ReviewiScience2021
Competing memristors for brain-inspired computing.
Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- Diffusive memristors in the edge of chaos.Nature communications · 2026Article
- An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing.Journal of the American Chemical Society · 2026Article
- Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation.Chemical reviews · 2025Review
- Confinement of ions within graphene oxide membranes enables neuromorphic artificial gustation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.GeroScience · 2025Review
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- Nanoscale Titanium Oxide Memristive Structures for Neuromorphic Applications: Atomic Force Anodization Techniques, Modeling, Chemical Composition, and Resistive Switching Properties.Nanomaterials (Basel, Switzerland) · 2025Article
- Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing.Nature nanotechnology · 2025Article
- Design of CMOS-memristor hybrid synapse and its application for noise-tolerant memristive spiking neural network.Frontiers in neuroscience · 2025Article
- Bioactive Ion-Confined Ultracapacitive Memristors with Neuromorphic Functions.Angewandte Chemie (International ed. in English) · 2024Article
- Insights of BDAPbIACS omega · 2024Article
- Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions.ACS nano · 2024Review
- Article
- Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications.Nano-micro letters · 2024Review
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- Enhanced electrical and magnetic properties of (Co, Yb) co-doped ZnO memristor for neuromorphic computing.RSC advances · 2023Article
- Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer.Science advances · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in implementing AI. To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed. A memristor can store numerous values by changing its resistance and emulate artificial synapses in brain-inspired computing. Here, we introduce six types of memristors classified according to their operation mechanisms: ionic migration, phase change, spin, ferroelectricity, intercalation, and ionic gating. We review how memristor-based neuromorphic computing can learn, infer, and even create, using various artificial neural networks. Finally, the challenges and perspectives in the competing memristor technology for neuromorphic computing systems are discussed.
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What OpenQuestion holds
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.