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 results are applicable to the V1 and MT model of the neural networks in the cortex.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Reichard, W.: Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. Rosenblith edn. Wiley, New York (1961)
Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2(2), 284–298 (1985)
Heess, N., Bai, R.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)
Chubb, C., Sperling, G.: Drift-Balanced Random Stimuli, A General Basis for Studying Non-Fourier Motion. J. Optical Soc. of America A, 1986–2006 (1988)
Taub, E., Victor, J.D., Conte, M.: Nonlinear preprocessing in short-range motion. Vis. Res. 37, 1459–1477 (1997)
Simonceli, E.P., Heeger, D.J.: A model of neuronal responses in visual area MT. Vis. Res. 38, 743–761 (1996)
Heeger, D.J.: Normalization of cell responses in cat striate cortex. Vis. Neurosci. 9, 181–197 (1992)
Marmarelis, P.Z., Marmarelis, V.Z.: Analysis of Physiological Systems – The White Noise Approach. Plenum Press, New York (1978)
Marmarelis, V.Z.: Nonlinear Dynamic Modeling of Physiological Systems. Wiley-IEEE Press, New Jersey (2004)
Marmarelis, V.Z.: Modeling methodology for nonlinear physiological systems. Ann. Biomed. Eng. 25, 239–251 (1997)
Wiener, N.: Nonlinear Problems in Random Theory. The MIT Press, Cambridge (1966)
Sakuranaga, M., Naka, K.I.: Signal transmission in the catfish retina. III. Transmissioto type-C cell. J. Neurophysiol. 53(2), 411–428 (1985)
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)
Lee, Y.W., Schetzen, M.: Measurements of the Wiener kernels of a nonlinear by cross-correlation. Int. J. of Control 2, 237–254 (1965)
Ishii, N., Ozaki, M., Sasaki, H.: Correlation computations for movement detection in neural networks. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3214, pp. 124–130. Springer, Heidelberg (2004)
Ishii, N., Deguchi, T., Kawaguchi, M.: Neural computations by asymmetric networks with nonlinearities. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 37–45. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ishii, N., Deguchi, T., Kawaguchi, M., Sasaki, H. (2016). Motion Detection in Asymmetric Neural Networks. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-40663-3_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40662-6
Online ISBN: 978-3-319-40663-3
eBook Packages: Computer ScienceComputer Science (R0)