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Book Chapter: Neuronal realizations based on memristive devices
Title | Neuronal realizations based on memristive devices |
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Authors | |
Keywords | Memristor Spiking neural network Computing Neurons Neuronal |
Issue Date | 2020 |
Publisher | Woodhead Publishing. |
Citation | Neuronal realizations based on memristive devices. In Spiga, S, Sebastian, A, Querlioz, D, et al. (Eds.), Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks, p. 407-426. Duxford, UK: Woodhead Publishing, 2020 How to Cite? |
Abstract | Development of hardware-based spiking neural networks calls for novel building blocks, such as artificial neurons. This could lead to the development of systems with reduced power consumption, fault tolerance, and biomimetic artificial intelligence. Memristors, which are devices with a signal history dependent resistance, are realized via various physical mechanisms such as phase-change phenomena, redox reactions, Ovonic switching, Mott insulator-to-metal transition, and magnetoresistance. These devices possess unique dynamics which could potentially replicate biological neuronal behaviors such as leaky integrate-and-fire function. Spiking networks enabled by such artificial neurons have demonstrated intrinsic bio-realistic unsupervised learning protocols, which promises a compact and energy-efficient hardware implementation of neuromorphic computing. |
Persistent Identifier | http://hdl.handle.net/10722/291117 |
ISBN | |
Series/Report no. | Woodhead Publishing Series in Electronic and Optical Materials |
DC Field | Value | Language |
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dc.contributor.author | Wang, Z | - |
dc.contributor.author | Midya, R | - |
dc.contributor.author | Yang, JJ | - |
dc.date.accessioned | 2020-11-04T08:54:50Z | - |
dc.date.available | 2020-11-04T08:54:50Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Neuronal realizations based on memristive devices. In Spiga, S, Sebastian, A, Querlioz, D, et al. (Eds.), Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks, p. 407-426. Duxford, UK: Woodhead Publishing, 2020 | - |
dc.identifier.isbn | 9780081027820 | - |
dc.identifier.uri | http://hdl.handle.net/10722/291117 | - |
dc.description.abstract | Development of hardware-based spiking neural networks calls for novel building blocks, such as artificial neurons. This could lead to the development of systems with reduced power consumption, fault tolerance, and biomimetic artificial intelligence. Memristors, which are devices with a signal history dependent resistance, are realized via various physical mechanisms such as phase-change phenomena, redox reactions, Ovonic switching, Mott insulator-to-metal transition, and magnetoresistance. These devices possess unique dynamics which could potentially replicate biological neuronal behaviors such as leaky integrate-and-fire function. Spiking networks enabled by such artificial neurons have demonstrated intrinsic bio-realistic unsupervised learning protocols, which promises a compact and energy-efficient hardware implementation of neuromorphic computing. | - |
dc.language | eng | - |
dc.publisher | Woodhead Publishing. | - |
dc.relation.ispartof | Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks | - |
dc.relation.ispartofseries | Woodhead Publishing Series in Electronic and Optical Materials | - |
dc.subject | Memristor | - |
dc.subject | Spiking neural network | - |
dc.subject | Computing | - |
dc.subject | Neurons | - |
dc.subject | Neuronal | - |
dc.title | Neuronal realizations based on memristive devices | - |
dc.type | Book_Chapter | - |
dc.identifier.email | Wang, Z: zrwang@hku.hk | - |
dc.identifier.authority | Wang, Z=rp02714 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/B978-0-08-102782-0.00016-2 | - |
dc.identifier.hkuros | 700003892 | - |
dc.identifier.spage | 407 | - |
dc.identifier.epage | 426 | - |
dc.publisher.place | Duxford, UK | - |