A convolutional neural-network framework for modelling auditory sensory cells and synapses

Drakopoulos et al developed a machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Focusing on auditory neurons and synapses, they showed that the...

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Autores principales:	Fotios Drakopoulos, Deepak Baby, Sarah Verhulst
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c5801428ff8749b5922186c0d9f89358
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spelling	oai:doaj.org-article:c5801428ff8749b5922186c0d9f893582021-12-02T16:10:32ZA convolutional neural-network framework for modelling auditory sensory cells and synapses10.1038/s42003-021-02341-52399-3642https://doaj.org/article/c5801428ff8749b5922186c0d9f893582021-07-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02341-5https://doaj.org/toc/2399-3642Drakopoulos et al developed a machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Focusing on auditory neurons and synapses, they showed that their DNN-model architecture could be extended to a variety of existing analytical models and to other neuron and synapse types, thus potentially assisting the development of large-scale brain networks and DNN-based treatments.Fotios DrakopoulosDeepak BabySarah VerhulstNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Fotios Drakopoulos Deepak Baby Sarah Verhulst A convolutional neural-network framework for modelling auditory sensory cells and synapses
description	Drakopoulos et al developed a machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Focusing on auditory neurons and synapses, they showed that their DNN-model architecture could be extended to a variety of existing analytical models and to other neuron and synapse types, thus potentially assisting the development of large-scale brain networks and DNN-based treatments.
format	article
author	Fotios Drakopoulos Deepak Baby Sarah Verhulst
author_facet	Fotios Drakopoulos Deepak Baby Sarah Verhulst
author_sort	Fotios Drakopoulos
title	A convolutional neural-network framework for modelling auditory sensory cells and synapses
title_short	A convolutional neural-network framework for modelling auditory sensory cells and synapses
title_full	A convolutional neural-network framework for modelling auditory sensory cells and synapses
title_fullStr	A convolutional neural-network framework for modelling auditory sensory cells and synapses
title_full_unstemmed	A convolutional neural-network framework for modelling auditory sensory cells and synapses
title_sort	convolutional neural-network framework for modelling auditory sensory cells and synapses
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c5801428ff8749b5922186c0d9f89358
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A convolutional neural-network framework for modelling auditory sensory cells and synapses

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