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Ojeda Guerrero Juanjo.pdf (1.01 MB)

Document type

Bachelor thesis

Publication date

2018-06

Publication license

cc-by-nc-nd (c) Ojeda, 2018
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/125147

Information transmission in complex neural networks

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Abstract

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.

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Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2018, Tutora: Maria Ángeles Serrano Moral

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Xarxes neuronals (Neurobiologia), Processament humà de la informació, Treballs de fi de grau

Subject (English)

Neural networks (Neurobiology), Human information processing, Bachelor's theses

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Treballs Finals de Grau (TFG) - Física

Citation

OJEDA GUERRERO, Juan José. Information transmission in complex neural networks. [consulted: 14 of August of 2026]. Available at: https://hdl.handle.net/2445/125147

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