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Carbon Nanotube Synaptic Transistor Network for Pattern Recognition
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor
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The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse‐Neuron System
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A Carbon Nanotube Synapse with Dynamic Logic and Learning
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Highly uniform carbon nanotube nanomesh network transistors
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Hysteresis Caused by Water Molecules in Carbon Nanotube Field-Effect Transistors
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A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications
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Carbon Nanotube Complementary Wrap-Gate Transistors
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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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Short-Channel Transistors Constructed with Solution-Processed Carbon Nanotubes
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Analog Neuromorphic Module Based on Carbon Nanotube Synapses
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Tuning Resistive Switching Characteristics of Tantalum Oxide Memristors through Si Doping
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Training and operation of an integrated neuromorphic network based on metal-oxide memristors
inferredcites →https://doi.org/10.1038/ncomms3072openalex-snapshot
Pattern classification by memristive crossbar circuits using ex situ and in situ training
inferredcites →https://doi.org/10.1038/nrn1949openalex-snapshot
Variability, compensation and homeostasis in neuron and network function
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Advantages of top-gate, high-k dielectric carbon nanotube field-effect transistors
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Artificial neural networks: a tutorial
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Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/ted.2011.2147791openalex-snapshot
An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation
inferredcites →https://doi.org/10.1109/tnano.2013.2250995openalex-snapshot
Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices
inferredcites →https://doi.org/10.1109/tnn.2004.832725openalex-snapshot
Temporal Coding in a Silicon Network of Integrate-and-Fire Neurons
inferredcites →https://doi.org/10.1109/tnn.2005.860850openalex-snapshot
A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity
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Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
inferredcites →https://doi.org/10.1371/journal.pcbi.1003037openalex-snapshot
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
inferredcites →https://doi.org/10.1523/jneurosci.18-24-10464.1998openalex-snapshot
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
inferredcites →https://doi.org/10.3389/fnins.2011.00026openalex-snapshot
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
inferredcites →https://doi.org/10.3389/fnins.2014.00205openalex-snapshot
Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Structure, modeling, fabrication, and applications of carbon nanotube field-effect transistors: a comprehensive review
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Evidence of Transport Degradation in 22 nm FD-SOI Charge Trapping Transistors for Neural Network Applications
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Multi-walled carbon nanotube/cellulose nanofiber synaptic devices with concentration-tunable time constants for physical reservoir computing
inferred← citeshttps://doi.org/10.48550/arxiv.2110.05910openalex-snapshot
Bridging the Band Gap: What Device Physicists Need to Know About Machine Learning
inferred← citeshttps://doi.org/10.5772/intechopen.110097openalex-snapshot
Neuromorphic Computing between Reality and Future Needs
inferred← citeshttps://doi.org/10.70675/38a49993zd685z458czb25cz953366beac6dopenalex-snapshot
Mémoires résistives et technologies 3D monolithiques pour processeurs neuromorphiques impulsionnels et reconfigurables
inferred← citeshttps://doi.org/10.7567/jjap.57.03ea06openalex-snapshot
Recent progress on fabrication of memristor and transistor-based neuromorphic devices for high signal processing speed with low power consumption
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