This title appears in the Scientific Report :
2016
Please use the identifier:
http://dx.doi.org/10.12751/nncn.bc2016.0059 in citations.
Please use the identifier: http://hdl.handle.net/2128/12715 in citations.
Multiscale approach to explore the relationships between connectivity and function in whole brain simulations
Multiscale approach to explore the relationships between connectivity and function in whole brain simulations
To better understand the relationship between connectivity and function in the brain at different scales, in this work we show the results of using point-neuron network simulations to complement connectivity information from whole brain simulations based on a dynamic neuron mass model. In our multis...
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Personal Name(s): | Diaz, Sandra (Corresponding author) |
---|---|
Nowke, Christian (Corresponding author) / Peyser, Alexander / Weyers, Benjamin / Hentschel, Bernd / Morrison, Abigail / Kuhlen, Torsten W. | |
Contributing Institute: |
Jülich Supercomputing Center; JSC JARA - HPC; JARA-HPC |
Imprint: |
2016
|
DOI: |
10.12751/nncn.bc2016.0059 |
Conference: | HBP Summit 2016, Florence (Italy), 2016-10-12 - 2016-10-14 |
Document Type: |
Poster |
Research Program: |
Deutschland - USA Zusammenarbeit in Computational Science: Mechanistische Zusammenhänge zwischen Struktur und funktioneller Dynamik im menschlichen Gehirn W2/W3 Professorinnen Programm der Helmholtzgemeinschaft Connectivity and Activity Computational Science and Mathematical Methods Theory, modelling and simulation SimLab Neuroscience |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/12715 in citations.
To better understand the relationship between connectivity and function in the brain at different scales, in this work we show the results of using point-neuron network simulations to complement connectivity information from whole brain simulations based on a dynamic neuron mass model. In our multiscale approach, we simulate a whole brain parcellated into 68 regions where each region is modeled as a dynamic neuron mass, and in parallel, we also model each region as small 200 point-neuron populations in NEST. Structural plasticity in NEST is then used to calculate inner connectivity of each region with the aid of an interactive tool designed for visualizing and steering the algorithm. Using this approach, the fitting and parameter space exploration times are reduced and a new way to explore the impact of connectivity in function at different scales is presented. |