Advertisement

Applications of Asymmetric Networks to Bio-Inspired Neural Networks for Motion Detection

  • Naohiro IshiiEmail author
  • Toshinori Deguchi
  • Masashi Kawaguchi
  • Hiroshi Sasaki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 513)

Abstract

To make clear the mechanism of the visual movement is important in the visual system. The prominent feature is the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Conventional model for motion processing in cortex, is the use of symmetric quadrature functions with Gabor filters. This paper proposes a new motion sensing processing model in the asymmetric networks. To make clear the behavior of the asymmetric nonlinear network, white noise analysis and Wiener kernels are applied. It is shown that the biological asymmetric network with nonlinearities is effective and general for generating the directional movement from the network computations. The qualitative analysis is performed between the asymmetrical network and the conventional quadrature model. The analyses of the asymmetric neural networks are applied to the V1 and MT neural networks model of in the cortex.

Keywords

Amacrine Cell Gabor Filter Impulse Response Function Order Nonlinearity Gabor Function 
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.

References

  1. 1.
    Reichard, W.: Autocorrelation, A principle for the evaluation of sensory information by the central nervous system. Rosenblith Edition, Wiley, NY (1961)Google Scholar
  2. 2.
    Sakuranaga, M., Naka, K.-I.: Signal transmission in the Catfish Retina. III. Transmission to type-C cell. J. Neurophysiol. 53(2), 411–428 (1985)Google Scholar
  3. 3.
    Naka, K.-I., Sakai, H.M., Ishii, N.: Generation of transformation of second order nonlinearity in catfish retina. Ann. Biomed. Eng. 16, 53–64 (1988)CrossRefGoogle Scholar
  4. 4.
    Chubb, C., Sperling, G.: Drift-balanced random stimuli, A general basis for studying non-Fourier motion. J. Opt. Soc. America A 1986–2006 (1988)Google Scholar
  5. 5.
    Taub, E., Victor, J.D., Conte, M.: Nonlinear preprocessing in short-range motion. Vis. Res. 37, 1459–1477 (1997)CrossRefGoogle Scholar
  6. 6.
    Simonceli, E.P., Heeger, D.J.: A model of neuronal responses in visual area MT. Vis. Res. 38, 743–761 (1996)CrossRefGoogle Scholar
  7. 7.
    Heeger, D.J.: Normalization of cell responses in cat striate cortex. Vis. Neurosci. 9, 181–197 (1992)CrossRefGoogle Scholar
  8. 8.
    Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. J. Opt. Soc. America A 284–298(1985)Google Scholar
  9. 9.
    Heess, N., Bair, W.: Direction opponency, not quadrature, is key to the 1/4 cycle preference for apparent motion in the motion energy model. J. Neurosci. 30(34), 11300–11304 (2010)CrossRefGoogle Scholar
  10. 10.
    Marmarelis, P.Z., Marmarelis, V.Z.: Analysis of Physiological Systems—The White Noise Approach. Plenum Press, New York (1978)zbMATHGoogle Scholar
  11. 11.
    Marmarelis, V.Z.: Nonlinear Dynamic Modeling of Physiologiocal Systems. Wiley-IEEE Press, New Jersey (2004)Google Scholar
  12. 12.
    Marmarelis, V.Z.: Modeling methodology for nonlinear physiological systems. Ann. Biomed. Eng. 25, 239–251 (1997)CrossRefGoogle Scholar
  13. 13.
    Wiener N.: Nonlinear Problems in Random Theory. The MIT press(1966)Google Scholar
  14. 14.
    Fukushima, F.: visual motion analysis by a neural network. Neural Inf. Process. 11(4–6), 63–73 (2007)Google Scholar
  15. 15.
    Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233, 1416–1419 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Naohiro Ishii
    • 1
    Email author
  • Toshinori Deguchi
    • 2
  • Masashi Kawaguchi
    • 3
  • Hiroshi Sasaki
    • 4
  1. 1.Department of Information ScienceAichi Institute of TechnologyToyotaJapan
  2. 2.Department of Electrical and Computer EngineeringNational Institute of Technology, Gifu CollegeGifuJapan
  3. 3.Department of Electrical and Electronics EngineeringNational Institute of Technology, Suzuka CollegeMieJapan
  4. 4.Department of Sports and Health SciencesFukui University of TechnologyFukuiJapan

Personalised recommendations