Generation of Bases for Classification in the Bio-inspired Layered Networks

Machine learning, deep learning and neural networks are extensively developed in many fields. As the function of cortical neural model, a sparse coding has been studied which is based on the bases functions of input stimulus. In this paper, it is shown that the bio-inspired networks are useful for the explanation of network functions. First, the asymmetric network with nonlinear functions is created based on the bio-inspired retinal network. They have orthogonal properties useful for features classification and processing. Second, it is shown that the asymmetric network is superior to the conventional symmetric network in the classification performance. Further, the asymmetric network is extended to the layered networks, which are also generated on the bio-inspired model of brain cortex. In the extended asymmetric layered networks, the higher dimensional orthogonal bases are created. To improve the classification performance, the bases replacements are performed in the layered networks. It is shown the bases replacements in the layered networks improve classification performance in both asymmetric and symmetric networks.