NbO₂ Memristive Neurons for Burst‐Based Perceptron

Abstract

Neuromorphic computing using spike‐based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. Herein, the dynamic operating conditions of NbO2‐based memristive neurons and their transformation boundaries between the spiking and the bursting are comprehensively investigated. Furthermore, the underlying mechanism of bursting is analyzed, and the controllability of the number of spikes during each burst period is demonstrated. Finally, pattern classification and information transmitting in a perceptron neural network by using the number of spikes per bursting period to encode information is proposed. The results show a promising approach for the practical implementation of neuristor in spiking neural networks.

Standards for the Characterization of Endurance in Resistive Switching Devices

“…Depending on the number of resistance states and their stability, RS devices may be used for different applications. For example, RS devices exhibiting one nonvolatile state and one volatile state can be used as selectors to minimize sneak path currents in crossbar array circuits and are being considered for the hardware implementation of electronic neurons in deep neural networks (DNNs) and spiking neural networks (SNNs). , RS devices exhibiting two nonvolatile states have been employed to construct radiofrequency switches, , logic gates, − stochastic computing systems, , and nonvolatile memories (NVM). − RS devices exhibiting multiple nonvolatile and stable states are being considered for the hardware implementation of electronic synapses in DNNs, as they allow implementing computing algorithms ( e . g ., backpropagation) by updating and maintaining multiple conductance states in each training iteration (often referred to as epoch). , …”

Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

“…Unlike the extensively reported memristive synapses, the development of memristor-based artificial neurons is limited. Artificial neurons based on VCM, ECM, phase change, and Mott transition have been investigated, [50,[79][80][81][82] whereas artificial neurons based on other types of memristors, such as ferroelectric transition and magnetic transition-based memristors, are rarely reported. Various synaptic functions can be mimicked by a single memristor via the tuning of synaptic weight represented by its conductance.…”

Evolutionary Learning of Binary Neural Network Using a TaOx Memristor via Stochastic Stateful Logic

“…Some recent works have tried to realize some neuron functions by adopting threshold switch-type memristors to lighten the circuitry burden. [23][24][25][26][27][28][29] Nevertheless, completely replacing the CMOS-based circuits to execute the complicated neuron functions seems infeasible.…”