Control of criticality and computation in spiking neuromorphic networks with plasticity

Designing efficient artificial networks able to quickly converge to optimal performance for a given task remains a challenge. Here, the authors demonstrate a relation between criticality, task-performance and information theoretic fingerprint in a spiking neuromorphic network with synaptic plasticit...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Benjamin Cramer, David Stöckel, Markus Kreft, Michael Wibral, Johannes Schemmel, Karlheinz Meier, Viola Priesemann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/fb7c0a50753d437fb6161c1380bc2dcd
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fb7c0a50753d437fb6161c1380bc2dcd
record_format	dspace
spelling	oai:doaj.org-article:fb7c0a50753d437fb6161c1380bc2dcd2021-12-02T15:57:19ZControl of criticality and computation in spiking neuromorphic networks with plasticity10.1038/s41467-020-16548-32041-1723https://doaj.org/article/fb7c0a50753d437fb6161c1380bc2dcd2020-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16548-3https://doaj.org/toc/2041-1723Designing efficient artificial networks able to quickly converge to optimal performance for a given task remains a challenge. Here, the authors demonstrate a relation between criticality, task-performance and information theoretic fingerprint in a spiking neuromorphic network with synaptic plasticity.Benjamin CramerDavid StöckelMarkus KreftMichael WibralJohannes SchemmelKarlheinz MeierViola PriesemannNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Benjamin Cramer David Stöckel Markus Kreft Michael Wibral Johannes Schemmel Karlheinz Meier Viola Priesemann Control of criticality and computation in spiking neuromorphic networks with plasticity
description	Designing efficient artificial networks able to quickly converge to optimal performance for a given task remains a challenge. Here, the authors demonstrate a relation between criticality, task-performance and information theoretic fingerprint in a spiking neuromorphic network with synaptic plasticity.
format	article
author	Benjamin Cramer David Stöckel Markus Kreft Michael Wibral Johannes Schemmel Karlheinz Meier Viola Priesemann
author_facet	Benjamin Cramer David Stöckel Markus Kreft Michael Wibral Johannes Schemmel Karlheinz Meier Viola Priesemann
author_sort	Benjamin Cramer
title	Control of criticality and computation in spiking neuromorphic networks with plasticity
title_short	Control of criticality and computation in spiking neuromorphic networks with plasticity
title_full	Control of criticality and computation in spiking neuromorphic networks with plasticity
title_fullStr	Control of criticality and computation in spiking neuromorphic networks with plasticity
title_full_unstemmed	Control of criticality and computation in spiking neuromorphic networks with plasticity
title_sort	control of criticality and computation in spiking neuromorphic networks with plasticity
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/fb7c0a50753d437fb6161c1380bc2dcd
work_keys_str_mv	AT benjamincramer controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity AT davidstockel controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity AT markuskreft controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity AT michaelwibral controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity AT johannesschemmel controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity AT karlheinzmeier controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity AT violapriesemann controlofcriticalityandcomputationinspikingneuromorphicnetworkswithplasticity
_version_	1718385367934042112

Control of criticality and computation in spiking neuromorphic networks with plasticity

Ejemplares similares