Rotational Pattern Recognition by Spiking Correlated Neural Network Based on Dual‐Gated MoS2 Neuristor

Abstract

Beyond the great success in machine learning (ML), the engineering community has been actively exploring neuromorphic computing systems based on spiking neural networks (SNNs). In the preliminary SNN, the neuronal information is simply encoded by the firing rate, which limits the volume of information in a certain spike pattern. Studies of the biological nervous system have discovered neuronal cooperativity, where flow of neural information is collaboratively encoded and carried by the spatiotemporal stamp and the correlated neuronal activity. In this study, by encoding the control pattern on the dual gates, a MoS2‐based neuristor is exploited to program spatiotemporal information and neuronal correlations via the device's internal dynamics as well as the network topology, and a spiking correlated neural network (SCNN) is demonstrated. This correlated neural network not only succeeds in identifying the patterns with translational and rotational symmetry but also correctly reveals the rotated angles. Compared with conventional SNNs, the dimension of information flow in SCNNs can be greatly enlarged by taking advantage of the synergy between the rate coding and the neuronal correlations. This proof‐of‐concept work provides the potential to achieve high‐volume information processing with the simplified circuitry of neuromorphic computing systems.