Memristor‐Based Biologically Plausible Memory Based on Discrete and Continuous Attractor Networks for Neuromorphic Systems

To approach an advanced neuromorphic system, a significant unsettled problem is how to realize biologically plausible memory structures that are dramatically different from classical computers. Herein, a physical system based on memristors is simulated to realize associative memory based on discrete attractor networks, which is essentially content‐based storage, and the influence of device characteristics on network performance is systematically studied. An in situ unsupervised learning method is applied to make greater use of array structure and competitions between neurons, demonstrating significant performance improvement in memory capacity and noise tolerance compared with existing supervised approaches. By extending to continuous attractor neural networks (CANNs), working memory is realized based on memristors for the first time via simulation, and the write and read noises in memristor arrays are found to have different impacts on the ability of CANN in maintaining dynamic information. This work lays a foundation for the construction of future advanced neuromorphic computing systems. A physical system based on memristors is used to realize associative memory based on discrete attractor networks, enabling content‐based storage. By extending it to continuous attractor neural networks, working memory is realized based on memristors. The write and read noises in memristor arrays are found to have different impacts on the ability of the network in maintaining dynamic information.