ArticleNature communications2025
Electrochemical ohmic memristors for continual learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Stable Analog Weight Programming in Single-Crystalline van der Waals Ferroelectric Transistors for Reliable Computing-in-Memory.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application.Nano-micro letters · 2026Review
- HfOScience advances · 2026Article
- Intrinsic gradient oxygen-driven second-order memristors for continual reinforcement learning.Nature communications · 2026Article
- In Situ Quantization with Memory-Transistor Transfer Unit Based on Electrochemical Random-Access Memory for Edge Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Designing a heterostructure of graphite-like layer/TaORegenerative biomaterials · 2026Article
- An innovative diagnostic method for myopia using blood-based biomemristor.Materials today. Bio · 2025Article
- Advances of Emerging Memristors for In-Memory Computing Applications.Research (Washington, D.C.) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing versatile and reliable memristive devices is crucial for advancing future memory and computing architectures. The years of intensive research have still not reached and demonstrated their full horizon of capabilities, and new concepts are essential for successfully using the complete spectra of memristive functionalities for industrial applications. Here, we introduce two-terminal ohmic memristor, characterized by a different type of switching defined as filament conductivity change mechanism (FCM). The operation is based entirely on localized electrochemical redox reactions, resulting in essential advantages such as ultra-stable binary and analog switching, broad voltage stability window, high temperature stability, high switching ratio and good endurance. The multifunctional properties enabled by the FCM can be effectively used to overcome the catastrophic forgetting problem in conventional deep neural networks. Our findings represent an important milestone in resistive switching fundamentals and provide an effective approach for designing memristive system, expanding the horizon of functionalities and neuroscience applications.
Identifiers
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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.