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000901960 0247_ $$2Handle$$a2128/28830
000901960 0247_ $$2URN$$aurn:nbn:de:0001-2021110925
000901960 020__ $$a978-3-95806-577-2
000901960 037__ $$aFZJ-2021-03936
000901960 1001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b0$$eCorresponding author$$ufzj
000901960 245__ $$aStructural plasticity as a connectivity generation and optimization algorithm in neural networks$$f- 2021-03-11
000901960 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2021
000901960 300__ $$a167
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000901960 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v47
000901960 502__ $$aDissertation, RWTH Aachen University, 2021$$bDissertation$$cRWTH Aachen University$$d2021
000901960 520__ $$aOur brains are formed by networks of neurons and other cells which receive, filter, store and process information and produce actions. The morphology of the neurons changes through time as well as the connections between them. For years the brain has been studied as a snapshot in time, but today we know that the way it structurally changes is strongly involved in learning, healing, and adaptation. The ensemble of structural changes that neural networks present through time is called structural plasticity. In this work, I present structural plasticity from its neurobiological foundations and the implementation of a model to describe generation and optimization of connectivity in spiking neural networks. I have targeted two relevant and open questions in the computational neuroscience community: how can we model biologically inspired structural changes in simulations of spiking neural networks and how can we use this model and its implementation to optimize brain connectivity to answer specific scientific questions related to healing, development, and learning. I present several studies which explain the implementation of structural plasticity in a well established neural network simulator and its application on different types of neural networks. In this thesis I have also defined the requirements and use cases for the co-development of tools to visualize and interact with the structural plasticity algorithm. Moreover, I present two scientific applications of the structural plasticity model in the clinical neuroscience and computer science fields. In conclusion, my thesis provides the basis of a software framework and a methodology to address complex neuroscience questions related to plasticity and the links between structure and function in the brain, with potential applications not only in neuroscience but also for machine learning and optimization.
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000901960 9141_ $$y2021
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