An Artificial Spiking Afferent Neuron System Achieved by 1M1S for Neuromorphic Computing

Abstract

Neuromorphic computing based on spiking neural networks (SNNs) has attracted significant research interest due to its low energy consumption and high similarity to biological neural systems. The artificial spiking afferent neuron (ASAN) system is the essential component of neuromorphic computing system to interact with the environment. This work presents an ASAN system with simple structure by employing a new architecture of one VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Mott memristor and one resistive sensor (1M1S). The Mott memristors show the bidirectional Mott transition, good endurance (> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.3\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> ), and high uniformity. By incorporating a flexible pressure sensor into the 1M1S architecture, a tactile ASAN system is realized with the pressure stimuli converted into rate-coded spikes. Using a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> array of the tactile ASAN systems, different pressure stimulus patterns can be well recognized. The strong adaptability of the proposed system will enable it to convert lots of environmental stimuli through the widely used resistive sensors into rate-coded spikes as the inputs of neuromorphic computing based on SNNs.