Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T11:59:51.092Z Has data issue: false hasContentIssue false
  • English
  • Français

A neuronally based model of contrast gain adaptation in fly motion vision

Published online by Cambridge University Press:  22 August 2011

ZULEY RIVERA-ALVIDREZ ,
ICHI LIN  and
CHARLES M. HIGGINS
ZULEY RIVERA-ALVIDREZ
Affiliation:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona
ICHI LIN
Affiliation:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona
CHARLES M. HIGGINS*
Affiliation:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona Department of Neuroscience, University of Arizona, Tucson, Arizona
*
† Address correspondence and reprint requests to: Charles M. Higgins, Department of Neuroscience, University of Arizona, 1040 E 4th St, Tucson, AZ 85721. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Motion-sensitive neurons in the visual systems of many species, including humans, exhibit a depression of motion responses immediately after being exposed to rapidly moving images. This motion adaptation has been extensively studied in flies, but a neuronal mechanism that explains the most prominent component of adaptation, which occurs regardless of the direction of motion of the visual stimulus, has yet to be proposed. We identify a neuronal mechanism, namely frequency-dependent synaptic depression, which explains a number of the features of adaptation in mammalian motion-sensitive neurons and use it to model fly motion adaptation. While synaptic depression has been studied mainly in spiking cells, we use the same principles to develop a simple model for depression in a graded synapse. By incorporating this synaptic model into a neuronally based model for elementary motion detection, along with the implementation of a center-surround spatial band-pass filtering stage that mimics the interactions among a subset of visual neurons, we show that we can predict with remarkable success most of the qualitative features of adaptation observed in electrophysiological experiments. Our results support the idea that diverse species share common computational principles for processing visual motion and suggest that such principles could be neuronally implemented in very similar ways.

Keywords

Motion adaptationElementary motion detectionLobula plate tangential cellsVisual motionInsect vision
Type
Research Articles
Information
Visual Neuroscience , Volume 28 , Issue 5 , September 2011 , pp. 419 - 431
Copyright
Copyright © Cambridge University Press 2011

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

Abbott, L.F., Sen, K., Varela, J.A. & Nelson, S.B. (1997). Synaptic depression and cortical gain control. Science 275, 220–224.CrossRefGoogle ScholarPubMed
Barlow, H.B. & Levick, W.R. (1965). The mechanism of directionally selective units in rabbit’s retina. The Journal of Physiology 178, 477–504.Google Scholar
Borst, A., Egelhaaf, M. & Haag, J. (1995). Mechanisms of dendritic integration underlying gain control in fly motion-sensitive interneurons. Journal of Computational Neuroscience 2, 5–18.CrossRefGoogle ScholarPubMed
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.Google Scholar
Chance, F.S., Nelson, S.B. & Abbott, L.F. (1998). Synaptic depression and the temporal response characteristics of v1 cells. The Journal of Neuroscience 18, 4785–4799.Google 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, Sensory, Neural, and Behavioral Physiology 166, 23–35.Google Scholar
Douglass, J.K. & Strausfeld, N.J. (2004). Sign-conserving amacrines in the fly’s external plexiform layer. Visual Neuroscience 22, 345–358.CrossRefGoogle Scholar
Dyhr, J. & Higgins, C. (2010). Non-directional motion detectors can be used to mimic optic flow dependent behaviors. Biological Cybernetics 103, 433–446.CrossRefGoogle ScholarPubMed
Egelhaaf, M. & Borst, A. (1989). Transient and steady-state response properties of movement detectors. Journal of the Optical Society of America A, Optics and Image Science 6, 116–127.Google 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, Optics and Image Science 6, 1070–1087.CrossRefGoogle ScholarPubMed
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.Google Scholar
Harris, R.A., O’Carroll, D.C. & Laughlin, S.B. (2000). Contrast gain reduction in fly motion adaptation. Neuron 28, 595–606.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. (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. Plenum Press.Google Scholar
Higgins, C.M., Douglass, J.K. & Strausfeld, N.J. (2004). The computational basis of an identified neuronal circuit for elementary motion detection in dipterous insects. Visual Neuroscience 21, 567–586.CrossRefGoogle ScholarPubMed
Juusola, M., Weckstrom, M., Uusitalo, R.O., Korenberg, M.J. & French, A.S. (1995). Nonlinear models of the first synapse in the light-adapted fly retina. Journal of Neurophysiology 74, 2538–2547.CrossRefGoogle ScholarPubMed
Koch, C. (1999). Biophysics of Computation: Information Processing in Single Neurons. New York: Oxford University Press.Google Scholar
Kohn, A. & Movshon, J.A. (2003). Neuronal adaptation to visual motion in area MT of the macaque. Neuron 39, 681–691.Google Scholar
Kurtz, R. (2007). Direction-selective adaptation in fly visual motion-sensitive neurons is generated by an intrinsic conductance-based mechanism. Neuroscience 146, 573–583.CrossRefGoogle ScholarPubMed
Laughlin, S.B., Howard, J. & Blakeslee, B. (1987). Synaptic limitations to contrast coding in the retina of the blowfly Calliphora . Proceedings of the Royal Society of London. Series B, Biological Sciences 231, 437–467.Google 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 of London. Series B, Biological Sciences 225, 251–275.Google Scholar
Melano, T. & Higgins, C.M. (2005). The neuronal basis of direction selectivity in lobula plate tangential cells. Neurocomputing 65–66, 153–159.CrossRefGoogle Scholar
Neher, E. (1998). Vesicle pools and ca2+ domains: New tools for understanding their roles in neurotransmitter release. Neuron 20, 389–399.CrossRefGoogle Scholar
Neri, P. (2007). Fast-scale adaptive changes of directional tuning in fly tangential cells are explained by a static nonlinearity. The Journal of Experimental Biology 210, 3199–3208.CrossRefGoogle ScholarPubMed
Nishikawa, K.C. (2002). Evolutionary convergence in nervous systems: Insights from comparative phylogenetic studies. Brain, Behavior and Evolution 59, 240–249.CrossRefGoogle ScholarPubMed
Reisenman, C., Haag, J. & Borst, A. (2003). Adaptation of response transients in fly motion vision. I: Experiments. Vision Research 43, 1291–1307.Google Scholar
Rivera-Alvidrez, Z. & Higgins, C.M. (2005). Contrast saturation in a neuronally-based model of elementary motion detection. Neurocomputing 65–66, 173–179.CrossRefGoogle Scholar
Sinakevitch, I. & Strausfeld, N.J. (2004). Chemical neuroanatomy of the fly’s movement detection pathway. The Journal of Comparative Neurology 486, 6–23.Google Scholar
Single, S., Haag, J. & Borst, A. (1997). Dendritic computation of direction selectivity and gain control in visual interneurons. The Journal of Neuroscience 17, 6023–6030.Google Scholar
Srinivasan, M.V., Laughlin, S.B. & Dubs, A. (1982). Predictive coding: A fresh view of inhibition in the retina. Proceedings of the Royal Society of London. Series B, Biological Sciences 216, 427–459.Google ScholarPubMed
Strausfeld, N.J. & Campos-Ortega, J.A. (1977). Vision in insects: Pathways possibly underlying neural adaptation and lateral inhibition. Science 195, 894–897.Google Scholar
Strausfeld, N.J. & Nässel, D.R. (1980). Neuroarchitectures serving compound eyes of Crustacea and insects. In Handbook of Sensory Physiology, VII/68, ed. Autrum, H., pp. 1–132. Springer.Google Scholar
Takahashi, M., Kovalchuck, Y. & Attwell, D. (1995). Pre- and postsynaptic determinants of EPSC waveform at cerebellar fiber and Purkinje cell synapses. The Journal of Neuroscience 15, 5693–5707.Google Scholar
Varela, J.A., Sen, K., Gibson, J., Forst, J., Abbott, L.F. & Nelson, S.B. (1997). A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. The Journal of Neuroscience 17, 7926–7940.CrossRefGoogle ScholarPubMed
Wohlgemuth, A. (1911). On the After-Effect of Seen Movement. Cambridge University Press.Google Scholar