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Biologically inspired feature detection using cascaded correlations of off and on channels

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EN
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EN
Flying insects are valuable animal models for elucidating computational processes underlying visual motion detection. For example, optical flow analysis by wide-field motion processing neurons in the insect visual system has been investigated from both behavioral and physiological perspectives [1]. This has resulted in useful computational models with diverse applications [2,3]. In addition, some insects must also extract the movement of their prey or conspecifics from their environment. Such insects have the ability to detect and interact with small moving targets, even amidst a swarm of others [4,5]. We use electrophysiological techniques to record from small target motion detector (STMD) neurons in the insect brain that are likely to subserve these behaviors. Inspired by such recordings, we previously proposed an ‘elementary’ small target motion detector (ESTMD) model that accounts for the spatial and temporal tuning of such neurons and even their ability to discriminate targets against cluttered surrounds [6-8]. However, other properties such as direction selectivity [9] and response facilitation for objects moving on extended trajectories [10] are not accounted for by this model. We therefore propose here two model variants that cascade an ESTMD model with a traditional motion detection model algorithm, the Hassenstein Reichardt ‘elementary motion detector’ (EMD) [11]. We show that these elaborations maintain the principal attributes of ESTMDs (i.e. spatiotemporal tuning and background clutter rejection) while also capturing the direction selectivity observed in some STMD neurons. By encapsulating the properties of biological STMD neurons we aim to develop computational models that can simulate the remarkable capabilities of insects in target discrimination and pursuit for applications in robotics and artificial vision systems.
Rocznik
Strony
5--14
Opis fizyczny
Bibliogr. 39 poz., rys.
Twórcy
  • Adelaide Centre for Neuroscience Research, The University of Adelaide, South Australia, Australia, 5005
  • 1Adelaide Centre for Neuroscience Research, The University of Adelaide, South Australia, Australia, 5005
Bibliografia
  • [1] A. Borst, and T. Euler, “Seeing things in motion: Models, circuits, and mechanisms,” Neuron, vol. 71, pp. 974–994, 2011.
  • [2] N. Franceschini, J.M. Pichon, C. Blanes, and J.M. Brady, “From insect vision to robot vision [and discussion],” Philos. T. R. Soc. Lon. B vol. 337, pp. 283–294, 1992.
  • [3] M.V. Srinivasan, et al., “Robot navigation inspired by principles of insect vision,” Robot. Auton. Syst. vol. 26, pp. 203–216, 1999.
  • [4] P.S. Corbet, Dragonflies: Behavior and Ecology of Odonata, Ithaca: Cornell Univ Press, 1999
  • [5] S.D. Wiederman and D.C O’Carroll, “Selective attention in an insect visual neuron” Curr Biol, Vol 23, pp156-161, 2013
  • [6] S.D. Wiederman, P.A. Shoemaker, and D.C O’Carroll, “A model for the detection of moving targets in visual clutter inspired by insect physiology” PLoS ONE vol. 3 pp. e2784, 2008.
  • [7] S.D. Wiederman, S.D. Wiederman, R.S.A. Brinkworth and D.C. O’Carroll “Bio-inspired small target discrimination in high dynamic range natural scenes” 3rd International Conference on Bio-Inspired Computing: Theories and Applications. pp 109-116, 2008
  • [8] S.D. Wiederman, R.S.A. Brinkworth and D.C. O’Carroll, “Performance of a bio-inspired model for the robust detection of moving targets in high dynamic range natural scenes,” J. Comput. Theor. Nanos. vol. 7, pp. 911-920, 2010.
  • [9] D. O’Carroll, “Feature-detecting neurons in dragonflies,” Nature vol. 362, pp. 541–543,1993.
  • [10] K. Nordstrm, D.M. Bolzon and D.C. O’Carroll. “Spatial facilitation by a high-performance dragonfly target-detecting neuron,” Biol. Lett. Vol. 7, pp. 588-592, 2011.
  • [11] B. Hassenstein and W. Reichardt, “Analyse der zeit-, reihenfolgen- und vorzeichenauswertung bei der bewegungsperzeption des rsselkfers Chlorophanus,” Z. Naturf., vol. 11b, pp. 513–524, 1956.
  • [12] H.G. Krapp, and R. Hengstenberg, “Estimation of self-motion by optic flow processing in single visual interneurons,” Nature, vol. 384, pp. 463-466, 1996.
  • [13] B. R. H. Geurten, K. Nordstrm, J. D. H. Sprayberry, D.M. Bolzon, and D.C. O’Carroll, “Neural mechanisms underlying target detection in a dragonfly centrifugal neuron,” J. Exp. Biol., vol. 210, pp. 3277–3284, 2007.
  • [14] K. Nordstrm, P.D. Barnett, and D.C. O Carroll, “Insect detection of small targets moving in visual clutter,” PLoS Biol. vol. 4, pp. 378-386, 2006.
  • [15] K. Nordstrm, and D.C. O’Carroll, “Feature detection and the hypercomplex property in insects,” Trends Neurosci. 32:383–391, 2009.
  • [16] P.D. Barnett, K. Nordstrm, and D.C. O’Carroll, “Retinotopic organization of small-field-targetdetecting neurons in the insect visual system,” Curr. Biol. vol. 17, pp. 569–578, 2007.
  • [17] R. Dror, D.C. O’Carroll, S.B. Laughlin, “The role of natural image statistics in biological motion estimation”. Lect Notes Comput Sc, vol. 1811, pp 492–501, 2000
  • [18] S.D. Wiederman, P.A. Shoemaker & D.C. O’Carroll, “Biologically inspired small target detection mechanisms” IEEE Proc of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (ISSNIP) pp. 269- 273, 2007.
  • [19] D. Osorio, “Mechanisms of early visual processingin the medulla of the locust optic lobe – How self-inhibition, spatial-pooling, and signal rectification contribute to the properties of transient cells”. Visual Neurosci. vol. 7, pp. 345-3, 1991.
  • [20] N. Jansonius, and J. Hateren, “Fast temporal adaptation of on-off units in the first optic chiasm of the blowfly,” J. Comp. Physiol. A vol. 168, pp. 631–637, 1991.
  • [21] D.M. Bolzon, K. Nordstrom, D.C. O’Carroll “Local and large-range inhibition in feature detection” J Neurosci vol. 29 pp 14143–14150 2009
  • [22] S.D. Wiederman SD and D.C. O’Carroll, “Discrimination of features in natural scenes by a dragonfly neuron,” J. Neurosci. vol. 31, pp. 7141–7144, 2011.
  • [23] J. Zanker, “Modelling human motion perception. II. Beyond Fourier motion stimuli,” Naturwissenschaften vol. 81, pp. 200–209, 1994.
  • [24] J.R. Dunbier, S.D. Wiederman, P.A. Shoemaker, and D.C. O’Carroll, “Modelling the temporal response properties of an insect small target motion detector”, Proc. 7th Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, pp. 125-130, 2011.
  • [25] J.R. Dunbier, S.D. Wiederman, P.A. Shoemaker and D.C. O’Carroll, “Facilitation of dragonfly target-detecting neurons by slow moving features on continuous paths,” Front. Neural Circuits. Vol. 6, pp. 79, 2012.
  • [26] S.D. Wiederman, D.C. O’Carroll, “Biomimetic Target Detection: modeling 2nd order correlation of OFF and ON channels”. Proc. of the IEEE, Symposium Series on Computational Intelligence for Multimedia, Signal and Vision Processing, Singapore (in press).
  • [27] K. Nordstrm, and D.C. O’Carroll, “Small object detection neurons in female hoverflies,” P. Roy. Soc. B-Biol. Sci. vol. 273, pp.1211-1216, 2006.
  • [28] S.D. Wiederman, R.S.A. Brinkworth and D.C. O’Carroll, “Bio-inspired target detection in natural scenes: optimal thresholds and ego-motion,” Proc. of the SPIE, Biosensing, vol. 7035, pp. 70350Z, 2008.
  • [29] H. Eichner, M. Joesch, B. Schnell, D.F. Reiff, and A. Borst “Internal structure of the fly elementary motion detector,” Neuron vol. 70, pp. 1155–1164, 2011.
  • [30] R.A. Harris, D.C. O’Carroll and S.B. Laughlin, “Contrast gain reduction in fly motion adaptation” Neuron, vol 28 pp 595. 2000
  • [31] J.C. Theobald, B.J. Duistermars, D.L. Ringach and M.A. Frye, “Flies see second-order ?motion,” Curr. Biol. vol. 18, pp. R464–R465, 2008.
  • [32] E. L. Mah, R. S. Brinkworth, and D. C. O’Carroll, ”An elaborated electronic prototype of a biological photoreceptor,” Biol Cybern vol. 98, pp. 357-369, 2008.
  • [33] M. Juusola, R. O. Uusitalo, and M. Weckstrom, ”Transfer of graded potentials at the photoreceptor interneuron synapse,” J Gen Physiol vol. 105, pp. 117-148, 1995.
  • [34] A. C. James, ”Nonlinear operator network models of processing in the fly lamina,” in Nonlinear Vision, N. B, Ed. Boca Raton, FL: CRC, 1992, pp. 39-74.
  • [35] M. V. Srinivasan and R. G. Guy, ”Spectral properties of movement perception in the dronefly Eristalis,” J Comp Physiol A vol. 166, pp. 287-295, 1990.
  • [36] D. G. Stavenga, ”Angular and spectral sensitivity of fly photoreceptors. I. Integrated facet lens and rhabdomere optics,” J Comp Physiol A vol. 189, pp. 1-17, 2003.
  • [37] A. D. Straw, E. J. Warrant, and D. C. O’Carroll, ”A ‘bright zone’ in male hoverfly (Eristalis tenax) eyes and associated faster motion detection and increased contrast sensitivity,” J Exp Biol vol. 209, pp. 4339-4354, 2006.
  • [38] J. H. van Hateren and H. P. Snippe, ”Information theoretical evaluation of parametric models of gain control in blowfly photoreceptor cells,” Vision Res vol. 41, pp. 1851-1865, 2001.
  • [39] R.S.A. Brinkworth and D. C. O’Carroll, ” Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology” PLoS Comput Biol vol 5, (??), e1000555. doi:10.1371/journal.pcbi.1000555, 2009
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9e365a18-8057-419d-9277-18d1e2764a2c
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