ArticleFrontiers in neuroscience2013
STDP and STDP variations with memristors for spiking neuromorphic learning systems.
Article in Frontiers in neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.Research square · 2026Article
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.bioRxiv : the preprint server for biology · 2026Article
- Printed Organic Memristive Device on Rigid and Flexible Supports for Neuromorphic Applications.Biomimetics (Basel, Switzerland) · 2026Article
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Emergence of Polymer-Networked Nanoparticle Structures as Primitive Neuromorphic Computing States.The journal of physical chemistry. A · 2025Article
- Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing.Chemical reviews · 2025Review
- A Neuransistor with Excitatory and Inhibitory Neuronal Behaviors for Liquid State Machine.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Residual Stresses and Micro-voids Propel Metal Diffusion for Filament-Based Memristors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Brain-inspired learning rules for spiking neural network-based control: a tutorial.Biomedical engineering letters · 2025Review
- Neuromorphic Computing Primitives Using Polymer-Networked Nanoparticles.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Article
- Article
- Recent advancements in implantable neural links based on organic synaptic transistors.Exploration (Beijing, China) · 2024Review
- Nonvolatile Memristive Materials and Physical Modeling for In-Memory and In-Sensor Computing.Small science · 2024Article
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- The Memristive Properties and Spike Timing-Dependent Plasticity in Electrodeposited Copper Tungstates and Molybdates.Materials (Basel, Switzerland) · 2023Article
- Organic Bioelectronics Development in Italy: A Review.Micromachines · 2023Review
- Spike-timing-dependent plasticity rewards synchrony rather than causality.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original "moving wall" or to the "filament creation and annihilation" models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.
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