A Reconfigurable Bidirectional Associative Memory Network with Memristor Bridge

Abstract Bidirectional associative memories (BAMs) have been extensively applied in auto and heteroassociative learning. However, the research on the hardware implementation of BAM neural networks is relatively few. Memristor, a nanoscale synaptic-like element, provides a new perspective for the circuit implementation of neural networks. In this work, we improve a threshold memristor bridge circuit (T-MBC) and design a novel memristive bidirectional associative memory (MBAM) network circuit on this basis. T-MBC is capable to achieve positive, negative, and zero synaptic weights without any switches, inverters, transistors, and current-voltage conversion processes. The validity of T-MBC is illustrated by PSPICE. MBAM has a simpler structure and gains excellent effects in flexibility, and reconfigurability. Besides, it is shown that MBAM can be trained to recall both disturbed binary images and incomplete/noisy grey-scale images robustly. Meanwhile, a more complex application of emotion classification based on MBAM is also accomplished. Experimental results verify the effectiveness of the MBAM neural network and demonstrate that the MBAM neural network has high recognition accuracy and strong noise immunity.