@article {https://doi.org/10.1002/aelm.202001104,
	title = {Emulation of Synaptic Scaling Based on MoS2 Neuristor for Self-Adaptative Neuromorphic Computing},
	journal = {Advanced Electronic Materials},
	volume = {7},
	number = {4},
	year = {2021},
	pages = {2001104},
	abstract = {Abstract Recent studies indicate that synaptic scaling is a vital mechanism to solve instability risks brought by the positive feedback of synaptic weight change related with standalone Hebbian plasticity. There are two kinds of synaptic scaling in the neural network, including local scaling and global scaling, both important for stabilizing the neural function. In this paper, for the first time, local synaptic scaling is emulated based on the MoS2 neuristor. The first-principle calculation reveals that synaptic scaling achieved by the neuristor is associated with an internal residual Li+-related weak dynamical process. Experimental results show the potential of achieving global synaptic scaling by the same device. Moreover, inspired by the synaptic scaling in the human brain, a new method of weight mapping called weight scaling mapping (WSM) is proposed to improve the stability of an artificial neural network (ANN). The simulation results indicate that WSM can improve the accuracy and anti-noise ability of the network compared with the traditional mapping method. These findings provide new insight into bionic research and help advance the construction of stable neuromorphic systems.},
	keywords = {Hebbian plasticity, MoS2 neuristors, synaptic scaling, weight scaling mapping},
	doi = {https://doi.org/10.1002/aelm.202001104},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aelm.202001104},
	author = {Wu, Lindong and Bao, Lin and Wang, Zongwei and Yu, Zhizhen and Wang, Bowen and Qingyu Chen and Ling, Yaotian and Qin, Yabo and Tang, Kechao and Cai, Yimao and Ru HUANG}
}
