ArticleFrontiers in neuroscience2016
Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model.Scientific reports · 2024Article
- BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network.Nature communications · 2024Article
- Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation.Frontiers in neuroscience · 2024Article
- Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.Frontiers in neuroscience · 2024Article
- SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.Frontiers in neuroscience · 2023Article
- A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses.Micromachines · 2022Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- Spin-Torque Memristors Based on Perpendicular Magnetic Tunnel Junctions for Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Utilizing the Switching Stochasticity of HfOFrontiers in neuroscience · 2021Article
- Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks.Frontiers in neuroscience · 2021Article
- Adaptive Extreme Edge Computing for Wearable Devices.Frontiers in neuroscience · 2021Review
- Memory devices and applications for in-memory computing.Nature nanotechnology · 2020Review
- Evidence of Biorealistic Synaptic Behavior in Diffusive Li-based Two-terminal Resistive Switching Devices.Scientific reports · 2020Article
- Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised Training Scheme.Materials (Basel, Switzerland) · 2019Article
- On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits.Materials (Basel, Switzerland) · 2019Article
- Stimulated Ionic Telegraph Noise in Filamentary Memristive Devices.Scientific reports · 2019Article
- Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays.Frontiers in neuroscience · 2019Article
- Memristor-Based Edge Detection for Spike Encoded Pixels.Frontiers in neuroscience · 2019Article
- Neuromorphic computing with multi-memristive synapses.Nature communications · 2018Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO
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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.