ArticleNature communications2023
Online dynamical learning and sequence memory with neuromorphic nanowire networks.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Neuromorphic tissues: Soft biomolecular networks for brain-inspired temporal computing.Science advances · 2026Article
- Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing.Nature communications · 2026Article
- Real-Time TEM Observation of the Microstructural Evolution in Silver Nanowires under Heating and Electrical Biasing.ACS applied electronic materials · 2026Article
- Utilizing Cooperative Proton-Electron Mixed Conduction Induced via Chemical Dedoping of Self-Doped Poly(3,4-ethylenedioxythiophene) Nanofilms for In-Material Physical Reservoirs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Memristance and transmemristance in multiterminal memristive systems.Scientific reports · 2026Article
- Training-free AI for earth observation change detection using physics aware neuromorphic networks.Scientific reports · 2025Article
- Ultrahigh-precision analog computing using memory-switching geometric ratio of transistors.Science advances · 2025Article
- Self-organizing neuromorphic nanowire networks as stochastic dynamical systems.Nature communications · 2025Article
- Direct on-Chip Optical Communication between Nano Optoelectronic Devices.ACS photonics · 2025Article
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- Artificial Neuron Based on the Bloch-Point Domain Wall in Ferromagnetic Nanowires.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.
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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.