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@PHDTHESIS{Layer:854997,
author = {Layer, Moritz},
othercontributors = {Helias, Moritz and Kampa, Björn M.},
title = {{D}ynamical and statistical structure of spatially
organized neuronal networks},
volume = {85},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
reportid = {RWTH-2022-09916},
isbn = {978-3-95806-651-9},
series = {Schriften des Forschungszentrums Jülich. Reihe Information
= Information},
pages = {1 Online-Ressource (xiii, 165 Seiten) : Illustrationen,
Diagramme},
year = {2022},
note = {Druckausgabe: 2022. - Onlineausgabe: 2022. - Auch
veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2022},
abstract = {The cerebral cortex, the outer layer of mammalian brains,
comprises a vast number of neurons arranged and connected in
a highly organized fashion. The likelihood of neurons to be
connected and how fast they may exchange signals depends,
among other properties, on their spatial distance. Cortical
networks may be well described as completely random networks
on microscopic scales because cortical neurons have
essentially uniform connection probabilities within a few
tens of micrometers. However, the distance-dependence of
neuronal connections certainly is important on mesoscopic
scales spanning several millimeters, where many neurons are
most likely unconnected. While the theory of random networks
is already well-established, how such a spatial organization
affects a network's activity is not yet fully understood.
The objective of this thesis is to provide an overview of
the current analytical understanding of spatially organized
networks on a mesoscopic scale, as well as to advance this
understanding with three studies covering complementary
aspects of spatially organized network theory.A variety of
experimental recordings in cortex reveals that neuronal
activity is coordinated across several millimeters:
Multi-electrode-arrays covering a few square millimeters,
for example, provide access to the local field potential, a
measure of population activity, as well as single neuron
spiking activity. While spiking activity exhibits
distance-dependent correlation characteristics, population
activity shows spatio-temporally coherent activity, like
periodic patterns, waves, or bumps. In this thesis we employ
a combination of network models, analytical tools, and
simulations to gain an understanding of such findings. We
particularly make use of mean-field theory, which is a
viable tool for investigating statistical properties of
populations made up of thousands of neurons, and it
therefore may be utilized to gain a coarse-grained
description of network activity at large scales. In the
first main part, we present a Python package we developed to
make previously developed analytical results from neuronal
network mean-field theory applicable to concrete network
models, giving access to estimates of model properties such
as firing rates and power spectra, as well as more elaborate
tools that can support network modeling. In the second
study, we investigate how neurons may coordinate their
activity dynamically across large distances, without the
need for highly correlated input or long-range connections.
In the third study, we explore how a temporal delay may
affect pattern formation in planar networks. As we
demonstrate, spatial organization is a critical network
feature that does not merely lead to obvious phenomena like
spatially structured activity. On the contrary, as we show
in this thesis, spatial organization leads to a variety of
interesting, non-trivial effects, that on first sight might
even seem counterintuitive, and this topic certainly
provides a multitude of intriguing research questions for
the near future.},
cin = {163110 / 136930 ; 136920 / 130000 / 160000},
ddc = {570},
cid = {$I:(DE-82)163110_20180110$ / $I:(DE-82)136930_20160614$ /
$I:(DE-82)130000_20140620$ / $I:(DE-82)160000_20140620$},
pnm = {GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze
zur Aufklärung neuronaler multisensorischer Integration
(368482240) / HBP SGA1 - Human Brain Project Specific Grant
Agreement 1 (720270) / HBP SGA2 - Human Brain Project
Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
Project Specific Grant Agreement 3 (945539) / JL SMHB -
Joint Lab Supercomputing and Modeling for the Human Brain
(JL SMHB-2021-2027)},
pid = {G:(GEPRIS)368482240 / G:(EU-Grant)720270 /
G:(EU-Grant)785907 / G:(EU-Grant)945539 / G:(DE-Juel1)JL
SMHB-2021-2027},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2022-09916},
url = {https://publications.rwth-aachen.de/record/854997},
}
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