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Publikationen:
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Hahne, J., Helias, M., Kunkel, S., Igarashi, J., Bolten, M., Frommer, A. and Diesmann, M. (2015).
A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.
Front. Neuroinform. 9:22. doi: 10.3389/fninf.2015.00022
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Hahne, J., Helias, M., Kunkel, S., Igarashi, J., Kitayama, I., Wylie, B., Bolten, M., Frommer, A., and Diesmann, M. (2016).
Including gap junctions into distributed neuronal network simulations.
In: Brain Inspired Computing, eds: Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov. Lecture Notes in Computer Science 10087, Springer, 43-57.
http://link.springer.com/chapter/10.1007/978-3-319-50862-7_4
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Hahne, J., Dahmen, D., Schuecker, J., Frommer, A., Bolten, M., Helias, M. and Diesmann, M. (2017).
Integration of continuous-time dynamics in a spiking neural network simulator.
Front. Neuroinform. 11:34. doi: 10.3389/fninf.2017.00034
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Senden, M., Schuecker, J., Hahne, J., Diesmann, M., Goebel, R. (2018).
[Re] A neural model of the saccade generator in the reticular formation.
ReScience, volume 4, issue 1, #3.
doi: 10.5281/zenodo.1241004
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Hahne, J. (2018).
Waveform-relaxation methods for ordinary and stochastic differential equations with applications in distributed neural network simulations.
Dissertation.
urn:nbn:de:hbz:468-20180727-140556-2
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Bolten, M. and Hahne, J. (2019).
An SDE waveform‐relaxation method with application in distributed neural network simulations.
Proc. Appl. Math. Mech., 19: e201900373.
doi: 10.1002/pamm.201900373
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