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Integrative loops between experiment and model

Integrative loops between experiment and modelCopyright: Sonja Grün

Network models provide the possibility to learn about biophysical and network mechanisms that underlie observations in experimental data. Experimental approaches typically do not allow us to perturb the dynamics or change the network because invasive or destructive approaches are required. The alternative, to ask different questions to the biological system by newly designing and building experiments is often too time consuming. Therefore it is highly desirable to have reasonably detailed bottom-up models at hand that replicate the experimental findings and support virtual experiments. The groups at INM-6 concentrating on models of structure and dynamics provide the great opportunity to approach such questions together.

Publications

Gutzen R., von Papen M., Trensch G., Quaglio P., Grün S., Denker M. (2018) Reproducible neural network simulations: statistical methods for model validation on the level of network activity data. Frontiers in Neuroinf. DOI: 10.3389/fninf.2018.00090

Trensch G., Gutzen R., Blundell I., Denker M., Morrison A. (2018) Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data., Front. Neuroinform. 12:81. DOI: 10.3389/fninf.2018.00081

Dahmen D., Grün S., Diesmann M., Helias M (2018) Two types of criticality in the brain. arXiv:1711.10930

Rostami V., Porta Mana P., Grün S., Helias M. (2017) Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models. PLOS Computational Biology 13(10): e1005762. .DOI: 10.1371/journal.pcbi.1005762.

Senk J., Yegenoglu A., Amblet O., Brukau Y., Davison A., Lester DR., Lührs A., Quaglio P., Rostami V., Rowley A., Schuller B., Stokes AB., van Albada SJ., Zielasko D., Diesmann M., Weyers B., Denker M., Grün S. (2017) A collaborative simulation-analysis workflow for computational neuroscience using HPC. In: Di Napoli E, Hermanns M-A, Iliev H, Lintermann A, Peyser A eds. High-Performance Scientific Computing. Cham: Springer International Publishing, 243–256. DOI: 10.1007/978-3-319-53862-4_21.

Bouchard KE., Aimone JB., Chun M., Dean T., Denker M., Diesmann M., Donofrio DD., Frank LM., Kasthuri N., Koch C., Ruebel O., Simon HD., Sommer FT., Prabhat (2016). High-performance computing in neuroscience for data-driven discovery, integration, and dissemination. Neuron 92:628–631. DOI: 10.1016/j.neuron.2016.10.035.

Hagen E., Dahmen D., Stavrinou ML., Lindén H., Tetzlaff T., van Albada SJ., Grün S., Diesmann M., Einevoll GT. (2016) Hybrid scheme for modeling local field potentials from point-neuron networks. Cerebral Cortex 26:4461–4496. DOI: 10.1093/cercor/bhw237.

Schultze-Kraft M., Diesmann M., Grün S., Helias M. (2013) Noise suppression and surplus synchrony by coincidence detection. PLoS Computational Biology 9:e1002904. DOI: 10.1371/journal.pcbi.1002904.

Gerstein GL., Williams ER., Diesmann M., Grün S., Trengove C. (2012). Detecting synfire chains in parallel spike data. Journal of Neuroscience Methods 206:54–64. DOI: 10.1016/j.jneumeth.2012.02.003.

Lindén H., Tetzlaff T., Potjans TC., Pettersen KH., Grün S., Diesmann M., Einevoll GT. (2011) Modeling the spatial reach of the LFP. Neuron 72:859–872. DOI: 10.1016/j.neuron.2011.11.006.

Schrader S., Grün S., Diesmann M., Gerstein GL. (2008). Detecting synfire chain activity using massively parallel spike train recording. Journal of Neurophysiology 100:2165–2176. DOI: 10.1152/jn.01245.2007.


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