Master Thesis

FZJ-2022-06055

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png An applied waveform relaxation method for distributed gap junctions in the Arbor simulation library

Kanzl, L. S.

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.

Note: Masterarbeit, RWTH Aachen, 2022

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Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
3. SLNS - SimLab Neuroscience (Helmholtz-SLNS) (Helmholtz-SLNS)

Appears in the scientific report 2022

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