Results 1 to 10 of about 10,889 (168)
Is Neuromorphic MNIST Neuromorphic? Analyzing the Discriminative Power of Neuromorphic Datasets in the Time Domain [PDF]
Frontiers in Neuroscience, 2021 A major characteristic of spiking neural networks (SNNs) over conventional artificial neural networks (ANNs) is their ability to spike, enabling them to use spike timing for coding and efficient computing.
Laxmi R. Iyer +3 more
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Neuromorphic spintronics [PDF]
Nature Electronics, 2020 Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices. In recent years, multiple groups have demonstrated
J. Grollier +5 more
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NPG Asia Materials, 2023 Various memristive devices have been proposed for use in neuromorphic computing systems as artificial synapses. Analog synaptic devices with linear conductance updates during training are efficiently essential to train neural networks.
Keonhee Kim +14 more
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Nature Communications, 2022 Conventional filamentary memristors are limited in dynamics by the high electric-field dependence of the conductive filament. Here, Jeong et al. presents a method which creates a cluster-type memristor, enabling large conductance range and long data ...
Jaehyun Kang +10 more
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Scientific Reports, 2021 Mid-infrared wavelengths are called the molecular fingerprint region because it contains the fundamental vibrational modes inherent to the substances of interest. Since the mid-infrared spectrum can provide non-destructive identification and quantitative
Hwa-Seub Lee +7 more
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Novel nano-plasmonic sensing platform based on vertical conductive bridge
Scientific Reports, 2021 A novel nano-plasmonic sensing platform based on vertical conductive bridge was suggested as an alternative geometry for taking full advantages of unique properties of conductive junction while substantially alleviating burdens in lithographic process ...
Hyo-Seung Park +5 more
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An Optimized Deep Spiking Neural Network Architecture Without Gradients
IEEE Access, 2022 We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spatio-temporal spike patterns.
Yeshwanth Bethi +4 more
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Neuromorphic metasurface [PDF]
Photonics Research, 2019 Metasurfaces have been used to realize optical functions such as focusing and beam steering. They use subwavelength nanostructures to control the local amplitude and phase of light. Here we show that such control could also enable a new function of artificial neural inference.
Zhicheng Wu +4 more
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Crystals, 2023 The compatibility of memristor materials with advanced complementary metal-oxide-semiconductor (CMOS) technology is a key factor for microelectronics element base manufacturing.
Dmitry Mizginov +5 more
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CoRR, 2020 Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biological organization of neural systems to develop novel technology for computing, sensing, and actuating. The unique properties of such systems call for new signal processing and control paradigms.
Luka Ribar, Rodolphe Sepulchre
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