000911032 001__ 911032
000911032 005__ 20240313103131.0
000911032 0247_ $$2Handle$$a2128/32732
000911032 0247_ $$2URN$$aurn:nbn:de:0001-2022112310
000911032 020__ $$a978-3-95806-651-9
000911032 037__ $$aFZJ-2022-04358
000911032 1001_ $$0P:(DE-Juel1)174497$$aLayer, Moritz$$b0$$eCorresponding author$$ufzj
000911032 245__ $$aDynamical and statistical structure of spatially organized neuronal networks
000911032 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2022
000911032 300__ $$axiii, 165
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000911032 4900_ $$aSchriften des Forschungszentrums Jülich Reihe Information / Information$$v85
000911032 502__ $$aDissertation, RWTH Aachen University, 2022$$bDissertation$$cRWTH Aachen University$$d2022
000911032 520__ $$aThe cerebral cortex, the outer layer of mammalian brains, comprises a vast number of neurons arranged and connected in a highly organized fashion. The likelihood of neurons to be connected and how fast they may exchange signals depends, among other properties, on their spatial distance. Cortical networks may be well described as completely random networks on microscopic scales because cortical neurons have essentially uniform connection probabilities within a few tens of micrometers. However, the distance-dependence of neuronal connections certainly is important on mesoscopic scales spanning several millimeters, where many neurons are most likely unconnected. While the theory of random networks is already well-established, how such a spatial organization affects a network’s activity is not yet fully understood. The objectiveof this thesis is to provide an overview of the current analytical understanding of spatially organized networks on a mesoscopic scale, as well as to advance this understanding with three studies covering complementary aspects of spatially organized network theory.
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000911032 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
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