journal article·2024-07-23

Threshold Switching Memristor Based on 2D SnSe for Nociceptive and Leaky-Integrate and Fire Neuron Simulation

Yuwei Qin, Mengfan Wu, Niannian Yu, Ziqi Chen, Junhui Yuan, Jiafu Wang

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Abstract

Multifunctional neuromorphic devices to tackle complex tasks are highly desirable for the development of artificial neural networks. Threshold switching (TS) memory, which exhibits volatile abrupt resistance change under external electric fields, is capable of emulating multiple biological behaviors because of its rich temporal dynamics. Here, a TS device based on two-dimensional (2D) SnSe is demonstrated. Owing to the diffusive dynamics of Ag ions in SnSe, intrinsic stochasticity of the TS behavior is observed, which can be exploited to construct a compact stochastic Leaky-Integrate and Fire (LIF) model with improved performance in spiking neuron network (SNN). Moreover, an artificial nociceptor is constructed based on the 2D TS device, successfully emulating typical nociceptive features of “threshold”, “relaxation”, “no adaptation”, “hyperalgesia” and “allodynia”. The realization of bioinspired devices with combined sensory and information processing abilities paves the way for developing neuromorphic electronics for SNN and humanoid robots.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Photoreceptor and optogenetics research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Science, Wuhan University of Science and Technology, Wuhan 430070, China

School of Artificial Intelligence, Jianghan University, Wuhan 430056, China

Funding

Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China

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