Modular neural networks with radial neural columnar architecture

Abstract

Abstract A new radial columnar architecture for the modular assembly neural network is proposed together with a modification of this architecture which differs in less number of learning connections in the network versus the former full-connected modular assembly neural networks. Validation of the latter architecture has been done in experiments on recognition of handwritten digits of the MNIST database. The experiments allow us to conclude that efficiency of the modular neural network with reduced number of learning connections is only slightly less than that of the full-connected modular neural network. Also, the experiments have demonstrated that its recognition capability is higher than that of the LiRA classifier. The main result of the work is that the full-connected network can be successfully replaced by its reduced version with retaining almost the same performance and with acquisition of a much higher speed of image processing.