ReviewFrontiers in neuroscience2015
Plasticity in memristive devices for spiking neural networks.
Review in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic Functionality.ACS applied materials & interfaces · 2026Article
- Memristor Synapse-A Device-Level Critical Review.Nanomaterials (Basel, Switzerland) · 2026Review
- Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems.Nano-micro letters · 2026Review
- Artificial Synapse Based on Black Phosphorus/SnSACS omega · 2025Article
- Organic Bioelectronics Development in Italy: A Review.Micromachines · 2023Review
- Brain-like critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix.Nanoscale advances · 2022Article
- Stochastic Resonance in Organic Electronic Devices.Polymers · 2022Review
- Applications and Techniques for Fast Machine Learning in Science.Frontiers in big data · 2022Review
- 4-Ethylguaiacol Modulates Neuroinflammation and Promotes Heme Oxygenase-1 Expression to Ameliorate Brain Injury in Ischemic Stroke.Frontiers in immunology · 2022Article
- Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity.Scientific reports · 2021Article
- Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs.Frontiers in neuroscience · 2021Article
- Perovskite neural trees.Nature communications · 2020Article
- Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications.Scientific reports · 2019Article
- On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits.Materials (Basel, Switzerland) · 2019Article
- Synaptic memory devices from CoO/Nb:SrTiORoyal Society open science · 2019Article
- Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays.Frontiers in neuroscience · 2019Article
- Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits.Nature communications · 2018Article
- Neuromorphic computing with multi-memristive synapses.Nature communications · 2018Article
- Signal and noise extraction from analog memory elements for neuromorphic computing.Nature communications · 2018Article
- Deep Learning With Spiking Neurons: Opportunities and Challenges.Frontiers in neuroscience · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.
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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.