly's medulla: combination of 2-deoxyglucose studies with anatomical fine analysis. In Nervous Systems: Principles of Design and Function, ed. Singh, R.N., pp. 223–239. New Delhi, India: Wiley Eastern.
Borst, A. (2001). Modelling fly motion vision. In Computational Neuroscience: A Comprehensive Approach, ed. Feng, Jianfeng, pp. 401–434. Boca Raton, FL: CRC Press.
Borst, A. and Haag, J. (2002). Neural networks in the cockpit of the fly. Journal of Comparative Physiology A 188, 419–437.Google Scholar
Boschek, C.B. (1971). On the fine structure of the peripheral retina and the lamina of the fly, Musca domestica. Zeitschrift für Zellforschung und Mikroskopische Anatomie 110, 336–349.Google Scholar
Buchner, E. & Buchner, S. (1984). Neuroanatomical mapping of visually induced neuron activity in insects by 3H-deoxyglucose. In Photoreception and Vision in Invertebrates, ed. Ali, M.A., pp. 623–634. New York: Plenum Press.CrossRef
Buchner, E., Buchner, S., & Hengstenberg, R. (1979). 2-Deoxy-D-glucose maps movement-specific nervous activity in the second visual ganglion of Drosophila. Science 205, 687–688.CrossRefGoogle Scholar
Buschbeck, E.K. & Strausfeld, N.J. (1996). Visual motion-detection circuits in flies: Small field retinotopic elements responding to motion are evolutionarily conserved across taxa. Journal of Neuroscience 16, 4563–4578.Google Scholar
Campos-Ortega, J.A. & Strausfeld, N.J. (1973). Synaptic connections of intrinsic cells and basket arborizations in the external plexiform layer of the fly's eye. Brain Research 59, 119–136.CrossRefGoogle Scholar
Clifford, C.W.G. & Ibbotson, M.R. (2003). Fundamental mechanisms of visual motion detection: Models, cells and functions. Progress in Neurobiology 68, 409–437.Google Scholar
Collett, T.S. & Land, M.F. (1978). How hoverflies compute interception courses. Journal of Comparative Physiology 125, 191–204.CrossRefGoogle Scholar
Coombe, P.E., Srinivasan, M.V., & Guy, R.G. (1989). Are the large monopolar cells of the insect lamina on the optomotor pathway? Journal of Comparative Physiology A 166, 23–35.Google Scholar
de Ruyter van Steveninck, R., Lewen, G.D., Strong, S.P., Koberle, R., & Bialek, W. (1997). Reproducibility and variability in neural spike trains. Science 275, 1805–1808.CrossRefGoogle Scholar
Douglass, J.K. & Strausfeld, N.J. (1995). Visual motion detection circuits in flies: Peripheral motion computation by identified small field retinotopic neurons. Journal of Neuroscience 15, 5596–5611.Google Scholar
Douglass, J.K. & Strausfeld, N.J. (1996). Visual motion detection circuits in flies: Parallel direction-and non-direction sensitive pathways between the medulla and lobula plate. Journal of Neuroscience 16, 4551–4562.Google Scholar
Douglass, J.K. & Strausfeld, N.J. (1998). Functionally and anatomically segregated visual pathways in the lobula complex of a calliphorid fly. Journal of Comparative Neurology 396, 84–104.3.0.CO;2-E>CrossRefGoogle Scholar
Douglass, J.K. & Strausfeld, N.J. (2004). Sign-conserving amacrines in the fly's external plexiform layer. Visual Neuroscience (in review).Google Scholar
Dvorak, D.R., Bishop, L.G., & Eckert, H.E. (1975). Intracellular recording and staining of directionally selective motion detecting neurons in fly optic lobe. Vision Research 15, 451–453.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 Scholar
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 Scholar
Egelhaaf, M., Borst, A., Warzecha, A.K., Flecks, S., & Wildemann, A. (1993). Neural circuit tuning fly visual neurons to motion of small objects. II. Input organization of inhibitory circuit elements revealed by electrophysiological and optical recording techniques. Journal of Neurophysiology 69, 340–351.Google Scholar
Egelhaaf, M., Hausen, K., Reichardt, W., & Wehrhahn, C. (1988). Visual course control in flies relies on neuronal computation of object and background motion. Trends in Neuroscience 11, 351–358.CrossRefGoogle Scholar
Fischbach, K.-F. & Dittrich, A.P.M. (1989). The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure. Cell and Tissue Research 258, 441–475.Google Scholar
Franceschini, N., Riehle, A., & Le Nestour, A. (1989). Directionally selective motion detection by insect neurons. In Facets of Vision, ed. Stavenga, D.G., Hardie & R.C., chapter 17, pp. 360–390. Springer-Verlag.CrossRef
Fried, S.I., Münch, T.A., & Werblin, F.S. (2002). Mechanisms and circuitry underlying directional selectivity in the retina. Nature 420, 411–414.CrossRefGoogle Scholar
Gilbert, C. & Strausfeld, N.J. (1991). The functional organization of male-specific visual neurons in flies. Journal of Comparative Physiology A 169, 395–411.Google Scholar
Harris, R.A., O'Carroll, D.C., & Laughlin, S.B. (1999). Adaptation and the temporal delay filter of fly motion detectors. Vision Research 39, 2603–2613.CrossRefGoogle Scholar
Hassenstein, B. (1950). Wandernde geometrische Interferenzfiguren im Insektenauge. Naturwissenschaften 37, 45–46.CrossRefGoogle Scholar
Hassenstein, B. (1951). Ommatidienraster und afferente Bewegungsintegration. Zeitschrift für vergleichende Physiologie 33, 301–326.Google Scholar
Hassenstein, B. (1958). Die Stärke von optokinetschen Reaktionen auf verschiedene Mustergeschwindigkeiten. Zeitschrift für Naturforschung 13b, 1–6.Google Scholar
Hassenstein, B. & Reichardt, W. (1956). Systemtheorische analyse der Zeit-, Reihenfolgen-und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus. Zeitschrift für Naturforschung 11b, 513–524.Google Scholar
Hausen, K. (1982). Motion sensitive interneurons in the optomotor system of the fly. I. The horizontal cells: Structure and signals. Biological Cybernetics 45, 143–156.Google Scholar
Hausen, K. (1984). The lobula-complex of the fly: Structure, function, and significance in visual behaviour. In Photoreception and Vision in Invertebrates, ed. Ali, M.A., pp. 523–599. New York: Plenum Press.CrossRef
Hildreth, E.C. & Koch, C. (1987). The analysis of visual-motion-from computational theory to neuronal mechanisms. Annual Review of Neuroscience 10, 477–533.CrossRefGoogle Scholar
Ibbotson, M.R. (2001). Identification of mechanisms underlying motion detection in mammals. In Motion Vision: Computational, Neural, and Ecological Constraints, ed. Zanker, J.M. & Zeil, J., pp. 57–65. Berlin: Springer.
James, A.C. & Osorio, D. (1996). Characterisation of columnar neurons and visual signal processing in the medulla of the locust optic lobe by system identification techniques. Journal of Comparative Physiology A 178, 183–199.Google Scholar
Järvilehto, M. & Zettler, F. (1973). Electrophysiological-histological studies on some functional properties of visual cells and second-order neurons of an insect retina. Zeitshchrift für Zellforschung und Mikroskopische Anatomie 136, 291–306.CrossRefGoogle Scholar
Koch, C. (1999). Biophysics of Computation: Information Processing in Single Neurons. New York: Oxford University Press.
Koch, C., Poggio, T., & Torre, V. (1982). Retinal ganglion cells: A functional interpretation of dendritic morphology. Philosophical Transactions of the Royal Society B (London) 298, 227–264.CrossRefGoogle Scholar
Krapp, H.G., Hengstenberg, B., & Hengstenberg, R. (1998). Dendritic structure and receptive-field organization of optic flow processing interneurons in the fly. Journal of Neurophysiology 79, 1902–1917.Google Scholar
Laughlin, S. (1984). The roles of parallel channels in early visual processing by the arthropod compound eye. In Photoreception and Vision in Invertebrates, ed. Ali, M.A., pp. 457–482. New York: Plenum Press.CrossRef
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.CrossRefGoogle Scholar
Nilsson, D.-E. (1989). Optics and evolution of the compound eye. In Facets of Vision, ed. Stavenga, D.G., Hardie & R.C., Chapter 3, pp. 30–73. Berlin: Springer-Verlag.CrossRef
O'Carroll, D.C. (2001). Motion adaptation and evidence for parallel processing in the lobula plate of the bee-fly Bombylius major. In Motion Vision: Computational, Neural, and Ecological Constraints, ed. Zanker, J.M. & Zeil, J., pp. 381–394. Berlin: Springer.
Pix, W., Zanker, J.M., & Zeil, J. (2000). The optomotor response and spatial resolution of the visual system in male Xenos vesparum (strepsiptera). Journal of Experimental Biology 203, 3397–3409.Google Scholar
Reichardt, W., Egelhaaf, M., & Guo, A.K. (1989). Processing of figure and background motion in the visual-system of the fly. Biological Cybernetics 61, 327–345.CrossRefGoogle Scholar
Rivera-Alvidrez, Z. & Higgins, C.M. (2004). Contrast saturation in a neuronally-based model of elementary motion detection. Neurocomputing. (In Press)Google Scholar
Sinakevitch, I. & Strausfeld, N.J. (2004). Chemical neuroanatomy of the fly's movement detection pathway. Journal of Comparative Neurology 486, 6–23.CrossRefGoogle Scholar
Stavenga, D.G. (1979). Pseudopupils of compound eyes. In Handbook of Sensory Physiology, ed. Autrum, H. & Vol. VII/6A, chapter 7, pp. 357–439. Berlin: Springer.CrossRef
Sterling, P. (2002). How neurons compute direction. Nature 420, 375–376.CrossRefGoogle Scholar
Strausfeld, N.J. (1976). Atlas of an insect brain. Heidelberg, New York: Springer.CrossRef
Strausfeld, N.J. & Campos-Ortega, J.A. (1977). Vision in insects: Pathways possibly underlying neural adaptation and lateral inhibition. Science 195, 894–897.CrossRefGoogle Scholar
Strausfeld, N.J. & Lee, J.K. (1991). Neuronal basis for parallel visual processing in the fly. Visual Neuroscience 7, 13–33.CrossRefGoogle Scholar
Torre, V. & Poggio, T. (1978). Synaptic mechanism possibly underlying directional selectivity to motion. Proceedings of the Royal Society B (London) 202, 409–416.CrossRefGoogle Scholar
Van Santen, J.P.H. & Sperling, G. (1985). Elaborated Reichardt detectors. Journal of the Optical Society of America A 2, 300–320.CrossRefGoogle Scholar
Wicklein, M. & Strausfeld, N.J. (2000). The organization and significance of neurons detecting change of depth in the hawk moth Manduca sexta. Journal of Comparative Neurology 424(2), 356–376.3.0.CO;2-T>CrossRefGoogle Scholar