000943468 001__ 943468
000943468 005__ 20230203120221.0
000943468 037__ $$aFZJ-2023-01037
000943468 041__ $$aEnglish
000943468 1001_ $$0P:(DE-HGF)0$$aKempter, Miriam$$b0$$eCorresponding author
000943468 245__ $$aImplementation of a Spiking Neural Network Model with Biologically Plausible Connectivity Characteristics for Neuromorphic Benchmarking$$f - 2022-11-08
000943468 260__ $$c2022
000943468 300__ $$a65 p.
000943468 3367_ $$2DRIVER$$abachelorThesis
000943468 3367_ $$02$$2EndNote$$aThesis
000943468 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
000943468 3367_ $$0PUB:(DE-HGF)2$$2PUB:(DE-HGF)$$aBachelor Thesis$$bbachelor$$mbachelor$$s1675339424_26081
000943468 3367_ $$2BibTeX$$aMASTERSTHESIS
000943468 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
000943468 502__ $$aBachelorarbeit, RWTH Aachen, 2022$$bBachelorarbeit$$cRWTH Aachen$$d2022
000943468 520__ $$aThe cortex exhibits a complex connectivity structure, containing spatially clustered connectivity patterns. Those, so-called patchy connections, are often neglected in studies concerning neural networks. However, they perform an important role in the efficiency of the cortex. The goal of neuromorphic hardware systems is, to imitate the highly efficient cortex structure, and perform faster-than-realtime calculations. In order to benchmark the performance of such systems, it is important to have realistic neural network models available. Here, such a benchmark model will be presented. Focusing on connectivity, the restrains on all other network aspects are kept to a minimum. It respects the biological distant dependent connectivity patterns, including spatial structure and patchy connectivity. Three different variations of the model, with slightly different spatially clustering approaches, are constructed. Additionally the model will provide benchmarking features. A fourth model, without spatial clustering is included to serve as a comparison.
000943468 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x0
000943468 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x1
000943468 536__ $$0G:(DE-HGF)POF4-899$$a899 - ohne Topic (POF4-899)$$cPOF4-899$$fPOF IV$$x2
000943468 8564_ $$uhttps://juser.fz-juelich.de/record/943468/files/BachelorThesis.pdf$$yRestricted
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000943468 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b0$$kRWTH
000943468 9131_ $$0G:(DE-HGF)POF4-899$$1G:(DE-HGF)POF4-890$$2G:(DE-HGF)POF4-800$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000943468 920__ $$lyes
000943468 9801_ $$aEXTERN4VITA
000943468 980__ $$abachelor
000943468 980__ $$aEDITORS
000943468 980__ $$aI:(DE-Juel1)JSC-20090406