000916254 001__ 916254
000916254 005__ 20230104121219.0
000916254 037__ $$aFZJ-2022-06055
000916254 1001_ $$0P:(DE-HGF)0$$aKanzl, Lea Sophie$$b0
000916254 245__ $$aAn applied waveform relaxation method for distributed gap junctions in the Arbor simulation library$$f - 2022-08-15
000916254 260__ $$c2022
000916254 300__ $$a48
000916254 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
000916254 3367_ $$02$$2EndNote$$aThesis
000916254 3367_ $$2BibTeX$$aMASTERSTHESIS
000916254 3367_ $$2DRIVER$$amasterThesis
000916254 3367_ $$0PUB:(DE-HGF)19$$2PUB:(DE-HGF)$$aMaster Thesis$$bmaster$$mmaster$$s1672830680_22540
000916254 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
000916254 502__ $$aMasterarbeit, RWTH Aachen, 2022$$bMasterarbeit$$cRWTH Aachen$$d2022
000916254 520__ $$aWith morphologically detailed neurons at the forefront, the Arbor simulation library provides a framework for the simulation of large-scale spiking neural networks. Simulating the temporal behaviour of a neuron’s biophysical properties is heavily dependent on the implementation of connectivity between neurons via synapses and gap junctions. While the former intrinsically induce a signal delay, the latter implement an instantaneous influence on a cell’s membrane potential, calling for state updates in every time step during a simulation. Due to high levels of paral- lelization, the zero-delay communication of such state vectors across processes with MPI would be highly inefficient. The aim of this thesis is to implement and evalu- ate Waveform Relaxation as a method to iteratively solve systems of gap-junction- coupled neurons without the requirement of continuous data exchange across processes.
000916254 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000916254 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000916254 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x2
000916254 909CO $$ooai:juser.fz-juelich.de:916254$$pextern4vita
000916254 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000916254 9141_ $$y2022
000916254 920__ $$lyes
000916254 9801_ $$aEXTERN4VITA
000916254 980__ $$amaster
000916254 980__ $$aEDITORS
000916254 980__ $$aI:(DE-Juel1)JSC-20090406