ArticleNature communications2020
Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Artificial neural manifolds.Nature communications · 2026Article
- Adaptive Redox Resistive Memory Programming for Efficient and Robust Class-Incremental Learning.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Microelectrode Arrays Technology for Brain-on-a-Chip Applications.Biosensors · 2026Review
- The evolution of speech communication devices for anarthria: a review.Journal of neurology · 2026Review
- Privacy-preserving data analysis using a memristor chip with colocated authentication and processing.Science advances · 2026Article
- An Analogue Memristor Based on Conjugated Porous Polymer Composite for Artificial Synapse.Exploration (Beijing, China) · 2026Article
- Vertical Self-Rectifying Memristive Arrays for Page-Wise Parallel Logic and Arithmetic Processing.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Pruning random resistive memory for optimizing analog AI.Nature communications · 2026Article
- Brain-Inspired In-Memory Data Pruning and Computing with TaOAdvanced materials (Deerfield Beach, Fla.) · 2025Article
- Heterogeneous Integration Technology Drives the Evolution of Co-Packaged Optics.Micromachines · 2025Review
- Neuromorphic chips for biomedical engineering.Mechanobiology in medicine · 2025Review
- Rapid detection of brain tumor cells using memristors for biomedical applications.Materials today. Bio · 2025Article
- In-memory ferroelectric differentiator.Nature communications · 2025Article
- Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Single-trial detection of auditory cues from the rat brain using memristors.Science advances · 2024Article
- Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision.Science advances · 2024Article
- Recent Progress in Artificial Neurons for Neuromodulation.Journal of functional biomaterials · 2024Review
- Memristor-based storage system with convolutional autoencoder-based image compression network.Nature communications · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is falling behind. One of the key bottlenecks is that they adopt conventional von Neumann architecture with digital computation that is fundamentally different from the working principle of human brain. In this work, we present a memristor-based neural signal analysis system, where the bio-plausible characteristics of memristors are utilized to analyze signals in the analog domain with high efficiency. As a proof-of-concept demonstration, memristor arrays are used to implement the filtering and identification of epilepsy-related neural signals, achieving a high accuracy of 93.46%. Remarkably, our memristor-based system shows nearly 400× improvements in the power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor systems. This work demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces.
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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.