A neuromorphic network composed of silver nanowires coated with TiO2 is found to show certain parallels with neural networks in nature such as biological brains. Owing to the memristive properties emerging at nanowire-to-nanowire contacts, where the Ag/TiO2/Ag interface exists, the network can store information in the form of connectivity between nanowires in the network as electrically measured as an increase in conductance. The observed memory arises from an interplay between the topological constraints imposed by a complex network structure and the plasticity of its constituting memristive Ag/TiO2/Ag junctions. Regarding the long-term decay of the connectivity in the network, we further investigate the controllability of the established connectivity. Inspired by the regulated activity cycles of the human brain during sleep, a learning-sleep-recovery cycle was mimicked by applying voltage pulses, with controlling pulse heights and duty ratios, to the nanowire network. Interestingly, even when the conductance was lost during sleep, the network could quickly recover previous states of conductance in the recovery process after sleep. Comparison between results of experiments and theoretical simulations revealed that such a quick recovery of conductance can be realized by sparse voltage pulse application during sleep; in other words, sleep-dependent memory consolidation occurs and can be controlled. The present results provide clues to new learning designs in neuromorphic networks for achieving longer memory retention for future neuromorphic technology.
Keywords: artificial synapse; memory consolidation; memristor; nanowire network; neuromorphic network.