Advertisement

Biological Cybernetics

, Volume 93, Issue 4, pp 275–287 | Cite as

Velocity constancy and models for wide-field visual motion detection in insects

  • P. A. Shoemaker
  • D. C. O’Carroll
  • A. D. Straw
Article

Abstract

The tangential neurons in the lobula plate region of the flies are known to respond to visual motion across broad receptive fields in visual space.When intracellular recordings are made from tangential neurons while the intact animal is stimulated visually with moving natural imagery,we find that neural response depends upon speed of motion but is nearly invariant with respect to variations in natural scenery. We refer to this invariance as velocity constancy. It is remarkable because natural scenes, in spite of similarities in spatial structure, vary considerably in contrast, and contrast dependence is a feature of neurons in the early visual pathway as well as of most models for the elementary operations of visual motion detection. Thus, we expect that operations must be present in the processing pathway that reduce contrast dependence in order to approximate velocity constancy.We consider models for such operations, including spatial filtering, motion adaptation, saturating nonlinearities, and nonlinear spatial integration by the tangential neurons themselves, and evaluate their effects in simulations of a tangential neuron and precursor processing in response to animated natural imagery. We conclude that all such features reduce interscene variance in response, but that the model system does not approach velocity constancy as closely as the biological tangential cell.

Keywords

Natural Scene Early Vision Saturate Nonlinearity Motion Adaptation Lobula Plate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

This work was supported by US Air Force SBIR contract F08630-02-C-0013 and by USAir Force IRI grant F62562-01- P-0158. Straw was supported by a fellowship from the Howard Hughes Medical Institute. Data on velocity constancy were contributed in part by T. Rainsford. The authors thank T. Bartolac for data processing and for comments on the manuscript.

Reference

  1. Borst A, Egelhaaf M, Haag J (1995) Mechanisms of dendritic integration underlying gain control in fly motion-sensitive neurons. J Comput Neurosci 2:5–18PubMedCrossRefGoogle Scholar
  2. Buchner E (1976) Elementary movement detectors in an insect visual system. Biol Cybernet 24:85–101CrossRefGoogle Scholar
  3. Clifford CWG, Ibbotson MR (2003) Fundamental mechanisms of visual motion detection: models, cells and functions. Prog Neurobiol 68:409–437CrossRefGoogle Scholar
  4. Clifford CWG, Langley K (1996) Psychophysics of motion adaptation parallels insect electrophysiology. Curr Biol 6:1340–1342PubMedCrossRefGoogle Scholar
  5. de Ruyter van Steveninck R, Zaagman WH, Mastebroek HAK (1986) Adaptation of transient responses of a movement-sensitive neuron in the visual system of the blowfly Calliphora erythrocephala. Biol Cybernet 54:223–236CrossRefGoogle Scholar
  6. Douglass JK, Strausfeld N (1995) Visual motion detection circuits in flies: peripheral motion computation by identified small-field retinotopic neurons. J Neurosci 15:5596–5611PubMedGoogle Scholar
  7. Dror RO, O’Carroll DC, Laughlin SB (2001) Accuracy of velocity estimation by Reichardt correlators. J Opt Soc Am A 18:241–252CrossRefGoogle Scholar
  8. Egelhaaf M, Borst A (1993) A look into the cockpit of the fly: visual orientation, algorithms, and identified neurons. J Neurosci 13:4563–4574PubMedGoogle Scholar
  9. 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. J Opt Soc Am A 6:1070–1087PubMedGoogle Scholar
  10. Fairhall AL, Lewen GD, Bialek W, de Ruyter van Steveninck R (2001) Efficiency and ambiguity in an adaptive neural code. Nature 41:787–792CrossRefGoogle Scholar
  11. Franceschini N, Riehle A, Le Nestour A (1989) Directionally selective motion detection by insect neurons. In: Stavenga DG, Hardie RC (eds) Facets of vision, Springer, Berlin Heidelberg New York, pp 360–390Google Scholar
  12. Franz MO, Krapp HG (1998) Wide-field, motion-sensitive neurons and matched filters for optic flow fields. Biol Cybernet 83:185–197CrossRefGoogle Scholar
  13. Götz KG (1964) Optomotorische untersuchung des visuellen systems einiger augenmutanten der fruchtfliege drosophila. Kybernetik 2:77–92PubMedCrossRefGoogle Scholar
  14. Haag J, Egelhaaf M, Borst A (1992) Dendritic integration of motion information in visual interneurons of the blowfly. Neurosci Lett 140:173–176PubMedCrossRefGoogle Scholar
  15. Harris RA, O’Carroll DC, Laughlin SB (1999) Adaptation and the temporal filter of fly motion detectors. Vision Res 39:2603–2613PubMedCrossRefGoogle Scholar
  16. Harris RA, O’Carroll DC, Laughlin SB (2000) Contrast gain reduction in fly motion adaptation. Neuron 28:595–606PubMedCrossRefGoogle Scholar
  17. Harrison RR, Koch C (2001) A silicon model of the fly’s optomotor control system. Neural Comput 12: 2291–2304CrossRefGoogle Scholar
  18. Hassenstein B, Reichardt W (1956) Systemtheoretische analyse der zeit-, reihenfolgen-, und vorseichenauswertung bei der berwegungsperzeption des rüsselkäfers chlorophanus. Z Naturforsch 11b:513–524Google Scholar
  19. Hausen K (1982) Motion-sensitive interneurons in the optomotor system of the fly. II. The horizontal cells: receptive field organization and response characteristics. Biol Cybernet 46:67–79CrossRefGoogle Scholar
  20. Hausen K (1993) The decoding of retinal image flow in insects. In: Miles FA, Wallman J (eds) Visual motion and its role in the stabilisation of gaze. Elsevier, LondonGoogle Scholar
  21. Hausen K, Egelhaaf M (1989) Neural mechanisms of visual course control in insects. In: Stavenga DG, Hardie RC (eds) Facets of vision, Springer, Berlin Heidelberg New York, pp 391–424Google Scholar
  22. Higgins CM, Douglass JK, Strausfeld NJ (2004) The computational basis of an identified neuronal circuit for elementary motion detection in dipterous insects. Vis Neurosci 21:567–586PubMedCrossRefGoogle Scholar
  23. James AC (1992) Nonlinear operator network models of processing in the fly lamina. In: Nabet B (ed) Nonlinear vision. CRC, Boca Raton, FL, pp 39–74Google Scholar
  24. Kern R, Lutterklas M, Petereit C, Lindemann JP, Egelhaaf M (2001) Neuronal processing of behaviourally generated optic flow: experiments and model simulations. Netw Comput Neural Syst 12:351–369CrossRefGoogle Scholar
  25. Kirschfeld K (1972) The visual system of Musca: studies on optics, structure, and function. In: Wehner R (ed) Information processing in the visual system of arthropods. Springer, Berlin Heidelberg New York, pp 61–74Google Scholar
  26. Kirschfeld K (1991) An optomotor control system with automatic compensation for contrast and texture. Proc R Soc Lond B Biol Sci 246:261–268CrossRefGoogle Scholar
  27. Krapp HG, Hengstenberg B, Hengstenberg R (1998) Dendritic structure and receptive-field organization of optic flow processing interneurons in the fly. J Neurophysiol 79:1902–1917PubMedGoogle Scholar
  28. Laughlin SB, Weckström M (1993) Fast and slow photoreceptors—a comparative study of the functional diversity of coding and conductances in the diptera. J Comp Physiol A 172:593–609CrossRefGoogle Scholar
  29. Lipetz LE (1971) The relation of physiological and psychological aspects of sensory intensity. In: Loewenstein WR (ed) Handbook of sensory physiology. Springer, Berlin Heidelberg New York, pp 192–225Google Scholar
  30. Maddess T, Laughlin SB (1985) Adaptation of the motion-sensitive neuron H1 is generated locally and governed by contrast frequency. Proc R Soc Lond B Biol Sci 225:251–275CrossRefGoogle Scholar
  31. Naka KI, Rushton WAH (1966) S-potentials from luminosity units in retina of fish (Cyprinidae). J Physiol Lond 185:587–599PubMedGoogle Scholar
  32. Poggio T, Reichardt W, Hausen K (1981) A neural circuitry for relative movement discrimination by the visual system of the fly. Naturwissenschaften 443:446Google Scholar
  33. Potters M, Bialek W (1994) Statistical mechanics and visual signal processing. J Phys IV Colloq I 4:1755–1775Google Scholar
  34. Ruderman DL (1994) The statistics of natural images. Netw Comput Neural Syst 5:517–548CrossRefGoogle Scholar
  35. Snyder AW (1979) Physics of vision in compound eyes. In: Autrum H (ed) Comparative physiology and evolution of vision in invertebrates: invertebrate photoreceptors, vol VII/6A of handbook of sensory physiology. Springer, Berlin Heidelberg New York, pp 225–313Google Scholar
  36. Snyder AW, Stavenga DF, Laughlin SB (1977) Spatial information capacity of the eyes. J Comp Physiol 116:183–207CrossRefGoogle Scholar
  37. Srinivasan MV, Guy RG (1990) Spectral properties of movement perception in the dronefly Eristalis. J Comp Physiol A 166:287–295CrossRefGoogle Scholar
  38. Srinivasan MV, Laughlin SB, Dubs A (1982) Predictive coding: a fresh view of inhibition in the retina. Proc R Soc Lond B Biol Sci 216:427–459PubMedGoogle Scholar
  39. Straw A, Rainsford T, O’Carroll D (2005) Estimates of natural scene velocity in fly motion detectors are contrast independent. Curr Biol (submitted)Google Scholar
  40. Tolhurst DJ, Tadmor Y, Chao T (1992) Amplitude spectra of natural images. Ophthalmol Physiol Opt 12:229–232CrossRefGoogle Scholar
  41. van Hateren JH (1990) Directional tuning curves, elementary movement detectors, and the estimation of the direction of visual movement. Vision Res 30:603–614PubMedCrossRefGoogle Scholar
  42. van Hateren JH (1992) Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation. J Comp Physiol A 171:157–170CrossRefGoogle Scholar
  43. van Hateren JH (1997) Processing of natural time series of intensities by the visual system of the blowfly. Vision Res 37:3407–3416PubMedCrossRefGoogle Scholar
  44. van Hateren JH, Snippe HP (2001) Information theoretical evaluation on parametric models of gain control in blowfly photoreceptor cells. Vision Res 41:1851–1865PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • P. A. Shoemaker
    • 1
  • D. C. O’Carroll
    • 2
  • A. D. Straw
    • 3
  1. 1.Tanner Research Inc.PasadenaUSA
  2. 2.Discipline of PhysiologyUniversity of AdelaideAdelaideAustralia
  3. 3.California Institute of TechnologyPasadenaUSA

Personalised recommendations