Programmable Synaptic Metaplasticity and below Femtojoule Spiking Energy Realized in Graphene-Based Neuromorphic Memristor
Source abstract
Abstract
Memristors with rich interior dynamics of ion migration are promising for mimicking various biological synaptic functions in neuromorphic hardware systems. A graphene-based memristor shows an extremely low energy consumption of less than a femtojoule per spike, by taking advantage of weak surface van der Waals interaction of graphene. The device also shows an intriguing programmable metaplasticity property in which the synaptic plasticity depends on the history of the stimuli and yet allows rapid reconfiguration via an immediate stimulus. This graphene-based memristor could be a promising building block toward designing highly versatile and extremely energy efficient neuromorphic computing systems.
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
State Key Laboratory of Electronic Thin Films and Integrate Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
Department of Electrical and Computer Engineering, University of California, Riverside, California 92521, United States
Integrated Service Technology, Shanghai, China
Department of Electronics Engineering and Institute of Electronics, National Chiao Tung University, Hsinchu, 300, Taiwan
Funding
Chang Gung Memorial Hospital, Linkou · Ministry of Science and Technology, Taiwan
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