Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity

In this paper, we propose a methodology to design learning systems based on a memristor crossbar structure. Learning is carried out with the help of a hardware-friendly spike-timing-dependent plasticity learning rule. Several simplifications and adaptations are made in order to apply the learning algorithm to memristor-based neural networks. The difficulties in conducting division and representing...