Spiking neural networks based on two-dimensional materials

The development of artificial neural networks using memristors is gaining a lot of interest among technological companies because it can reduce the computing time and energy consumption. There is still no memristor, made of any material, capable to provide the ideal figures-of-merit required for the implementation of artificial neural networks, meaning that more research is required. Here we present the use of multilayer hexagonal boron nitride based memristors to implement spiking neural networks for image classification. Our study indicates that the recognition accuracy of the network is high, and that can be resilient to device variability if the number of neurons employed is large enough. There are very few studies that present the use of a two-dimensional material for the implementation of synapses of different features; in our case, in addition to a study of the synaptic characteristics of our memristive devices, we deal with complete spiking neural network training and inference processes.Ministry of Science and Technology, China 2018YFE0100800National Natural Science Foundation of China (NSFC) 61874075Collaborative Innovation Centre of Suzhou Nano Science TechnologyPriority Academic Program Development of Jiangsu Higher Education Institutions111 Project from the State Administration of Foreign Experts Affairs of ChinaJunta de AndaluciaEuropean Commission A-TIC-117-UGR18 B-TIC-624-UGR20 IE2017-5414Spanish GovernmentERDF fund RTI2018-098983-B-I00King Abdullah University of Science & Technolog