This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.3389/fninf.2018.00032 in citations.
Please use the identifier: http://hdl.handle.net/2128/18896 in citations.
Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation
Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation
Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are...
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Personal Name(s): | Nowke, Christian (Corresponding author) |
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Diaz, Sandra / Weyers, Benjamin / Hentschel, Bernd / Morrison, Abigail / Kuhlen, Torsten W. / Peyser, Alexander | |
Contributing Institute: |
Jülich Supercomputing Center; JSC JARA - HPC; JARA-HPC Computational and Systems Neuroscience; INM-6 |
Published in: | Frontiers in neuroinformatics, 12 (2018) S. 32 |
Imprint: |
Lausanne
Frontiers Research Foundation
2018
|
DOI: |
10.3389/fninf.2018.00032 |
PubMed ID: |
29937723 |
Document Type: |
Journal Article |
Research Program: |
Deutschland - USA Zusammenarbeit in Computational Science: Mechanistische Zusammenhänge zwischen Struktur und funktioneller Dynamik im menschlichen Gehirn Human Brain Project Specific Grant Agreement 1 Supercomputing and Modelling for the Human Brain Theory, modelling and simulation Computational Science and Mathematical Methods SimLab Neuroscience |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/18896 in citations.
Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed. |