ReviewNano-micro letters2026
Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.
Review in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has advanced rapidly in recent years and has been widely applied in healthcare, intelligent sensing, machine perception, and image recognition. Neuromorphic computing, inspired by the structure and operating principles of the human brain, has emerged as a promising paradigm for building efficient, low-power, and adaptive information processing systems. In this review, we summarize the development of neuromorphic memristors from the perspectives of biological inspiration, representative material systems, device architectures, performance metrics, and artificial intelligence-oriented applications. Different from previous reviews that mainly focus on memristor materials, switching mechanisms, or neuromorphic functions separately, this article further emphasizes the relationships between memristor characteristics and distinct AI-oriented tasks, including AI acceleration, neuromorphic computing, intelligent sensing, and human-machine interaction. Finally, the major challenges and future opportunities of neuromorphic memristors are discussed from the viewpoints of device optimization, system integration, scalability, and practical application.
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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.