Associative Learning with Temporal Contiguity in a Memristive Circuit for Large-Scale Neuromorphic Networks

Memristors, acting as artificial synapses, have promised their prospects in neuromorphic systems that imitate the brain's computing paradigm. However, most studies focused on the understanding of the memristive mechanism and how to optimize the synaptic performance, and the implementations of higher-order cognitive functions are quite limited. Here the experimental demonstration of a representative network level learning function, i.e., associative learning and extinction, in a compact memristive neuromorphic circuit with only one memristor is reported. The association of the conditioned and unconditioned stimulus is established within a temporal window through the spike-timing-dependent plasticity rule, whereas the extinction of the formed memory is due to the synaptic depression. The temporal contiguity consists with biological behaviors and reflects nature's cause and effect rule. An efficient methodology of integrating memristors into large-scale neuromorphic systems for massively parallel computing, such as pattern recognition, is provided herein.