000848193 001__ 848193 000848193 005__ 20240313094928.0 000848193 0247_ $$2doi$$a10.3389/fninf.2018.00032 000848193 0247_ $$2Handle$$a2128/18896 000848193 0247_ $$2pmid$$apmid:29937723 000848193 0247_ $$2WOS$$aWOS:000433903200001 000848193 0247_ $$2altmetric$$aaltmetric:41139040 000848193 037__ $$aFZJ-2018-03459 000848193 082__ $$a610 000848193 1001_ $$0P:(DE-HGF)0$$aNowke, Christian$$b0$$eCorresponding author 000848193 245__ $$aToward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation 000848193 260__ $$aLausanne$$bFrontiers Research Foundation$$c2018 000848193 3367_ $$2DRIVER$$aarticle 000848193 3367_ $$2DataCite$$aOutput Types/Journal article 000848193 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1563262119_618 000848193 3367_ $$2BibTeX$$aARTICLE 000848193 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000848193 3367_ $$00$$2EndNote$$aJournal Article 000848193 520__ $$aSimulation 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. 000848193 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0 000848193 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1 000848193 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2 000848193 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x3 000848193 536__ $$0G:(DE-Juel1)BMBF-01GQ1504B$$aVirtual Connectomics - Deutschland - USA Zusammenarbeit in Computational Science: Mechanistische Zusammenhänge zwischen Struktur und funktioneller Dynamik im menschlichen Gehirn (BMBF-01GQ1504B)$$cBMBF-01GQ1504B$$x4 000848193 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - 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