Books / Book Chapters


  • van Albada, SJ., Morales-Gregorio, A., Dickscheid T., Goulas A., Bakker R. Bludau S., Palm G., Hilgetag CC., Diesmann M. (2022) Bringing Anatomical Information into Neuronal Network Models. In: Giugliano, M., Negrello, M., Linaro, D. (eds) Computational Modelling of the Brain. Advances in Experimental Medicine and Biology 1359.
    DOI: 10.1007/978-3-030-89439-9_9

  • Aertsen A., Grün S., Maldonado PE., Palm G. (eds) (2022) Introducing Computation to Neuroscience: Selected papers of George Gerstein. Springer Series in Computational Neuroscience ISBN 978-3-030-87446-9
    DOI: 10.1007/978-3-030-87447-6

  • Lawrie S., Moreno-Bote R., Gilson M. (2022) Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification. In: Smys S., Tavares JMRS., Balas VE. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing 1420, 587 - 601
    DOI: 10.1007/978-981-16-9573-5_42


  • van Albada S., Pronold J., van Meegen A., Diesmann M. (2021) Usage and scaling of an open-source spiking multi-area model of monkey cortex. In: Amunts K., Grandinetti L., Lippert Th., Petkov N. (eds.) Brain-Inspired Computing Cham: Springer, Lecture Notes in Computer Science 12339:47-59.
    DOI: 10.1007/978-3-030-82427-3


  • Helias M., Dahmen D. (2020) Statistical Field Theory for Neural Networks. Springer International Publishing
    DOI: 10.1007/978-3-030-46444-8


  • Schmidt M., Diesmann M., van Albada SJ. (2018) Necessity and feasibility of large-scale neuronal network simulations. In: Lecture Notes of the 49th IFF Spring School “Physics of Life"


  • 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


  • Denker M., Grün S. (2016) Designing workflows for the reproducible Analysis of Electrophysiological Data in: Amunts K, Grandinetti L, Lippert T, Petkov N: Brain Inspired Computing, Springer Series Lecture Notes in Computer Science, Vol 10087:58-72.


  • Grün S. (2015) Spike Train Analysis: Overview. In: Jaeger D, Jung R. Book Section, Encyclopedia of Computational Neuroscience, Springer New York,

  • Grün S. (2015) Surrogate Data for Evaluation of Spike Correlation.In: Jaeger D, Jung R: Book Section, Encyclopedia of Computational Neuroscience, Springer New York,

  • Grün S. (2015) Unitary Event AnalysisIn: Jaeger D, Jung R. Book Section, Encyclopedia of Computational Neuroscience, Springer New York,


  • van Albada SJ., Kunkel S., Morrison A., Diesmann M. (2014) Integrating Brain Structure and Dynamics on Supercomputers. In: Grandinetti L., Lippert T., Petkov N. (eds). Brain-Inspired Computing LNCS 8603:22-32.
    DOI: 10.1007/978-3-319-12084-3_3





  • Diesmann M., Gewaltig MO. (2002) NEST: An environment for neural systems. In: Plessert T., Macho V. (eds). Forschung und wissenschaftliches Rechnen, Beiträge zum Heinz-Billing-Preis 2001 Ges. für Wiss. Datenverarbeitung 58: 43-70.
    For the article, click here.



  • Grün S. (1996) Unitary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significance, and Interpretation. Reihe Physik, Band 60. Verlag Harri Deutsch, Thun, Frankfurt/Main.


  • Aertsen A, Diesmann M, Grün S, Arndt M, Gewaltig MO (1995) Coupling dynamics and coincident spiking in cortical neural networks. In: H. J. Hermann, D. E. Wolf, and E. Pöppel eds. Supercomputing in Brain Research: from Tomography to Neural Networks, 213-223 world Scientific

  • Riehle A, Seal J, Requin J, Grün S, Aertsen A (1995) Multi-electrode recording of neuronal activity in the motor cortex: Evidence for changes in the functional coupling between neurons. In: H. J. Hermann, D. E. Wolf, and E. Pöppel eds. upercomputing in Brain Research: from Tomography to Neural Networks, p 281–288. World Scientific.


  • Martignon L, von Hasseln H, Grün S, and Palm G (1994) Modelling the interaction in a set of neurons implicit in their frequency distribution: a possible approach to neural assemblies. In: Collected Lectures of the Seminar on Cybernetics. Rosenberg-Sellier

Last Modified: 27.06.2023