Spiking neuromorphic networks with metal-oxide memristors

This is a brief review of our recent work on memristor-based spiking neuromorphic networks. We first describe the recent experimental demonstration of several most biology-plausible spike-time-dependent plasticity (STDP) windows in integrated metal-oxide memristors and, for the first time, the observed self-adaptive STDP, which may be crucial for spiking neural network applications. We then discuss recent theoretical work in which an analytical, data-verified STDP model was used to simulate operation of a spiking classifier of spatial-temporal patterns, and the capacity-to-fidelity tradeoff and noise immunity o f spiking spatial-temporal associative memories with local and global recording was evaluated.