Adaptive Orthogonal Characteristics of Bio-inspired Neural Networks

Abstract

Neural networks researches are developed for the machine learning and the recent deep learning. Adaptive mechanisms of neural networks are prominent in the visual pathway. In this paper, adaptive orthogonal properties are studied, First, this paper proposes a model of the bio-inspired asymmetric neural networks. The prominent features are the nonlinear characteristics as the squaring and rectification functions, which 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 analyzed from the orthogonality characteristics. Second, it is shown that the biological asymmetric network is effective for generating the orthogonality function using the network correlation computations. Finally, the asymmetric networks with nonlinear characteristics are able to generate independent subspaces, which will be useful for the creation of features spaces and efficient computations in the learning.