%0 Thesis
%A Kanzl, Lea Sophie
%T An applied waveform relaxation method for distributed gap junctions in the Arbor simulation library
%I RWTH Aachen
%V Masterarbeit
%M FZJ-2022-06055
%P 48
%D 2022
%Z Masterarbeit, RWTH Aachen, 2022
%X 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.
%F PUB:(DE-HGF)19
%9 Master Thesis
%U https://juser.fz-juelich.de/record/916254