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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 material-based 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 short-term plasticity (STP) and short-term memory (STM) functionalities. Then, we characterise a volatile RRAM synapse, discussing its potential use in a spiking neural network for speech recognition applications.
Covi et al. (Fri,) studied this question.