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000905621 037__ $$aFZJ-2022-00853
000905621 041__ $$aEnglish
000905621 1001_ $$0P:(DE-HGF)0$$aYu, Jessica$$b0$$eCorresponding author
000905621 245__ $$aEvolving autonomous agents with simulated brains using L2L and Netlogo$$f2021-03-01 - 2021-09-30
000905621 260__ $$aJülich$$c2021
000905621 300__ $$a76 p
000905621 3367_ $$2DRIVER$$abachelorThesis
000905621 3367_ $$02$$2EndNote$$aThesis
000905621 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
000905621 3367_ $$0PUB:(DE-HGF)2$$2PUB:(DE-HGF)$$aBachelor Thesis$$bbachelor$$mbachelor$$s1643354407_27099
000905621 3367_ $$2BibTeX$$aMASTERSTHESIS
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000905621 502__ $$aBachelorarbeit, RWTH Aachen, 2021$$bBachelorarbeit$$cRWTH Aachen$$d2021$$o2021-10-14
000905621 520__ $$aArtificial neural networks (ANNs) are popular machine learning techniques used to model autonomous agents. Spiking neural networks (SNNs) provide the ability to reproduce spatio-temporal dynamics by transmitting information through action potentials or spikes. Given their more biologically realistic characteristic, they are particularly attractive for modelling biological systems, including the analysis and understanding of biological self organisation. As with many neural models, the difficulty in achieving the desired performance is finding the appropriate parameters settings. A commonly used autonomous approach is given by genetic algorithms (GAs), which provide an evolution-based search technique inspired by natural adaptation processes. The performance of these meta-heuristic search techniques depends on the settings of its hyperparameters, which present a challenging task on their own.In this work, a multi-agent simulation model embedded in NetLogo is investigated. It simulates an artificial ant navigating through a virtual maze with many obstacles in search of food. Through this process the ant is controlled by an SNN, whose parameter optimisation is examined and optimised in this thesis using GAs of two different tools (L2L and BehaviorSearch). Afterwards, a deeper investigation on the optimized SNNs is covered to understand and explain the observed behavior in the simulation.
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000905621 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000905621 536__ $$0G:(EU-Grant)800858$$aICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)$$c800858$$fH2020-SGA-INFRA-FETFLAG-HBP$$x2
000905621 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000905621 8564_ $$uhttps://juser.fz-juelich.de/record/905621/files/Bachelor%20Thesis.pdf$$yOpenAccess
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000905621 9141_ $$y2021
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000905621 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000905621 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
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