ArticleNature communications2026
Intrinsic gradient oxygen-driven second-order memristors for continual reinforcement learning.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Realization of the Bienenstock-Cooper-Munro rule in a single memristor.Nature communications · 2026Article
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Authors and funding
14 authors.
Funding
Abstract
The intrinsic gradient in organisms governs long-term information processing and continual learning in dynamic environments. Reinforcement learning (RL) aims to emulate such temporal regulation to enhance learning efficiency and adaptability. However, the absence of intrinsic gradient construction in existing memristive devices leads to stochastic and abrupt state changes, disrupting the generation of temporally correlated internal states critical for continual RL. In this work, a second-order memristor incorporating a stable intrinsic oxygen gradient was designed via a molecular-coordinated layer, which enables a prolonged dynamic barrier evolution (>10
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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.