Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

Author:

Duan Qingxi,Jing Zhaokun,Zou Xiaolong,Wang YanghaoORCID,Yang Ke,Zhang Teng,Wu Si,Huang RuORCID,Yang YuchaoORCID

Abstract

AbstractAs a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems. Here, we demonstrate an artificial neuron based on NbOx volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model. A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor based neurons and nonvolatile TaOx memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems.

Funder

National Natural Science Foundation of China

National Outstanding Youth Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry

Reference50 articles.

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