ArticleNational science review2022
Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices.
Article in National science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed.
- Bioinspired learning and memory in ionogels through fast response and slow relaxation dynamics of ions.Nature communications · 2025Article
- Neuromorphic Computing Using Synaptic Plasticity of Supercapacitors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- MXene-Induced Flexible, Water-Retention, Semi-Interpenetrating Network Hydrogel for Ultra-Stable Strain Sensors with Real-Time Gesture Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- On Approximating theBiomedicines · 2023Article
- A Deep Learning-Enabled Skin-Inspired Pressure Sensor for Complicated Recognition Tasks with Ultralong Life.Research (Washington, D.C.) · 2023Article
- Dual sensing signal decoupling based on tellurium anisotropy for VR interaction and neuro-reflex system application.Nature communications · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-accuracy neuromorphic devices with adaptive weight adjustment are crucial for high-performance computing. However, limited studies have been conducted on achieving selective and linear synaptic weight updates without changing electrical pulses. Herein, we propose high-accuracy and self-adaptive artificial synapses based on tunable and flexible MXene energy storage devices. These synapses can be adjusted adaptively depending on the stored weight value to mitigate time and energy loss resulting from recalculation. The resistance can be used to effectively regulate the accumulation and dissipation of ions in single devices, without changing the external pulse stimulation or preprogramming, to ensure selective and linear synaptic weight updates. The feasibility of the proposed neural network based on the synapses of flexible energy devices was investigated through training and machine learning. The results indicated that the device achieved a recognition accuracy of ∼95% for various neural network calculation tasks such as numeric classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.