ArticleNature communications2016
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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
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.
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Who cites it
58 citing papers in PubMed.
- Homeostatic dendritic neuron based on co-integrated volatile and non-volatile memristors for neuromorphic processing.Nature communications · 2026Article
- Memristor Synapse-A Device-Level Critical Review.Nanomaterials (Basel, Switzerland) · 2026Review
- Probabilistic Neural Networks: An Overview.Methods in molecular biology (Clifton, N.J.) · 2026Review
- High-Entropy Oxide Memristors for Neuromorphic Computing: From Material Engineering to Functional Integration.Nano-micro letters · 2025Review
- Photonic Bayesian Neural Networks: Leveraging Programmable Noise for Robust and Uncertainty-Aware Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- IMPACT: In-Memory ComPuting Architecture based on Y-FlAsh Technology for Coalesced Tsetlin machine inference.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Transparent Zinc Oxide Memristor Structures: Magnetron Sputtering of Thin Films, Resistive Switching Investigation, and Crossbar Array Fabrication.Nanomaterials (Basel, Switzerland) · 2024Article
- Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics.Nature communications · 2024Article
- BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network.Nature communications · 2024Article
- DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays.Nature communications · 2024Article
- Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems.Nature communications · 2024Article
- In-situ electro-responsive through-space coupling enabling foldamers as volatile memory elements.Nature communications · 2023Article
- Short-Term Memory Characteristics of IGZO-Based Three-Terminal Devices.Materials (Basel, Switzerland) · 2023Article
- A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Palimpsest memories stored in memristive synapses.Science advances · 2022Article
- Flexible Neural Network Realized by the Probabilistic SiOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses.Micromachines · 2022Article
- An Artificial Nerve Capable of UV-Perception, NIR-Vis Switchable Plasticity Modulation, and Motion State Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Memristive LIF Spiking Neuron Model and Its Application in Morse Code.Frontiers in neuroscience · 2022Article
- Reconfigurable Stochastic neurons based on tin oxide/MoSNature communications · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.
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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.