This title appears in the Scientific Report :
2016
Please use the identifier:
http://hdl.handle.net/2128/12709 in citations.
Virtual Connectome: Discovering therelationship between structural and functionalconnectivity during steady-states andtransitions
Virtual Connectome: Discovering therelationship between structural and functionalconnectivity during steady-states andtransitions
The Virtual Connectome project is a new collaboration between the Charité Universitätsmedizin Berlin, the Indiana University Network Science Institute and the Neuroscience SimLab, JSC, Forschungszentrum Jülich.The main goal of the project is to understand the relationship between structural and func...
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Personal Name(s): | Diaz, Sandra |
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Peyser, Alexander | |
Contributing Institute: |
Institute for Advanced Simulation; IAS Jülich Supercomputing Center; JSC |
Imprint: |
2016
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Conference: | Aachen Jülich Mathematics Workshop, Jülich (Germany), 2016-06-15 - 2016-06-15 |
Document Type: |
Talk (non-conference) |
Research Program: |
SimLab Neuroscience Deutschland - USA Zusammenarbeit in Computational Science: Mechanistische Zusammenhänge zwischen Struktur und funktioneller Dynamik im menschlichen Gehirn Computational Science and Mathematical Methods |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
The Virtual Connectome project is a new collaboration between the Charité Universitätsmedizin Berlin, the Indiana University Network Science Institute and the Neuroscience SimLab, JSC, Forschungszentrum Jülich.The main goal of the project is to understand the relationship between structural and functional connectivity in the human brain.Experimental and simulated data of many subjects over long time spans will be analyzed and compared to identify the particular structural connectivity and brain model parameters which better predict the brain activity of the each individual.This presentation aims at providing a general introduction to the project and highlight current work which would benefit from collaborations with mathematicians in order to:1.Understand the capabilities of a Dynamic Mean Field Model of Neural Networks to model transitions between different dynamic states, and refine the model in order to better predict these states.2.Understand the role of the inner inhibition in this Dynamic Mean Field Model in order to obtain a simplified expression which depends on the strength of the inputs to a brain region. |