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Frontiers in Neuromorphic Engineering [PDF]
Frontiers in Neuroscience, 2011 ISSN:1662 ...
Giacomo Indiveri, Indiveri Giacomo
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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 architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing ...
Shuhui Ren, Yunfang Jia, Xiaobing Yan
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Spintronics for Neuromorphic Engineering
2021Today’s machine learning and artificial neural networks rely heavily on conventional electronic circuits. Progress in machine learning models and algorithms will eventually be limited by issues such as high power dissipation and scaling challenges posed by CMOS, and it is necessary to resolve these through a bottom-up approach.
Gerard Joseph Lim +2 more
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Editorial: Emerging talents in neuromorphic engineering
Frontiers in NeuroscienceThis Research Topic provides a platform to highlight the outstanding contributions of emerging talents in the field of neuromorphic engineering. Through this dedicated series, we aim to showcase the promising work of student researchers within Neuromorphic Engineering.
Horacio Rostro-Gonzalez +2 more
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2021
The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace ...
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The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace ...
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Dielectric Engineered Two-Dimensional Neuromorphic Transistors
Nano Letters, 2021Two-dimensional (2D) materials, which exhibit planar-wafer technique compatibility and pure electrically triggered communication, have established themselves as potential candidates in neuromorphic architecture integration. However, the current 2D artificial synapses are mainly realized at a single-device level, where the development of 2D scalable ...
Du Xiang +4 more
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A new computing rule for neuromorphic engineering
2015 15th Non-Volatile Memory Technology Symposium (NVMTS), 2015Neuromorphic engineering has helped to build a brain-inspired intelligent paradigm based on VLSI, and to promise a new application space on smart devices. Throughout most of the state-of-the-art neuromorphic systems, including analog, digital, the mixed one, as well as some other memristor-based systems, the dominated computing rule in neuron is simply
Lei Deng 0003 +4 more
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Neuromorphic engineering: Artificial brains for artificial intelligence
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
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SpiNNaker: Distributed Computer Engineering for Neuromorphics
2011SpiNNaker is a biologically-inspired massively-parallel computer architecture optimized specifically for modeling large-scale systems of spiking neurons in biological real time. The biological inspiration is manifest in the lightweight inter-processor communications architecture, which enables a “spike” generated by a neuron modeled
David R. Lester, Steve B. Furber
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Rebooting Neuromorphic Design - A Complexity Engineering Approach
2020 International Conference on Rebooting Computing (ICRC), 2020As the compute demands for machine learning and artificial intelligence applications continue to grow, neuromorphic hardware has been touted as a potential solution. New emerging devices like memristors, spintronics, atomic switches, etc have shown tremendous potential to replace CMOS-based circuits but have been hindered by multiple challenges with ...
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