Emulation of Synaptic Scaling Based on MoS₂ Neuristor for Self‐Adaptative Neuromorphic Computing

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.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

“…HSP is a neural mechanism with ability to modify the synaptic weight via paired activation of presynaptic neuron with postsynaptic neuron. [ 52 , 53 , 54 ] Hebbian synaptic learnings are crucial in mimicking process of biosynapses based on applied spike‐related computations, where spike time‐dependent plasticity (STDP) and spike rate‐dependent plasticity (SRDP) are two classic paradigms in mammalian brain for generation of the Hebbian synaptic plasticity, respectively. With respect to the brain cerebral behaviors, SRDP are entitled for transfer of data in biological neural networks which are directly connected to average action potential firing rate.…”

Research on the construction method of neural network model based on memristors

“…In biological neural networks, neurons, and synapses store and compute the input information locally, which solves the bottleneck problem between memory and computational units, and the system can process data in parallel. In addition, biological neural networks can adapt to complex environments, tolerate changes and noise in the system, and process complex information with very low power [6]- [7].…”