Gaussian synapses for probabilistic neural networks

Designing large-scale hardware implementation of Probabilistic Neural Network for energy efficient neuromorphic computing systems remains a challenge. Here, the authors propose an hardware design based on MoS2/BP heterostructures as reconfigurable Gaussian synapses enabling EEG patterns recognition.

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Autores principales:	Amritanand Sebastian, Andrew Pannone, Shiva Subbulakshmi Radhakrishnan, Saptarshi Das
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ad20e8a07db343ebacc0a4a6dcfc367e
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id	oai:doaj.org-article:ad20e8a07db343ebacc0a4a6dcfc367e
record_format	dspace
spelling	oai:doaj.org-article:ad20e8a07db343ebacc0a4a6dcfc367e2021-12-02T16:58:25ZGaussian synapses for probabilistic neural networks10.1038/s41467-019-12035-62041-1723https://doaj.org/article/ad20e8a07db343ebacc0a4a6dcfc367e2019-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12035-6https://doaj.org/toc/2041-1723Designing large-scale hardware implementation of Probabilistic Neural Network for energy efficient neuromorphic computing systems remains a challenge. Here, the authors propose an hardware design based on MoS2/BP heterostructures as reconfigurable Gaussian synapses enabling EEG patterns recognition.Amritanand SebastianAndrew PannoneShiva Subbulakshmi RadhakrishnanSaptarshi DasNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Amritanand Sebastian Andrew Pannone Shiva Subbulakshmi Radhakrishnan Saptarshi Das Gaussian synapses for probabilistic neural networks
description	Designing large-scale hardware implementation of Probabilistic Neural Network for energy efficient neuromorphic computing systems remains a challenge. Here, the authors propose an hardware design based on MoS2/BP heterostructures as reconfigurable Gaussian synapses enabling EEG patterns recognition.
format	article
author	Amritanand Sebastian Andrew Pannone Shiva Subbulakshmi Radhakrishnan Saptarshi Das
author_facet	Amritanand Sebastian Andrew Pannone Shiva Subbulakshmi Radhakrishnan Saptarshi Das
author_sort	Amritanand Sebastian
title	Gaussian synapses for probabilistic neural networks
title_short	Gaussian synapses for probabilistic neural networks
title_full	Gaussian synapses for probabilistic neural networks
title_fullStr	Gaussian synapses for probabilistic neural networks
title_full_unstemmed	Gaussian synapses for probabilistic neural networks
title_sort	gaussian synapses for probabilistic neural networks
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/ad20e8a07db343ebacc0a4a6dcfc367e
work_keys_str_mv	AT amritanandsebastian gaussiansynapsesforprobabilisticneuralnetworks AT andrewpannone gaussiansynapsesforprobabilisticneuralnetworks AT shivasubbulakshmiradhakrishnan gaussiansynapsesforprobabilisticneuralnetworks AT saptarshidas gaussiansynapsesforprobabilisticneuralnetworks
_version_	1718382349302890496

Gaussian synapses for probabilistic neural networks

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