Abstract
To make clear the mechanism of the visual movement is important in the visual system. The problem is how to perceive vectors of the optic flow in the network. First, the biological asymmetric network with nonlinearities is analyzed for generating the vector from the point of the network computations. The results are applicable to the V1 and MT model of the neural networks in the cortex. The stimulus with a mixture distribution is applied to evaluate their network processing ability for the movement direction and its velocity, which generate the vector. Second, it is shown that the vector is emphasized in the MT than the V1. The characterized equation is derived in the network computations, which evaluates the vector properties of processing ability of the network. The movement velocity is derived, which is represented in Wiener kernels. The operations of vectors are shown in the divisive normalization network , which will create curl or divergence vectors in the higher neural network as MST area.
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
Preview
Unable to display preview. Download preview PDF.
References
Reichard, W.: Autocorrelation, A principle for the evaluation of sensory information by the central nervous system, Rosenblith edn. Wiley, NY (1961)
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. Vision Research 37, 1459–1477 (1997)
Simonceli, E.P., Heeger, D.J.: A Model of Neuronal Responses in Visual Area MT. Vision Research 38, 743–761 (1996)
Heeger, D.J.: Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 181–197 (1992)
Naka, K.-I., Sakai, H.M., Ishii, N.: Generation of transformation of second order nonlinearity in catfish retina. Annals of 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)
Sejnowski, T., Koch, C., Churchland, P.: Computational Neuroscience. Science, New Series 241(4871), 1299–1306 (1988)
Fukushima, K.: Visual Motion Analysis by a Neural Network. Neural Information Processing 11(4-6), 63–73 (2007)
Beck, J.M., Latham, P.E., Pouget, A.: Marginalization in Neural Circuits with Divisive Normalization. Journal of Neuroscience 31(43), 15310–15319 (2011)
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, Part II. 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, Part II. LNCS, vol. 4432, pp. 37–45. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ishii, N., Deguchi, T., Kawaguchi, M., Sasaki, H. (2013). Vector Generation and Operations in Neural Networks Computations. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-37213-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37212-4
Online ISBN: 978-3-642-37213-1
eBook Packages: Computer ScienceComputer Science (R0)