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| Hauptseite > Publikationsdatenbank > Construction of a Spiking Network Model of Macaque Primary Visual Cortex: Towards Digital Twins > print |
| Hauptseite > Publikationsdatenbank > Construction of a Spiking Network Model of Macaque Primary Visual Cortex: Towards Digital Twins > print |
| 001 | 1037847 | ||
| 005 | 20250423202217.0 | ||
| 020 | _ | _ | |a 978-3-95806-800-1 |
| 024 | 7 | _ | |2 datacite_doi |a 10.34734/FZJ-2025-00990 |
| 024 | 7 | _ | |2 URN |a urn:nbn:de:0001-2504220847268.413844325757 |
| 037 | _ | _ | |a FZJ-2025-00990 |
| 100 | 1 | _ | |0 P:(DE-Juel1)176776 |a Kurth, Anno |b 0 |e Corresponding author |
| 245 | _ | _ | |a Construction of a Spiking Network Model of Macaque Primary Visual Cortex: Towards Digital Twins |f - 2024-09-18 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2024 |
| 300 | _ | _ | |a xvi, 207 |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
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| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |s 1744791215_17200 |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich Reihe Information / Information |v 107 |
| 502 | _ | _ | |a Dissertation, RWTH Aachen University, 2024 |b Dissertation |c RWTH Aachen University |d 2024 |
| 520 | _ | _ | |a The cerebral cortex of the mammalian brain is composed of an unfathomable amount of neurons that are organized in intricate circuits across several spatial scales. If present, cortical activity reflects higher-level information processing in mammals. One approach to study the relationship between the cortex’ structure and its activity is to represent the studied physical system by a “digital twin”, a computational model in which anatomical and physiological findings can be incorporated. In such digital twins, experiments can be performed and data obtained not feasible using the “physical twin”. This thesis focuses on building a large scale, biologically plausible spiking network model of macaque primary visual cortex. As such, it combines results from the experimental literature and contributes to building ever more sophisticated digital twins of the visual cortex. This quest is embedded into a larger neuroscientific research program aiming at expanding the usage of computer models in Neurosciene. In line with this approach, in this thesis first resting state neural activity recorded from macaque primary visual cortex is analyzed. A separation of neural activity into two clusters that can be related to the monkey’s behavior is found that is co-modulated along with topdown signals from V4. To explore whether this co-modulation might be causative for the separation of states, in silico experiments of a model of the local cortical circuit are conducted. However, this simple model neglects much of the fine structure of visual cortex. Hence, subsequently a large-scale, biologically plausible digital twin of this area is devised. After unifying and integrating a large body of data across multiple sources, simulations of the model reveal unrealistic activity. This motivates a further investigation of cortical connectivity in light of recent advances of reconstruction of microcircuits in the brain. The findings offer potential resolutions for the encountered problems and highlight stark differences between recent and previous reconstructions of local cortical networks. To employ digital twins as research platforms in Neuroscience, simulation technologies need to be readily available for the research community. Such technologies have to be continuously developed and updated to meet the requirements of the researchers. To contribute to this endeavor, in this thesis the performance of the neural simulation tool NEST is assessed and compared with alternative approaches. Additionally, a benchmarking workflow with a view towards neural network simulations is developed that aids the continuous development of spiking neural network simulation technologies. |
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| 536 | _ | _ | |0 G:(EU-Grant)785907 |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
| 536 | _ | _ | |0 G:(EU-Grant)945539 |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 3 |
| 536 | _ | _ | |0 G:(GEPRIS)313856816 |a DFG project G:(GEPRIS)313856816 - SPP 2041: Computational Connectomics (313856816) |c 313856816 |x 4 |
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