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Neuromorphic Engineering Needs Closed-Loop Benchmarks [PDF]
Frontiers in Neuroscience, 2022 Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and ...
Moritz B. Milde +11 more
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Neuromorphic chips for biomedical engineering
Mechanobiology in MedicineThe modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer ...
Kaiyang Wang +5 more
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Editorial: Neuromorphic engineering for robotics [PDF]
Frontiers in Neurorobotics, 2023 Neuromorphic engineering aims to apply insights from neurobiology to develop next-generation artificial intelligence for computation, sensing, and the control of robotic systems.
Zhenshan Bing +2 more
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Research topic: Neuromorphic Engineering Systems and ApplicationsA snapshot of neuromorphic systems engineering [PDF]
Frontiers in Neuroscience, 2014 ISSN:1662 ...
André eVan Schaik +2 more
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Neuromorphic Engineering: Artificial Brains for Artificial Intelligence [PDF]
Annals of the New York Academy of SciencesAbstractNeuromorphic engineering is a research discipline that tries to bridge the gaps between neuroscience and engineering, cognition and algorithms, and natural and artificial intelligence. Neuromorphic engineering promises revolutionary breakthroughs that could rapidly advance our understanding of the brain and pave the way toward more human‐like ...
Johannes Leugering
exaly +4 more sources
Advanced Intelligent Systems, 2020 In the tide of artificial intelligence evolution, the demand for data computing has exploded, and the von Neumann architecture computer with separate memory and computing units require cumbersome data interaction, which leads to serious degradation in ...
Shuiyuan Wang +4 more
doaj +2 more sources
2D Materials Powering Neuromorphic Intelligence [PDF]
Nano-Micro LettersHighlights Two-dimensional materials enable energy-efficient neuromorphic computing through gate tuneable bandgaps, fast switching kinetics, and compatibility with spiking neural networks for adaptive learning.
Jamal Kazmi +9 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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A triple-level cell charge trap flash memory device with CVD-grown MoS2
Results in Physics, 2022 This study investigates the triple-level cell (TLC) memory retention of a MoS2-channel based charge trap flash (CTF) device. A top-gated CTF device with a high-κ gate dielectric is found to have a high coupling ratio, which enhances the tunneling ...
Minkyung Kim +9 more
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