ArticleFrontiers in neuroscience2015
Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs.
Article 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 9 papers.
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Who cites it
9 citing papers in PubMed, 58 citations in OpenAlex.
- Heterosynaptic Memtransistors Based on Switching Operation Mechanism Using Designed Organic/Inorganic Heterostructures for Neuromorphic Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review on Resistive Switching Devices Based on Multiferroic BiFeONanomaterials (Basel, Switzerland) · 2023Review
- Run-off election-based decision method for the training and inference process in an artificial neural network.Scientific reports · 2021Article
- Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs.Frontiers in neuroscience · 2021Article
- Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations.Materials (Basel, Switzerland) · 2019Review
- Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype.Frontiers in neuroscience · 2018Article
- Coupling Resistive Switching Devices with Neurons: State of the Art and Perspectives.Frontiers in neuroscience · 2017Article
- Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.Nature communications · 2016Article
- Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.Frontiers in neuroscience · 2016Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one post-synaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25 ms to 125 μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.
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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.