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@MASTERSTHESIS{Kanzl:916254,
author = {Kanzl, Lea Sophie},
title = {{A}n applied waveform relaxation method for distributed gap
junctions in the {A}rbor simulation library},
school = {RWTH Aachen},
type = {Masterarbeit},
reportid = {FZJ-2022-06055},
pages = {48},
year = {2022},
note = {Masterarbeit, RWTH Aachen, 2022},
abstract = {With 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.},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511) / HBP SGA3 - Human
Brain Project Specific Grant Agreement 3 (945539) / SLNS -
SimLab Neuroscience (Helmholtz-SLNS)},
pid = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)945539 /
G:(DE-Juel1)Helmholtz-SLNS},
typ = {PUB:(DE-HGF)19},
url = {https://juser.fz-juelich.de/record/916254},
}