Abstract
Neural networks are extensively developed for the deep learning and intelligent processing. To improve the performance of neural networks, the biological inspired neural networks are often studied for artificial neural networks. Models for motion processing in the biological systems have been used, which consist of the symmetric networks with quadrature functions of Gabor filters. This paper proposes a model of the bio-inspired asymmetric neural networks with nonlinear characteristics, which are the squaring and rectification functions. These functions are observed in the retinal and visual cortex networks. In this paper, the proposed asymmetric network with Gabor filters and the conventional energy model are compared from the orthogonality characteristics. To show the role of the orthogonality in the feature space, tracking characteristics to input stimulus are experimented. Then, the orthogonality basis functions create better tracking results. Thus, asymmetric structure of the network and its nonlinear characteristics are shown to be effective factors for generating orthogonality.
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References
Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. J. Optical Soc. Am. A(1985), 284–298
Pan, H., Jiang, H.: Learning convolutional neural networks using hybrid orthogonal projection and estimation. In: Proceedings of Machine Learning Research, ACML 2017, vol. 77, 1–16 (2017)
Huang, l., Liu, X., Lang, B., Yu, A.W., Wang, Y., Li, B.: Orthogonal weight normalization: solution to optimization over multiple dependent stiefel manifolds in deep neural networks. In: 32nd AAAI Conference on AI, AAAI-18, pp. 3271–3278 (2018)
Shi, W., Gong, Y., Cheng, D., Tao, X., Zheng, N.: Entropy orthogonality based deep discriminative feature learning for object recognition. Pattern Recogn. 81, 71–80 (2018)
Hashimoto, W.: Quadratic forms in natural images 14, 765–788 (2003)
Hyvarinen, A., Hoyer, P.: Emergence of phase-and shiftinvariant features by decomposition of natural images into independent feature subspaces. Neural Comput. 12, 1705–1720 (2000)
Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision. Res. 37(23), 3311–3325 (1997)
Simonceli, E.P., Heeger, D.J.: A model of neuronal responses in visual area MT. Vision. Res. 38, 743–761 (1996)
Sakai, H.M., Naka, K.-I.: Dissection of the neuron network in the catfish inner retina. I. Transmission to Ganglion Cells. J. Neurophysiol. 60(5), 1549–1567 (1988)
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)
Ishii, N., Deguchi, T., Kawaguchi, M., Sasaki, H.: Motion detection in asymmetric neural networks. In: Cheng, L., Liu, Q., Ronzhin, A. (eds.) ISNN 2016. LNCS, vol. 9719, pp. 409–417. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40663-3_47
Ishii, N., Deguchi, T., Kawaguchi, M., Sasaki, H., Matsuo, T.: Orthogonal properties of asymmetric neural networks with gabor filters. In: Pérez GarcÃa, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado RodrÃguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 589–601. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_50
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Ishii, N., Deguchi, T., Kawaguchi, M., Sasaki, H., Matsuo, T. (2022). Generation of Orthogonality for Feature Spaces in the Bio-inspired Neural Networks. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_2
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