A Feed-Forward Neural Network for Increasing the Hopfield-Network Storage Capacity

Abstract

In the hippocampal dentate gyrus (DG), pattern separation mainly depends on the concepts of 'expansion recoding', meaning random mixing of different DG input channels. However, recent advances in neurophysiology have challenged the theory of pattern separation based on these concepts. In this study, we propose a novel feed-forward neural network, inspired by the structure of the DG and neural oscillatory analysis, to increase the Hopfield-network storage capacity. Unlike the previously published feed-forward neural networks, our bio-inspired neural network is designed to take advantage of both biological structure and functions of the DG. To better understand the computational principles of pattern separation in the DG, we have established a mouse model of environmental enrichment. We obtained a possible computational model of the DG, associated with better pattern separation ability, by using neural oscillatory analysis. Furthermore, we have developed a new algorithm based on Hebbian learning and coupling direction of neural oscillation to train the proposed neural network. The simulation results show that our proposed network significantly expands the storage capacity of Hopfield network, and more effective pattern separation is achieved. The storage capacity rises from 0.13 for the standard Hopfield network to 0.32 using our model when the overlap in patterns is 10%.