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.
This is a preview of subscription content, log in via an institution to check access.
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 Edition, Wiley, NY (1961)
Sakuranaga, M., Naka, K.-I.: Signal transmission in the Catfish Retina. III. Transmission to 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)
Chubb, C., Sperling, G.: Drift-balanced random stimuli, A general basis for studying non-Fourier motion. J. Opt. Soc. 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)
Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. J. Opt. Soc. America A 284–298(1985)
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)
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 Physiologiocal 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(1966)
Fukushima, F.: visual motion analysis by a neural network. Neural Inf. Process. 11(4–6), 63–73 (2007)
Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233, 1416–1419 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ishii, N., Deguchi, T., Kawaguchi, M., Sasaki, H. (2017). Applications of Asymmetric Networks to Bio-Inspired Neural Networks for Motion Detection. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-46562-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46561-6
Online ISBN: 978-3-319-46562-3
eBook Packages: EngineeringEngineering (R0)