Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks

Recent developments of deep learning, machine learning, and artificial intelligence have a great influence on the wide areas of technologies. Classification is a core technology in their processing. This paper aims to make clear the classification performance for the bio-inspired asymmetric and symmetric networks. First, the bio-inspired asymmetric network is shown to have superior performance for tracing features compared to the symmetric one. Second, the classification characteristics of the asymmetric and symmetric networks are derived based on the independence of their outputs. Further, it is shown that generation of extended bases in the bio-inspired layered networks improves classification performance. Finally, the higher-dimensional mapping code generated as the extended bases are applied to the modified XOR problem.