A digitalized RRAMbased Spiking Neuron Network system with 3bit weight and unsupervised online learning scheme Resistiveswitching Random Access Memory (RRAM) has emerged as a promising candidate for the artificial synaptic in neuromorphic computation circuits due to its similar electronic characteristics with the synaptic and features such as high integration density, nonvolatile retention and supporting matrixvector multiplication. In this paper, a digitalized RRAMbased fullyconnected Spiking Neuron Network (SNN) system with 3bit weight and unsupervised online learning scheme is proposed. It consists of 64 preneurons and 10 postneurons, all the neurons are realized by digital circuits for low area overhead, low power consumption and high accuracy. An unsupervised online learning scheme based on binary STDP protocol is applied to train the synaptic weights. Experiments show that the system can be used to recognize the learned ten handwritten digits efficiently.

A Volatile RRAM Synapse for Neuromorphic Computing Neuromorphic computing has emerged as a promising approach for autonomous systems able to learn, adapt, and interact in real time with the environment. To build neuromorphic hardware, the recent development of novel materialbased devices such as resistive switching memory (RRAM) has shown to be crucial since this class of devices offers the unique advantage to implement neuron and synaptic functions in silico by device physics, thus avoiding bulky circuits and very complex algorithms. In this work, we first explore volatile switching behaviour of RRAM devices, investigating their ability to capture shortterm plasticity (STP) and shortterm memory (STM) functionalities. Then, we characterise a volatile RRAM synapse, discussing its potential use in a spiking neural network for speech recognition applications.