Results 11 to 20 of about 38,728 (269)
Embodied neuromorphic intelligence [PDF]
Nature Communications, 2022 AbstractThe design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for ...
Bartolozzi, Chiara +2 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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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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Neuromorphic quantum computing
Physical Review E, 2022 9 pages, 8 ...
Pehle, Christian, Wetterich, Christof
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Neuromorphic Analogue VLSI [PDF]
Annual Review of Neuroscience, 1995 Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology.
Douglas, Rodney +2 more
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Journal of Applied Physics, 2021 Neuromorphic photonic applies concepts extracted from neuroscience to develop photonic devices behaving like neural systems and achieve brain-like information processing capacity and efficiency. This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and paves the ...
Stabile, R. +5 more
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Event-Based Feature Extraction Using Adaptive Selection Thresholds
Sensors, 2020 Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed
Saeed Afshar +5 more
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IEEE Access, 2022 Neuromorphic hardware is a system with massive potential to enable efficient computing by mimicking the human brain. The novel system processes information using neuron spikes (Action Potentials) and the synaptic connections between neurons are trained ...
Suman Hu +11 more
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The Intel neuromorphic DNS challenge
Neuromorphic Computing and Engineering, 2023 A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
Jonathan Timcheck +7 more
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Neuromorphic artificial intelligence systems
Frontiers in Neuroscience, 2022 Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of currently available neuromorphic AI projects in which these
Dmitry Ivanov +6 more
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