ArticleNature communications2018
Neuromorphic computing with multi-memristive synapses.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers.
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Who cites it
124 citing papers in PubMed.
- Magnetoelectric Multiferroics for the Next-Generation Ultralow-Power Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Recent Progress of Photodetectors and Optoelectronic Synapses Based on Metal Oxide Thin-Film Transistors.Materials (Basel, Switzerland) · 2026Review
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Battery-Inspired Electrochemical Synapses for Neuromorphic Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Ultrahigh-Performance Mix-Structure GaSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- Charge-Encoded Sidechains Enable Deterministic Ion Ingress and Memory Retention in Organic Electrochemical Synaptic Transistors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- CMOS-Compatible AlScN Memristor on Silicon Exhibiting Short-Term Memory for Reservoir Computing.Biomimetics (Basel, Switzerland) · 2026Article
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- A ferroelectric-ionic-trapping transistor for low power and secure neuromorphic computing.Nature communications · 2026Article
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep neural network inference on an integrated, reconfigurable photonic tensor processor.Nature communications · 2026Article
- Multimodal ion-gated transistor based on 2D superionic conductor for in-memory computing in deep learning.Nature communications · 2026Article
- The Electromechanical Connectome: Integrating Voltage, Mechanical Nano-Forces, and Subcellular Fluid Phase Dynamics in Human Neural Computation.International journal of molecular sciences · 2026Review
- NeoHebbian synapses to accelerate online training of neuromorphic hardware.Scientific reports · 2026Article
- Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic Functionality.ACS applied materials & interfaces · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems.Nano-micro letters · 2026Review
- Spikes as perturbations of resonant neural circuits: an RLC framework with testable predictions.Frontiers in computational neuroscience · 2026Article
- Linear and Symmetric Artificial Synapses Driven by Hydrogen Bonding for Accurate and Reliable Neuromorphic Computing.Advanced materials (Deerfield Beach, Fla.) · 2025Article
64 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.
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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.