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Editorial: Linking experimental and computational connectomics
Alexander Peyser1, Sandra Diaz Pier1, Wouter Klijn1
1SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany.
Network Neuroscience (Cambridge, Mass.)
|October 23, 2019
Summary
Generating detailed brain connectomes requires integrating experimental data with computational models. This approach bridges biophysical detail and global function for advanced neuroscience research.
Area of Science:
- Neuroscience
- Computational Biology
- High-Performance Computing
Background:
- Large-scale in silico experimentation relies on comprehensive connectome data.
- Existing anatomical structures are insufficient for advanced modeling.
Purpose of the Study:
- To link experimental connectomics, theoretical neuroscience, and high-performance computing.
- To foster the development of generative models for multiscale connectomes.
Main Methods:
- Integrating findings from experimental connectomics.
- Applying theoretical neuroscience principles.
- Utilizing high-performance computing resources.
Main Results:
- Examples of integrated research across domains are presented.
- A pathway towards comprehensive generative models is outlined.
Conclusions:
- Linking diverse research fields is crucial for advancing connectome modeling.
- This integration enables models that bridge biophysical detail and global brain function.
