Advertisement

A Bio-inspired Model Reliably Predicts the Collision of Approaching Objects under Different Light Conditions

  • Ana Carolina Silva
  • Cristina Peixoto dos Santos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7426)

Abstract

In this paper, we present a model of the Lobula Giant Movement Detector, which is a part of a visual pathway responsible for triggering collision avoidance manouvres in the locust Locusta Migratoria. Also based on locust neural adaptation to transitions in light intensities, the model proposed here integrates a mechanism for light adaptation. The tests performed with the model demonstrate its ability to reproduce several characteristic properties of the LGMD response, including the firing rate profile for different visual stimuli. Additionally, results obtained for different light conditions show that the increase in the LGMD model efficiency is provided by the new mechanism of light adaptation. In here, the LGMD is presented as an ideal model to develop sensors for automatic collision detection.

Keywords

Bio-inspired model Lobula Giant Movement Detector neuron artificial neural networks collision avoidance spatiotemporal summation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zupanc, G.K.H.: Behavioral Neurobiology: An Integrative Approach. Oxford University Press (2010)Google Scholar
  2. 2.
    O’Shea, M., Williams, J.L.D.: The anatomy and output connection of a locust visual interneurone; the lobular giant movement detector (LGMD) neuron. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 257–266 (1974)Google Scholar
  3. 3.
    Chapman, R.F.: The insects: Structure and Function. Hodder and Stoughton, London (1980)Google Scholar
  4. 4.
    Warrant, E.J.: Seeing better at night: lift style, eye design and the optimum strategy of spatial and temporal summation. Vision Res. 39, 1611–1630 (1999)CrossRefGoogle Scholar
  5. 5.
    Gabbiani, F., Krapp, H., Laurent, G.: Computation of object approach by a wide-field motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999)Google Scholar
  6. 6.
    Gabbiani, F., Mo, C., Laurent, G.: Invariance of Angular Threshold Computation in a Wide-Field Looming-Sensitive Neuron. The Journal of Neuroscience 21(1), 314–329 (2001)Google Scholar
  7. 7.
    Gabbiani, F., Krapp, H.G., Koch, C., Laurent, G.: Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320–324 (2002)CrossRefGoogle Scholar
  8. 8.
    Gray, J.R., Lee, J.K., Robertson, R.M.: Activity of descending contralateral movement detector neurons and collision avoidance behaviour in response to head-on visual stimuli in locusts. Journal of Comparative Physiology A, 115–129 (2001)Google Scholar
  9. 9.
    Rind, F.C.: Non-directional, movement sensitive neurones of the locust optic lobe. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 477–494 (1987)Google Scholar
  10. 10.
    Guest, B.B., Gray, J.R.: Respones of a looming-sensitive neuron to compound and paired object approaches. Journal of Neurophysiology 95(3), 1428–1441 (2006)CrossRefGoogle Scholar
  11. 11.
    Gray, J.R., Blincow, E., Robertson, R.: A pair motion-sensitive neurons in the locust encode approaches of a looming object. Journal of Comparative Physiology A 196(12), 927–938 (2010)CrossRefGoogle Scholar
  12. 12.
    Rind, F.C., Bramwell, D.I.: Neural Network Based on the Input Organization of an Identified Neuron Signaling Impeding Collision. Journal of Neurophysiology 75(3), 967–985 (1996)Google Scholar
  13. 13.
    Blanchard, M., Rind, F.C., Verschure, P.F.M.J.: Collision avoidance using a model of the locust LGMD neuron. Robotics and Autonomous Systems 30(1), 17–37 (2000)CrossRefGoogle Scholar
  14. 14.
    Yue, S., Rind, F.C.: Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement. IEEE Transactions on Neural Networks 17(3), 705–716 (2006)CrossRefGoogle Scholar
  15. 15.
    Stafford, R., Santer, R.D., Rind, F.C.: A bio-inspired visual collision detection mechanism for cars: combining insect inspired neurons to create a robust system. BioSystems 87, 164–171 (2007)CrossRefGoogle Scholar
  16. 16.
    Meng, H., Yue, S., Hunter, A., Appiah, K., Hobden, M., Priestley, N., Hobden, P., Pettit, C.: A modified neural network model for the Lobula Giant Movement Detector with additional depth movement feature. In: Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, pp. 14–19 (2009)Google Scholar
  17. 17.
    Badia, S.B.I., Bernardet, U., Verschure, P.F.M.J.: Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector. PLoS Comput. Biol. 6(3), e1000701 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ana Carolina Silva
    • 1
  • Cristina Peixoto dos Santos
    • 1
  1. 1.Industrial Electronic DepartmentUniversity of MinhoPortugal

Personalised recommendations