Brain-Inspired Memristive Neural Networks for Unsupervised Learning

Abstract

Memristive devices, such as resistive switching memory (RRAM) and phase change memory (PCM), show variable resistance which can mimic the synaptic plasticity in the human brain. This fascinating analogy has provided the inspiration for many recent research advances, involving memristive devices and their use as artificial electronics synapses in neuromorphic circuits with learning capability. In particular, RRAM-based artificial synapses are extremely promising in terms of area efficiency, low power consumption, and flexibility of design which pave the way for spiking neural networks that perform and behave like the human brain. This chapter will review the state of the art about the design and development of memristive neural networks for unsupervised learning. First, the optimization of RRAM devices for synaptic applications will be discussed, and a novel RRAM device with improved resistance window and controllability of resistance will be introduced. Then, a hybrid CMOS/memristive synaptic circuit will be shown to carry out learning tasks via the spike-timing dependent plasticity (STDP), which is one of the learning rules in biological synapses. Finally, the neural networks based on RRAM synapses will be reviewed, covering both feed-forward networks and recurrent networks. In both cases, the network displays unsupervised learning of input patterns, which can be stored, recognized, or even reconstructed by the network, thus highlighting the wealth of potential promising applications for memristive networks with synaptic plasticity.