Abstract
Nonlinearity is an important factor in the biological visual neural networks. Among prominent features of the visual networks, movement detections are carried out in the visual cortex. The visual cortex for the movement detection, consist of two layered networks, called the primary visual cortex (V1),followed by the middle temporal area (MT), in which nonlinear functions will play important roles in the visual systems. These networks will be decomposed to asymmetric sub-networks with nonlinearities. In this paper, the fundamental characteristics in asymmetric neural networks with nonlinearities, are discussed for the detection of the changing stimulus or the movement detection in these neural networks. By the optimization of the asymmetric networks, movement detection equations are derived. Then, it was clarified that the even-odd nonlinearity combined asymmetric networks, has the ability in the stimulus change detection and the direction of movement or stimulus, while symmetric networks need the time memory to have the same ability. These facts are applied to two layered networks, V1 and MT.
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References
Hassenstein, B., Reichard, W.: Systemtheoretische analyse der zeit-, reihenfolgen- and vorzeichenauswertung bei der bewegungsperzeption des russelkafers. Chlorophanus. Z. Naturf. 11b, 513–524 (1956)
Barlow, H.B., Levick, R.W.: The mechanism of directional selectivity in the rabbit’s retina. J. Physiol. 173, 377–407 (1965)
Victor, J.D., Shapley, K.M.: The nonlinear pathway of Y ganglion cells in the cat retina. J. Gen. Physiol. 74, 671–689 (1979)
Ishii, N., Naka, K.-I.: Movement and Memory Function in Biological Neural Networks. Int. J. of Artificial Intelligence Tools 4(4), 489–500 (1995)
Ishii, N., Sugiura, S., Nakamura, M., Yamauchi, K.: Sensory Perception, Learning and Integration in Neural Networks. In: Proc. IEEE Int. Conf. on Information Intelligence & Systems, pp. 72–79 (1999)
Ishii, N., Naka, K.-I.: Function of Biological Asymmetrical Neural Networks. In: Cabestany, J., Mira, J., Moreno-Díaz, R. (eds.) IWANN 1997. LNCS, vol. 1240, pp. 1115–1125. Springer, Heidelberg (1997)
Korenberg, M.J., Sakai, H.M., Naka, K.-I.: Dissection of theneuronnetwork in the catfish inner retina. J. Neurophysiol. 61, 1110–1120 (1989)
Marmarelis, P.Z., Marmarelis, V.Z.: Analysis of Physiological System: The White Noise Approach. Plenum Press, New York (1978)
Naka, K.-I., Sakai, H.M., Ishii, N.: Generation of transformation of second order nonlinearity in catfish retina. Annals of Biomedical Engineering 16, 53–64 (1988)
Shapley, R.: Visual cortex: pushing the envelope. Nature: neuroscience 1, 95–96 (1998)
Reichardt, W.: Autocorrelation, a principle for the evaluation of sensory information by the central nervous system (Rosenblith Edition). Wiley, Chichester (1961)
Sakuranaga, M., Naka, K.-I.: Signal transmission in the catfish retina. III. Transmission to type-C cell. J. Neurophysiol. 58, 411–428 (1987)
Taub, E., Victor, J.D., Conte, M.M.: Nonlinear preprocessing in short-range motion. Vision Research 37, 1459–1477 (1997)
Heeger, D.J.: Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. J. Neurophysiol. 70, 1885–1898 (1993)
Heeger, D.J., Simoncelli, E.P., Movshon, J.A.: Computational models of cortical visual processing. Proc. Natl. Acad. Sci, USA 93, 623–627 (1996)
Simoncelli, E.P., Heeger, D.J.: A Model of Neuronal Responses in Visual Area MT. Vision Research 38, 743–761 (1998)
Lu, Z.L., Sperling, G.: Three-systems theory of human visual motion perception: review and update. J. Opt. Soc. Am. A 18, 2331–2370 (2001)
Ishii, N., Deguchi, T., Sasaki, H.: Parallel Processing for Movement Detection in Neural Networks with Nonlinear Functions. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 626–633. Springer, Heidelberg (2004)
Lee, Y.W., Schetzen, M.: Measurement of the Wiener kernels of a nonlinear system by cross-correlation. Int. J. Control 2, 237–254 (1965)
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Ishii, N., Deguchi, T., Kawaguchi, M. (2007). Neural Computations by Asymmetric Networks with Nonlinearities. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_5
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DOI: https://doi.org/10.1007/978-3-540-71629-7_5
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