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Information transmission in complex neural networks

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dc.contributor Serrano Moral, Ma. Ángeles (María Ángeles)
dc.creator Ojeda Guerrero, Juan José
dc.date 2018-10-08T16:51:59Z
dc.date 2018-10-08T16:51:59Z
dc.date 2018-06
dc.date.accessioned 2024-12-16T10:26:49Z
dc.date.available 2024-12-16T10:26:49Z
dc.identifier http://hdl.handle.net/2445/125147
dc.identifier.uri http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/21550
dc.description Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2018, Tutora: Maria Ángeles Serrano Moral
dc.description Computational neural networks are inspired in the brain structure and designed to mimic its intelligence artificially. In particular, they can be used as a tool to explore information transport and processing among interconnected units forming layers. In this article we study how the structure of the network (number of layers) and the state of the connections (edge weights) affect to the information flow. A computational model, that allows us to simulate neurons' collective behaviour, has been used over different neural networks configurations, analysing the variation of some structure features. Results show a strong dependence between the number of connections and the response of the network. Also, we have found a relationship among the edge weights distribution and the propagation of the information from the input node to the output layer.
dc.format 5 p.
dc.format application/pdf
dc.language eng
dc.rights cc-by-nc-nd (c) Ojeda, 2018
dc.rights http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights info:eu-repo/semantics/openAccess
dc.source Treballs Finals de Grau (TFG) - Física
dc.subject Xarxes neuronals (Neurobiologia)
dc.subject Processament humà de la informació
dc.subject Treballs de fi de grau
dc.subject Neural networks (Neurobiology)
dc.subject Human information processing
dc.subject Bachelor's theses
dc.title Information transmission in complex neural networks
dc.type info:eu-repo/semantics/bachelorThesis


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