Implementation of Neuronal Intrinsic Plasticity by Oscillatory Device in Spiking Neural Network
Lindong Wu, Zongwei Wang, Lin Bao, Zhizhen Yu, Qingyu Chen, Yaotian Ling, Yabo Qin, Shengyu Bao, Zhuoya Chen, Guandong Bai, Yimao Cai, John Robertson, Ru Huang
Abstract
Neurons are basic elements of the human brain to transmit and process various kinds of information. Besides the synaptic plasticity, the neuronal intrinsic plasticity (NIP) plays a vital role in improving and stabilizing the neural circuit for learning and adaptation to environment. In this article, we emulate the NIP behavior by combining hybrid VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$/$ </tex-math></inline-formula> TaO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> device with a tunable load resistor (TLR). By adjusting the TLR and stimulus intensity, the oscillatory threshold of artificial neurons can be adaptively modulated. Moreover, inspired by the nervous system, we implement the artificial NIP as an intercept-adjustable rectified linear unit (IA-ReLU) in the conversion-based spiking neural network (SNN). The results indicate that the NIP can mitigate the stuck-at-fault (SAF) effect on a memristor array and improve the recognition accuracy by up to 4.4% compared with the traditional ReLU function. These results demonstrate that the intrinsic plasticity of neurons can play a vital role in spiking neuromorphic computing systems.