Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/96363
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dc.contributor.advisorSoriano i Fradera, Jordi-
dc.contributor.authorTrias Creus, Albert-
dc.date.accessioned2016-03-10T16:50:15Z-
dc.date.available2016-03-10T16:50:15Z-
dc.date.issued2016-01-
dc.identifier.urihttp://hdl.handle.net/2445/96363-
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2016, Tutor: Jordi Soriano Fraderaca
dc.description.abstractMuch time and effort is being put to shed some light into the mysteries of the brain, and yet so much of it still remains unknown. This is why understanding the human brain and mind is considered one of the grand challenges of the XXI Century [1]. By implementing the recently developed information measure called Transfer Entropy, the strength and directionality of connections among neurons can be quantified. By the means of this mathematical tool, applied to trains of neuronal activity data measured in cultured neuronal networks, the structure of a neuronal network of 1,412 neurons has been analyzed and quantifiedca
dc.format.extent7 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Trias, 2016-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/-
dc.sourceTreballs Finals de Grau (TFG) - Física-
dc.subject.classificationEntropiacat
dc.subject.classificationXarxes neuronals (Neurobiologia)cat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherEntropyeng
dc.subject.otherNeural networks (Neurobiology)eng
dc.subject.otherBachelor's theseseng
dc.titleTransfer entropy: how to further understand connections among neuronseng
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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