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@PHDTHESIS{Pronold:976139,
author = {Pronold, Jari},
othercontributors = {Diesmann, Markus and Krämer, Michael},
title = {{L}arge-scale modeling and simulation of neuronal networks
in the human cortex},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-00013},
pages = {1 Online-Ressource : Illustrationen},
year = {2023},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2024; Dissertation, RWTH Aachen University, 2023},
abstract = {Neuroscience is concerned with how a complex network of
remarkably intricate neurons and synapses gives rise to
cognition. The cerebral cortices of macaque and human are
studied in this thesis and consist of billions of neurons
and trillions of synapses. Due to the sheer number of cells,
experimental studies can only focus on a limited number of
cells and locations simultaneously. However, in theory,
computational studies are not limited by the size of the
network: The upper bound is imposed by the size and power of
the computer and the performance of the simulation software.
Here, we study how bottlenecks in simulation software can be
overcome and how biological data can be used to build
large-scale computational models of cortices, enabling the
study of dynamics and function. We identify the delivery of
spikes to target neurons as especially time-consuming in
simulations of large-scale models. This phase involves
inherent random access to memory that cannot easily be
alleviated and causes long latencies. We describe how this
phase can be rearranged such that interleaved function calls
mask the latencies. The intricacies of this study expose the
need for a systematic benchmarking framework. In light of
multiple computers, simulation codes, and neuroscientific
models, it is imperative to keep track of results and
metadata. We present a framework that eases the challenges
involved with benchmarking. To study the link between
cortical structure and the dynamics of brain networks, we
integrate biological data on connectivity and
cytoarchitecture into a model of human cortex. The model is
layer- and population-resolved and statistical regularities
fill gaps in the experimental data. We poise the model into
a state where the activity matches single-cell cortical
recordings and whole-brain functional magnetic resonance
imaging scans. We study how a perturbation in the form of a
single spike propagates through the network. The following
study takes this a step further: We study the effect of
clustering of neuronal populations on the dynamics of a
large-scale model of macaque visual cortex and how
clustering enables targeted signal transmission on the
population level. With one exception, the clustered model
reproduces activity as well as or better than the original
model. In conclusion, this work shows how latencies due to
random memory access can be overcome and establishes a
workflow for systematic measurements of simulation code.
Furthermore, it presents how a large-scale model of cortex
can be built and how targeted signal transmission can be
realized. The results of this thesis support the efficient
study of hypotheses and functions in large-scale cortical
models.},
cin = {535500-2 ; 934910 / 136110 / 130000},
ddc = {530},
cid = {$I:(DE-82)535500-2_20140620$ / $I:(DE-82)136110_20140620$ /
$I:(DE-82)130000_20140620$},
pnm = {HBP SGA2 - Human Brain Project Specific Grant Agreement 2
(785907) / HBP SGA3 - Human Brain Project Specific Grant
Agreement 3 (945539) / DEEP-EST - DEEP - Extreme Scale
Technologies (754304) / ACA - Advanced Computing
Architectures (SO-092) / DFG project 347572269 -
Heterogenität von Zytoarchitektur, Chemoarchitektur und
Konnektivität in einem großskaligen Computermodell der
menschlichen Großhirnrinde (347572269) / GRK 2416 - GRK
2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
neuronaler multisensorischer Integration (368482240) / DFG
project 491111487 - Open-Access-Publikationskosten / 2022 -
2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) /
MetaMoSim - Generic metadata management for reproducible
high-performance-computing simulation workflows - MetaMoSim
(ZT-I-PF-3-026) / Brain-Scale Simulations
$(jinb33_20220812)$ / Helmholtz Platform for Research
Software Engineering - Preparatory Study
$(HiRSE_PS-20220812)$ / IVF - Impuls- und Vernetzungsfonds
(IVF-20140101) / DFG project 313856816 - SPP 2041:
Computational Connectomics (313856816) / Brain-Scale
Simulations $(jinb33_20121101)$},
pid = {G:(EU-Grant)785907 / G:(EU-Grant)945539 /
G:(EU-Grant)754304 / G:(DE-HGF)SO-092 / G:(GEPRIS)347572269
/ G:(GEPRIS)368482240 / G:(GEPRIS)491111487 /
G:(DE-Juel-1)ZT-I-PF-3-026 / $G:(DE-Juel1)jinb33_20220812$ /
$G:(DE-Juel-1)HiRSE_PS-20220812$ / G:(DE-HGF)IVF-20140101 /
G:(GEPRIS)313856816 / $G:(DE-Juel1)jinb33_20121101$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-00013},
url = {https://publications.rwth-aachen.de/record/976139},
}
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