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Chalcogenide optomemristors for multi-factor neuromorphic computation

Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. Howeve...

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Autores principales: Sarwat, Syed Ghazi, Moraitis, Timoleon, Wright, C. David, Bhaskaran, Harish
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042832/
https://www.ncbi.nlm.nih.gov/pubmed/35474061
http://dx.doi.org/10.1038/s41467-022-29870-9
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author Sarwat, Syed Ghazi
Moraitis, Timoleon
Wright, C. David
Bhaskaran, Harish
author_facet Sarwat, Syed Ghazi
Moraitis, Timoleon
Wright, C. David
Bhaskaran, Harish
author_sort Sarwat, Syed Ghazi
collection PubMed
description Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation.
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spelling pubmed-90428322022-04-28 Chalcogenide optomemristors for multi-factor neuromorphic computation Sarwat, Syed Ghazi Moraitis, Timoleon Wright, C. David Bhaskaran, Harish Nat Commun Article Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation. Nature Publishing Group UK 2022-04-26 /pmc/articles/PMC9042832/ /pubmed/35474061 http://dx.doi.org/10.1038/s41467-022-29870-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sarwat, Syed Ghazi
Moraitis, Timoleon
Wright, C. David
Bhaskaran, Harish
Chalcogenide optomemristors for multi-factor neuromorphic computation
title Chalcogenide optomemristors for multi-factor neuromorphic computation
title_full Chalcogenide optomemristors for multi-factor neuromorphic computation
title_fullStr Chalcogenide optomemristors for multi-factor neuromorphic computation
title_full_unstemmed Chalcogenide optomemristors for multi-factor neuromorphic computation
title_short Chalcogenide optomemristors for multi-factor neuromorphic computation
title_sort chalcogenide optomemristors for multi-factor neuromorphic computation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042832/
https://www.ncbi.nlm.nih.gov/pubmed/35474061
http://dx.doi.org/10.1038/s41467-022-29870-9
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