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001010679 005__ 20240313103135.0
001010679 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03187
001010679 0247_ $$2URN$$aurn:nbn:de:0001-20231004081300012-6971752-0
001010679 020__ $$a978-3-95806-708-0
001010679 037__ $$aFZJ-2023-03187
001010679 1001_ $$0P:(DE-Juel1)173607$$avan Meegen, Alexander$$b0$$eCorresponding author$$ufzj
001010679 245__ $$aSimulation and theory of large-scale cortical networks$$f- 2022-08-22
001010679 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2023
001010679 300__ $$a250 p.
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001010679 4900_ $$aSchriften des Forschungszentrums Jülich Reihe Information / Information$$v98
001010679 502__ $$aDissertation, Univ. Köln, 2022$$bDissertation$$cUniv. Köln$$d2022$$o2022-08-22
001010679 520__ $$aCerebral cortex is composed of intricate networks of neurons. These neuronal networks are strongly interconnected: every neuron receives, on average, input from thousands or more presynaptic neurons. In fact, to support such a number of connections, a majority of the volume inthe cortical gray matter is filled by axons and dendrites. Besides the networks, neurons themselves are also highly complex. They possess an elaborate spatial structure and support various types of active processes and nonlinearities. In the face of such complexity, it seems necessary to abstract away some of the details and to investigate simplified models.In this thesis, such simplified models of neuronal networks are examined on varying levels of abstraction. Neurons are modeled as point neurons, both rate-based and spike-based, and networks are modeled as block-structured random networks. Crucially, on this level of abstraction, the models are still amenable to analytical treatment using the framework of dynamical mean-field theory.The main focus of this thesis is to leverage the analytical tractability of random networks of point neurons in order to relate the network structure, and the neuron parameters, to the dynamics of the neurons—in physics parlance, to bridge across the scales from neurons to networks.More concretely, four different models are investigated: 1) fully connected feedforward networks and vanilla recurrent networks of rate neurons; 2) block-structured networks of rate neurons in continuous time; 3) block-structured networks of spiking neurons; and 4) a multi-scale, data-based network of spiking neurons. We consider the first class of models in the light of Bayesian supervised learning and compute their kernel in the infinite-size limit. In the second class of models, we connect dynamical mean-field theory with large-deviation theory, calculate beyond mean-field fluctuations, and perform parameter inference. For the third class of models, we develop a theory for the autocorrelation time of the neurons. Lastly, we consolidate data across multiple modalities into a layer- and population-resolved model of human cortex and compare its activity with cortical recordings.In two detours from the investigation of these four network models, we examine the distribution of neuron densities in cerebral cortex and present a software toolbox for mean-field analyses of spiking networks.
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001010679 536__ $$0G:(GEPRIS)313856816$$aDFG project 313856816 - SPP 2041: Computational Connectomics (313856816)$$c313856816$$x2
001010679 536__ $$0G:(GEPRIS)347572269$$aDFG project 347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269)$$c347572269$$x3
001010679 536__ $$0G:(DE-Juel1)jinb33_20090701$$aBrain-Scale Simulations (jinb33_20090701)$$cjinb33_20090701$$fBrain-Scale Simulations$$x4
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001010679 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x7
001010679 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x8
001010679 8564_ $$uhttp://kups.ub.uni-koeln.de/id/eprint/64465
001010679 8564_ $$uhttps://juser.fz-juelich.de/record/1010679/files/Information_98.pdf$$yOpenAccess
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001010679 9141_ $$y2023
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001010679 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001010679 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001010679 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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