Books / Book Chapters

2022

• 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

2021

• 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

2020

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

2018

• 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"

2017

• 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

2016

• 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.
  DOI:10.1007/978-3-319-50862-7_5

2015

• Grün S. (2015) Spike Train Analysis: Overview. In: Jaeger D, Jung R. Book Section, Encyclopedia of Computational Neuroscience, Springer New York,
  DOI:10.1007/978-1-4614-6675-8_776
  
  
• Grün S. (2015) Surrogate Data for Evaluation of Spike Correlation.In: Jaeger D, Jung R: Book Section, Encyclopedia of Computational Neuroscience, Springer New York,
  DOI:10.1007/978-1-4614-6675-8_411
  
  
• Grün S. (2015) Unitary Event AnalysisIn: Jaeger D, Jung R. Book Section, Encyclopedia of Computational Neuroscience, Springer New York,
  DOI:10.1007/978-1-4614-6675-8_412
  
  

2014

• 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

2010

• Grün S. and Rotter S. (eds.) (2010) Analysis of Parallel Spike Trains. Springer Series in Computational Neuroscience 7, Springer New York
  DOI:10.1007/978-1-4419-5675-0
  
  
• Grün, S, Diesmann, M, Aertsen, A.  (2010) Unitary event analysis. In: Sonja Grün, Stefan Rotter eds.
  Analysis of Parallel Spike Trains Chapter 10, 191-220 Springer
  
  
• Louis, S, Borgelt, C, Grün S (2010) Generation and Selection of Surrogate Methods for Correlation Analysis. In: Sonja Grün, Stefan Rotter eds.
  Analysis of Parallel Spike Trains Chapter 17, 359-382 Springer
  
  
• Riehle A, Roux S, Kilavik BE, Grün S. (2010) Dynamics of motor cortical networks: the complementarity of spike synchrony and firing rate. In: Danion F, Latash ML eds.
  Motor Control: Theories, experiments, and applications Chapter 6, 141-158 Oxford Univ Press: New York
  
  
• Staude, B, Grün, S, Rotter S (2010) Higher-Order Correlations and Cumulants. In: Sonja Grün, Stefan Rotter eds.
  Analysis of Parallel Spike Trains Chapter 12, 253-280 Springer

2008

• Grün S, Abeles M, Diesmann M (2008) Impact of higher-order correlations on coincidence distributions of massively parallel data. In:
  Lecture Notes in Computer Science, Dynamic Brain - from Neural Spikes to Behaviors 5286, 96-114

2007

• Pazienti A, Diesmann M, Grün S (2007) Bounds of the ability to destroy precise coincidences by spike dithering. In:
  Lecture Notes in Computer Science, 4729, 428 - 437

2002

• 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.
  To download the article, click here.

2001

• Aertsen A, Diesmann M, Gewaltig M O, Grün S, Rotter S (2001) Neural dynamics in cortical networks – precision of joint-spiking events. In: G. Bock and J. Goode eds.
  Complexity in biological information processing, 193-207 Wiley

1996

• 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.

1995

• 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.

1994

• 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