Peer-reviewed publications


  • Albers J., Pronold J., Kurth AC., Vennemo SB., Haghighi Mood K., Patronis A., Terhorst D., Jordan J., Kunkel S., Tetzlaff T., Diesmann M., Senk J. (2022) A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations. Front. Neuroinform. 16:837549.
    DOI: 10.3389/fninf.2022.837549

  • Bouhadjar Y., Wouters DJ., Diesmann M., Tetzlaff T. (2022) Sequence learning, prediction, and replay in networks of spiking neurons. PLoS Comput Biol 18(6):e1010233.
    DOI: 10.1371/journal.pcbi.1010233

  • Boutaib Y., Bartolomaeus W., Nestler S., Rauhut H. (2022) Path classification by stochastic linear recurrent neural networks. Advances in Continuous and Discrete Models: Theory and Applications. Accepted.
    DOI: 10.1186/s13662-022-03686-9

  • Chen X., Morales-Gregorio A., Sprenger J., Kleinjohann A., Sridhar S., van Albada S., Grün S., Roelfsema P. (2022) 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state. Scientific Data.
    DOI: 10.1038/s41597-022-01180-1

  • Dahmen D., Layer M., Deutz L., Dabrowska PA., Voges N., von Papen M., Brochier T., Riehle A., Diesmann M., Grün S., Helias M. (2022) Global organization of neuronal activity only requires unstructured local connectivity. eLife 2022;11:e68422.
    DOI: 10.7554/eLife.68422

  • Grün S., Li J., McNaughton B., Petersen C., McCormick D., Robson D., Buzsáki G., Harris K., Sejnowski T., Mrsic-Flogel T., Lindén H., Roland PE. (2022) Emerging principles of spacetime in brains: Meeting report on spatial neurodynamics. Neuron, Volume 110, Issue 12, 2022, pages 1894-1988.
    DOI: 10.1016/j.neuron.2022.05.18

  • Heittmann A., Psychou G., Trensch G., Cox CE., Wilcke WW., Diesmann M., Noll TG. (2022) Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer. Front. Neurosci. 15:728460.
    DOI: 10.3389/fnins.2021.728460

  • Herbers P., Calvo I., Diaz-Pier S., Robles OD., Mata S., Toharia P., Pastor L., Peyser A., Morrison A., Klijn W. (2022) ConGen - A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks. Front. Neuroinform. 15:766697.
    DOI: 10.3389/fninf.2021.766697

  • Ito J., Joana C., Yamane Y., Fujita I., Tamura H., Maldonado PE., Grün S. (2022) Latency shortening with enhanced sparseness and responsiveness in V1 during active visual sensing. Sci Rep 12, 6021.
    DOI: 10.1038/s41598-022-09405-4

  • Kiefer CM., Ito J., Weidner R., Boers F., Shah NJ., Grün S., Dammers J. (2022) Revealing Whole-Brain Causality Networks During Guided Visual Searching. Front. Neurosci. 16:826083.
    DOI: 10.3389/fnins.2022.826083

  • Korcsak-Gorzo A., Müller MG., Baumbach A., Leng L., Breitwieser OJ., van Albada SJ., Senn W., Meier K., Legenstein R., Petrocivi MA. (2022) Cortical oscillations support sampling-based computations in spiking neural networks. PLoS Comput Biol 18(3): e1009753.
    DOI: 10.1371/journal.pcbi.1009753

  • Kurth AC., Senk J.,Terhorst D.,Finnerty J., Diesmann M. (2022) Sub-realtime simulation of a neuronal network of natural density. Neuromorph. Comput. Eng. 2 021001.
    DATA available at Zenodo: 10.5281/zenodo.5637375

  • Layer M., Senk J., Essink S., van Meegen A., Bos H., Helias M. (2022) NNMT: Mean-field based analysis tools for neuronal network models. Front. Neuroinform.
    DOI: 10.3389/fninf.2022.883333

  • Pronold J., Jordan J., Wylie BJN., Kitayama I., Diesmann M., Kunkel S. (2022) Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring. Front. Neuroinform. 15:785068.
    DOI: 10.3389/fninf.2021.785068

  • Pronold J., Jordan J. Wylie BJN., Kitayama I., Diesmann M., Kunkel S. (2022) Routing Brain Traffic Through the Von Neumann Bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers. Parallel Computing. 113:102952.
    DOI: 10.1016/j.parco.2022.102952

  • Senk J., Kriener B., Djurfeldt M., Voges N., Jiang HJ., Schüttler L., Gramelsberger G., Diesmann M., Plesser HE., van Albada SJ. (2022) Connectivity concepts in neuronal network modeling. PLoS Comput Biol. 18(9):e1010086.
    DOI: 10.1371/journal.pcbi.1010086

  • Stella A., Bouss P., Palm G., Grün S. (2022) Comparing surrogates to evaluate precisely timed higher-order spike correlations. eNeuro.
    DOI: 10.1523/ENEURO.0505-21.2022

  • Tiberi L., Stapmanns J., Kühn T., Luu T., Dahmen D., Helias M. (2022) Gell-Mann–Low Criticality in Neural Networks. Phys. Rev. Lett. 128, 168301.
    DOI: 10.1103/PhysRevLett.128.168301

  • Tiddia G., Golosio B., Albers J., Senk J., Simula F., Pronold J., Fanti V., Pastorelli E., Pier Stanislao P., van Albada SJ. (2022) Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster. Front. Neuroinform. 16:883333.
    DOI: 10.3389/fninf.2022.883333

  • van der Vlag M., Woodman M., Fousek J., Diaz-Pier S., Pérez Martín A., Jirsa  V., Morrison A. (2022) RateML: A Code Generation Tool for Brain Network Models. Front. Netw. Physiol. 2:826345.
    DOI: 10.3389/fnetp.2022.826345

  • Weidner R., Boers F., Shah NJ., Grün S., Dammers J. (2022) Revealing Whole-Brain Causality Networks During Guided Visual Searching. Front. Neurosci. 16:826083.
    DOI: 10.3389/fnins.2022.826083


  • 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

  • Dąbrowska PA., Voges N., von Papen M., Ito J., Dahmen D., Riehle A., Brochier T., Grün S. (2021) On the Complexity of Resting State Spiking Activity in Monkey Motor Cortex. Cerebral Cortex Communications: Volume 2, Issue 3, 2021, tgab033.
    DOI: 10.1093/texcom/tgab033

  • Dasbach S., Tetzlaff T., Diesmann M. and Senk J. (2021) Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution. Front. Neurosci. 15:757790.
    DOI: 10.3389/fnins.2021.757790

  • Denker M., Grün S., Wachtler T., Scherberger H. (2021) Reproducibility and efficiency in handling complex neurophysiological data. Neuroforum:000010151520200041.
    DOI: 10.1515/nf-2020-0041

  • Keup C., Kühn T., Dahmen D. and Helias M. (2021) Transient Chaotic Dimensionality Expansion by Recurrent Networks. Phys. Rev. X 11, 021064.
    DOI: 10.1103/PhysRevX.11.021064

  • van Meegen A., van Albada SJ. (2021) Microscopic theory of intrinsic timescales in spiking neural networks. Physical Review Research 3(4), 043077.
    DOI: 10.1103/PhysRevResearch.3.043077

  • van Meegen A., Kühn T., Helias, M. (2021) Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions. Physical Review Letters 127(15), 158302.
    DOI: 10.1103/PhysRevLett.127.158302

  • Porrmann F., Pilz S., Stella A., Kleinjohann A., Denker M., Hagemeyer J., Rückert U. (2021) Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Front. Neuroinform. 15:723406.
    DOI: 10.3389/fninf.2021.723406

  • Spreizer, S., Senk, J., Rotter, S., Diesmann, M., Weyers, B. (2021) NEST Desktop - An educational application for neuroscience. ENEURO. 0274-21.2021
    DOI: 10.1523/ENEURO.0274-21.2021

  • Stapmanns J., Hahne J., Helias M., Bolten M., Diesmann M. and Dahmen D. (2021) Event-Based Update of Synapses in Voltage-Based Learning Rules. Front. Neuroinform. 15:609147.
    DOI: 10.3389/fninf.2021.609147

  • Weidel P., Duarte R., Morrison A. (2021) Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks Front. Comput. Neurosci. 15:543872
    DOI: 10.3389/fncom.2021.543872

  • Wachtler T., Bauer P., Denker M., Grün S., Hanke M., Klein J., Oeltze-Jafra S., Ritter P., Rotter S., Scherberger H., Stein A., Witte OW. (2021) NFDI-Neuro: building a community for neuroscience research data management in Germany. euroforum:000010151520200036.
    DOI: 10.1515/nf-2020-0036


  • Bachmann C., Tetzlaff T., Duarte R., Morrison A. (2020) Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer’s disease. PLoS Computational Biology 16(8).
    DOI: 10.1371/journal.pcbi.1007790

  • Dahmen D., Gilson M., Helias M. (2020) Capacity of the covariance perceptron. Journal of physics A 53(35), 354002.
    DOI: 10.1088/1751-8121/ab82dd

  • Denker M., Stein A., Wachtler T. (2020) Better data - better science: NFDI Neuroscience: an initiative to promote efficient data management for neuroscience. Neuroforum 26(2), 119-120.
    DOI: 10.1515/nf-2020-0010

  • Gilson M., Dahmen D., Moreno-Bote R., Insabato A., Helias M. (2020) The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks. PLoS Comput Biol.
    DOI: 10.1371/journal.pcbi.1008127

  • Helias M. (2020) Momentum-dependence in the infinitesimal Wilsonian renormalization group. Journal of Physics A 53(44).
    DOI: 10.1088/1751-8121/abb169

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

  • Jordan J., Helias M., Diesmann M., Kunkel S. (2020) Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Front. Neuroinform. 14:12.
    DOI: 10.3389/fninf.2020.00012

  • Nestler S., Keup C., Dahmen D., Gilson M., Rauhut H., Helias M. (2020) Unfolding recurrence by Green’s functions for optimized reservoir computing. 34th Conference on Neural Information Processing Systems, NeurIPS 2020, online, 6 Dec 2020 – 12 Dec 2020, 1pp.

  • René A., Longtin A., Macke JH. (2020) Inference of a Mesoscopic Population Model from Population Spike Trains. Neural Comput. 32(8):1448-1498.
    DOI: 10.1162/neco_a_01292

  • Senk J., Korvasova K., Schuecker J., Hagen E., Tetzlaff T., Diesmann M., Helias M. (2020) Conditions for wave trains in spiking neural networks. Phys. Rev. Research 2:023174.
    DOI: 10.1103/PhysRevResearch.2.023174

  • Stapmanns J., Kühn T., Dahmen D., Luu T., Honercamp C., Helias, M. (2020) Self-consistent formulations for stochastic nonlinear neuronal dynamics. Phys. Rev. E 101(4).
    DOI: 10.1103/PhysRevE.101.042124

  • Tatsuno M., Malek S., Kalvi L., Ponce-Alvarez A., Ali K., Euston DR., Grün S., McNaughton BL. (2020) Memory reactivation in rat medial prefrontal cortex occurs in a subtype of cortical UP state during slow-wave sleep. Philos. Trans. R. Soc. Lond. B Biol. Sci. 25;375(1799):20190227.
    DOI: 10.1098/rstb.2019.0227

  • Timonidis N., Bakker R., Tiesinga P. (2020) Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics 18(4):611-626.
    DOI: 10.1007/s12021-020-09471-x


  • Bach F., Bertuch O., Buss C., zu Castell W., Celo S., Denker M., Dinkelacker S., Druskat S., Faber C., Finke A., Fritzsch B., Hammitzsch M., Haseleu J., Konrad U., Krupa J., Leifels Y., Mohns-Pöschke K., Moravcikova M., Nöller J., Möhl C., Nolden M., Scheinert M., Schelhaas U., Scheliga KS., Schlauch T., Schnicke T., Scholz A., Schwennsen F., Seifarth J., Selzer M., Shishatskiy S., Steglich D., Strohbach S., Terhorst D., Al-Turany M., Vierkant P., Wieser T., Witter L., Wortmann D. (2019) Muster-Richtlinie Nachhaltige Forschungssoftware an denHelmholtz-Zentren. Potsdam Helmholtz Open Science Office, 16 p.
    DOI: 10.2312/os.helmholtz.007

  • Dahmen D., Grün S., Diesmann M., Helias M. (2019) Second type of criticality in the brain uncovers rich multiple-neuron dynamics. Proceedings of the National Academy of Sciences of the United States of America 16:1305-13060.
    DOI: 10.1073/pnas.1818972116

  • Duarte R., Morrison A. (2019) Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLoS Computational Biology 25;15[4]:e1006781.
    DOI: 10.1371/journal.pcbi.1006781.

  • Einevoll GT., Destexhe A., Diesmann M., Grün S., Jirsa V., de Kamps M., Migliore M., Ness TV., Plesser HE., Schürmann F. (2019) The Scientific Case for Brain Simulations. Neuron 102: 735-744.
    DOI: 10.1016/j.neuron.2019.03.027

  • Gleeson P., Cantarelli M., Marin B., Quintana A., Earnshaw M., Sadeh S., Piasini E., Birgiolas J., Cannon RC., Cayco-Gajic NA., Crook S., Davison AP., Dura-Bernal S., Ecker A., Hines ML., Idili G., Lanore F., Larson SD., Lytton WW., Majumdara A., McDougal RA., Sivagnanam S., Solinas S., Stanislovas R., van Albada SJ., van Geit W., Silver RA. (2019) Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron 103: 395-411.e5.
    DOI: 10.1016/j.neuron.2019.05.019

  • Hilgetag CC., Beul SF., van Albada SJ., Goulas A. (2019) An architectonic type principle integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate brain. Network Neuroscience 3: 905-923.
    DOI: 10.1162/netn_a_00100

  • Ito J., Lucrezia E., Palm G., Grün S. (2019) Detection and evaluation of bursts in terms of novelty and surprise. Mathematical Biosciences and Engineering 16:6990-7008.
    DOI: 10.3934/mbe.2019351

  • Jo HG., Ito J., Schulte Holthausen B., Baumann C., Grün S., Habel U., Kellermann T. (2019) Task-dependent functional organizations of the visual ventral stream. Scientific Reports 9:9316.
    DOI: 10.1038/s41598-019-45707-w

  • Jo HG., Kellermann T., Baumann C., Ito J., Schulte Holthausen B., Schneider F., Grün S., Habel U. (2019) Distinct modes of top-down cognitive processing in the ventral visual cortex. NeuroImage 193: 201-213.
    DOI: 10.1016/j.neuroimage.2019.02.068

  • Jordan J., Petrovici MA., Breitwieser O., Schemmel J., Meier K., Diesmann M., Tetzlaff T. (2019) Deterministic networks for probabilistic computing. Sci Rep 9, 18303.
    DOI: 10.1038/s41598-019-54137-7

  • Jordan J., Weidel P., Morrison A. (2019) A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents. Frontiers Computational Neuroscience 13:46.
    DOI: 10.3389/fncom.2019.00046

  • Kobayashi R., Kurita S., Kurth A., Kitano K., Mizuseki K., Diesmann M., Richmond BJ., Shinomoto S. (2019) Reconstructing neuronal circuitry from parallel spike trains. Nature Communications 10: 4468.
    DOI: 10.1038/s41467-019-12225-2

  • Peyser A., Diaz Pier S., Klijn W., Morrison A., Triesch J. (2019) Editorial: Linking experimental and computational connectomics. Network Neuroscience (2019) 3 (4): 902-904
    DOI: 10.1162/netn_e_00108

  • Sprenger J., Zehl L., Pick J., Sonntag M., Grewe J., Wachtler T., Grün S., Denker M. (2019) odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments. Frontiers in Neuroinformatics 13:62.
    DOI: 10.3389/fninf.2019.00062

  • Stella A., Quaglio P., Torre E., Grün S. (2019) 3d-SPADE: Significance evaluation of spatio-temporal patterns of various temporal extents. Biosystems, 185: 104022.
    DOI: 10.1016/j.biosystems.2019.104022

  • Sukiban J., Voges N., Dembek TA., Pauli R., Visser-Vandewalle V., Denker M., Weber I., Timmermann L., Grün S. (2019) Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations. Neuroscience 414: 168-185.
    DOI: 10.1016/j.neuroscience.2019.07.005

  • Zajzon B., Mahmoudian S., Morrison A., Duarte R. (2019) Passing the Message: Representation Transfer in Modular Balanced Networks. Front. Comput. Neurosci. 13:79.
    DOI: 10.3389/fncom.2019.00079

  • Zajzon B., Morales-Gregorio A. (2019) Trans-thalamic Pathways: Strong Candidates for Supporting Communication between Functionally Distinct Cortical Areas. Journal of Neuroscience 39: 7034-7036.
    DOI: 10.1523/JNEUROSCI.0656-19.2019


  • van Albada SJ., Rowley AG., Senk J., Hopkins M., Schmidt M., Stokes AB., Lester DR., Diesmann M., Furber SB. (2018) Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model. Frontiers in Neuroscience 12:291.
    DOI: 10.3389/fnins.2018.00291

  • Bachmann C., Jacobs HIL., Porta Mana P., Dillen K., Richter N., von Reutern B., Dronse J., Onur OA., Langen KJ., Fink GR., Kukolja J., Morrison, A. (2018) On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease. Frontiers in Neuroscience 12: 528.
    DOI: 10.3389/fnins.2018.00528

  • Bahuguna J., Weidel P., Morrison A. (2018) Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework. European Journal of Neuroscience 49: 737-753.
    DOI: 10.1111/ejn.14021

  • Blundell I., Brette R., Cleland TA., Close TG., Coca D., Davison AP., Diaz S., Fernandez Musoles C., Gleeson P., Goodman DFM., Hines M., Hopkin MW., Kumbhar P., Lester DR., Marin B., Morrison A., Müller E., Nowotny T., Peyser A., Plotnikov D., Richmond P., Rowley A., Rumpe B., Stimberg M., Stokes AB., Tomkins A., Trensch G., Woodman M., Eppler JM. (2018) Code Generation in Computational Neuroscience: A Review of Tools and Techniques. Frontiers in Neuroinformatics 12: 68.
    DOI: 10.3389/fninf.2018.00068

  • Blundell I., Plotnikov D., Eppler JM., Morrison A. (2018) Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models. Frontiers in Neuroinformatics 12: 50.
    DOI: 10.3389/fninf.2018.00050

  • Bouchard KE., Aimone JB., Chun M., Dean T., Denker M., Diesmann M., Donofrio DD., Frank LM., Kasthuri N., Koch C., Rubel O., Simon HD., Sommer FT., Prabhat (2018) International Neuroscience Initiatives through the Lens of High-Performance Computing. IEEE Computer.
    DOI: 10.1109/MC.2018.2141039

  • Brochier T., Zehl L., Hao Y., Duret M., Sprenger J., Denker M., Grün S., Riehle A. (2018) Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. (Data publication) Scientific Data 5:180055.
    DOI: 10.1038/sdata.2018.55.
    Data available at

  • Denker M., Zehl L., Kilavik BE., Diesmann M., Brochier T., Riehle A., Grün S. (2018) LFP beta amplitude is linked to mesoscopic spatio-temporal phase patterns. Scientific Reports 8:5200.
    DOI: 10.1038/s41598-018-22990-7

  • 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 Neuroinformatics 12:90.
    DOI: 10.3389/fninf.2018.00090

  • de Haan M., Brochier TG., Grün S., Riehle A., Barthelmy FV. (2018) Real-time visuomotor behavior and electrophysiology recording setup for use with humans and monkeys. Journal of Neurophysiology 120: 539-552.
    DOI: 10.1152/jn.00262.2017

  • Heers M., Helias M., Hedrich T., Dümpelmann M., Schulze-Bonhage A., Ball T. (2018) Spectral bandwidth of interictal fast epileptic activity characterizes the seizure onset zone. NeuroImage: Clinical 17: 865-872.
    DOI: 10.1016/j.nicl.2017.11.021

  • Heiberg T., Kriener B., Tetzlaff T., Einevoll GT., Plesser HE. (2018) Firing-rate models for neurons with a broad repertoire of spiking behaviors. Journal of Computational Neuroscience 45: 103-132.
    DOI: 10.1007/s10827-018-0693-9

  • Jordan J., Ippen T., Helias M., Kitayama I., Sato M., Igarashi J., Diesmann M., Kunkel S. (2018) Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics 12:2.
    DOI: 10.3389/fninf.2018.00002

  • Kass RE., Amari S., Arai K., Brown EN., Diekman CO., Diesmann M., Doiron B., Eden U., Fairhall A., Fiddyment GM., Fukai T., Grün S., Harrison MT., Helias M., Nakahara H., Teramae J., Thomas PJ., Reimers M., Rodu J., Rotstein HG., Shea-Brown E., Shimazaki H., Shinomoto S., Yu BM., Kramer MA. (2018) Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application 5: 183-214.
    DOI: 10.1146/annurev-statistics-041715-033733.

  • Krishnan J., Porta Mana PGL., Helias M., Diesmann M., Di Napoli E. (2018) Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons. Frontiers in Neuroinformatics 11:75.
    DOI: 10.3389/fninf.2017.00075.

  • Kühn T., Helias M. (2018) Expansion of the effective action around non-Gaussian theories. Journal of Physics A: Mathematical and Theoretical 51:375004.
    DOI: 10.1088/1751-8121/aad52e.

  • Maksimov A., Diesmann M., van Albada SJ. (2018) Criteria on Balance, Stability and Excitability in Cortical Networks for Constraining Computational Models. Frontiers in Computational Neuroscience 12: 44.
    DOI: 10.3389/fncom.2018.00044

  • Nowke C., Diaz-Pier S., Weyers B., Hentschel B., Morrison A., Kuhlen TW., Peyser A. (2018) Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation. Frontiers in Neuroinformatics 12:32.
    DOI: 10.3389/fninf.2018.00032.

  • Pauli R., Weidel P., Kunkel S., Morrison A. (2018) Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models. Frontiers in Neuroinformatics 12: 46.
    DOI: 10.3389/fninf.2018.00046

  • Quaglio P., Rostami V., Torre E., Grün S. (2018) Methods for identification of spike patterns in massively parallel spike trains. Biological Cybernetics 112:57-80.
    DOI: 10.1007/s00422-018-0755-0

  • Riehle A., Brochier TG., Nawrot M., Grün S. (2018) Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex. Frontiers Neural Circuits 12:52.
    DOI: 10.3389/fncir.2018.00052

  • Schmidt M., Bakker R., Hilgetag CC., Diesmann M., van Albada SJ. (2018) Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function 223:1409-1435.
    DOI: 10.1007/s00429-017-1554-4.

  • Schmidt M., Bakker R., Shen K., Bezgin G., Diesmann M., van Albada SJ. (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology 14:e1006359.
    DOI: 10.1371/journal.pcbi.1006359

  • Schuecker J., Goedeke S., Helias M. (2018) Optimal Sequence Memory in Driven Random Networks. Physical Review X 8: 041029.
    DOI: 10.1103/PhysRevX.8.041029

  • Senden M., Schuecker J., Hahne J., Diesmann M., Goebel R. (2018) [Re] A neural model of the saccade generator in the reticular formation. ReScience 4:1-12.
    DOI: 10.5281/zenodo.1241004

  • Senk J., Carde C., Hagen E., Kuhlen TW., Diesmann M., Weyers B. (2018) VIOLA - A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output. Frontiers in Neuroinformatics. 12:75.
    DOI: 10.3389/fninf.2018.00075

  • 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. Frontiers in Neuroinformatics 12: 81.
    DOI: 10.3389/fninf.2018.00081

  • Völker M., Fiederer LDJ., Berberich S., Hammer J., Behncke J., Krsek P., Tomasek M., Marusic P., Reinacher PC., Coenen VA., Helias M., Schulze-Bornhage A., Burgard W., Ball T. (2018) The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial EEG. NeuroImage 173:564-579.
    DOI: 10.1016/j.neuroimage.2018.01.059


  • Bahuguna J., Tetzlaff T., Kumar A., Hellgren Kotaleski J., Morrison A. (2017) Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions. Frontiers in Computational Neuroscience 11:79.
    DOI: 10.3389/fncom.2017.00079

  • Bezgin G., Solodkin A., Bakker R., Ritter P., McIntosh AR. (2017) Mapping complementary features of cross-species structural connectivity to construct realistic “Virtual Brains”. Human Brain Mapping 38:2080–2093.
    DOI: 10.1002/hbm.23506.

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  • Ito J., Yamane Y., Suzuki M., Maldonado P., Fujita I., Tamura H., Grün S. (2017) Switch from ambient to focal processing mode explains the dynamics of free viewing eye movements. Scientific Reports 7.
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  • Kass RE., Amari S., Arai K., Brown EN., Diekman CO., Diesmann M., Doiron B., Eden U., Fairhall A., Fiddyment GM., Fukai T., Grün S., Harrison MT., Helias M., Nakahara H., Teramae J., Thomas PJ., Reimers M., Rodu J., Rotstein HG., Shea-Brown E., Shimazaki H., Shinomoto S., Yu BM., Kramer MA. (2017) Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application 5.
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  • Quaglio P., Yegenoglu A., Torre E., Endres DM., Grün S. (2017) Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience 11:41.
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  • Bos H., Diesmann M., Helias M. (2016) Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. PLOS Computational Biology 12:e1005132.
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  • 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.
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  • Grytskyy D., Diesmann M., Helias M. (2016) Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality. Physical Review E 93.
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  • 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.
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  • Maksimov A, van Albada SJ. , Diesmann M. (2016) [Re] Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model. Rescience.

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  • Torre E., Canova C., Denker M., Gerstein G., Helias M., Grün S. (2016) ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains. PLOS Computational Biology 12:e1004939.
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  • Trengove C., Diesmann M., van Leeuwen C. (2016) Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains. Journal of Computational Neuroscience 40:1–26.
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  • Weidel P., Djurfeldt M., Duarte RC., Morrison A. (2016) Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS. Frontiers in Neuroinformatics 10:31.
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  • Wippler D., Wilks RG., Pieters BE., van Albada SJ., Gerlach D., Hüpkes J., Bär M., Rau U. (2016) Pronounced Surface Band Bending of Thin-Film Silicon Revealed by Modeling Core Levels Probed with Hard X-rays. ACS Applied Materials & Interfaces 27(8):17685-17693.
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  • van Albada SJ., Helias M., Diesmann M. (2015) Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. PLOS Computational Biology 11:e1004490.
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  • Duarte R. (2015) Expansion and State-Dependent Variability along Sensory Processing Streams. Journal of Neuroscience 35:7315–7316.
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  • Hahne J., Helias M., Kunkel S., Igarashi J., Bolten M., Frommer A., Diesmann M. (2015) A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics 9:22.
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  • Milekovic T., Truccolo W., Grün S., Riehle A., Brochier T. (2015) Local field potentials in primate motor cortex encode grasp kinetic parameters. NeuroImage 114:338–355.
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  • Schuecker J., Diesmann M., Helias M. (2015) Modulated escape from a metastable state driven by colored noise. Physical Review E 92.
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  • Sergejeva M., Papp EA., Bakker R., Gaudnek MA., Okamura-Oho Y., Boline J., Bjaalie JG., Hess A. (2015) Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates. Journal of Neuroscience Methods 240:161–169.
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  • 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.
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  • Helias M., Tetzlaff T., Diesmann M. (2014) The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics. PLoS Computational Biology 10:e1003428.
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  • Kriener B., Enger H., Tetzlaff T., Plesser HE., Gewaltig MO., Einevoll GT. (2014) Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses. Frontiers in Computational Neuroscience 8:136.
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  • Kriener B., Helias M., Rotter S., Diesmann M., Einevoll GT. (2014) How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime. Frontiers in Computational Neuroscience 7:187.
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  • Kunkel S., Schmidt M., Eppler JM., Plesser HE., Masumoto G., Igarashi J., Ishii S., Fukai T., Morrison A., Diesmann M., Helias M. (2014) Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics 8:78.
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  • Pettersen KH., Lindén H., Tetzlaff T., Einevoll GT. (2014) Power Laws from Linear Neuronal Cable Theory: Power Spectral Densities of the Soma Potential, Soma Membrane Current and Single-Neuron Contribution to the EEG. PLoS Computational Biology 10:e1003928.
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  • Potjans TC., Diesmann M. (2014) The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cerebral Cortex 24:785–806.
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  • Toledo-Suarez C., Duarte R., Morrison A. (2014) Liquid computing on and off the edge of chaos with a striatal microcircuit. Frontiers in Computational Neuroscience 8:130.
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  • Abeles M., Diesmann M., Flash T., Geisel T., Herrmann M., Teicher M. (2013) Compositionality in neural control: an interdisciplinary study of scribbling movements in primates. Frontiers in Computational Neuroscience 7:103.
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  • Heiberg T., Kriener B., Tetzlaff T., Casti A., Einevoll GT., Plesser HE. (2013) Firing-rate models capture essential response dynamics of LGN relay cells. Journal of Computational Neuroscience 35:359–375.
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  • Helias M., Tetzlaff T., Diesmann M. (2013) Echoes in correlated neural systems. New Journal of Physics 15:023002.
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  • Ito J., Maldonado P., Grün S. (2013) Cross-frequency interaction of the eye-movement related LFP signals in V1 of freely viewing monkeys. Frontiers in Systems Neuroscience 7:1.
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  • Kerr CC., van Albada SJ., Neymotin SA., Chadderdon GL., Robinson PA., Lytton WW. (2013) Cortical information flow in Parkinson’s disease: a composite network/field model. Frontiers in Computational Neuroscience 7:39.
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  • Nowke C., Hentschel B., Kuhlen T., Schmidt M., van Albada SJ., Eppler JM., Bakker R., Diesmann M. (2013) VisNEST – interactive analysis of neural activity data. IEEE BioVis 65-72.
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  • Picado-Muiño D., Borgelt C., Berger D., Gerstein G., Grün S. (2013) Finding neural assemblies with frequent item set mining. Frontiers in Neuroinformatics 7:9.
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  • Pipa G., Grün S., van Vreeswijk C. (2013) Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events. Neural Computation 25:1123–1163.
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  • Schultze-Kraft M., Diesmann M., Grün S., Helias M. (2013) Noise Suppression and Surplus Synchrony by Coincidence Detection. PLoS Computational Biology 9:e1002904.
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  • Torre E., Picado-Muiño D., Denker M., Borgelt C., Grün S. (2013) Statistical evaluation of synchronous spike patterns extracted by frequent item set mining. Frontiers in Computational Neuroscience 7:132.
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  • Trengove C., van Leeuwen C., Diesmann M. (2013) High-capacity embedding of synfire chains in a cortical network model. Journal of Computational Neuroscience 34:185–209.
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  • Vlachos A., Helias M., Becker D., Diesmann M., Deller T. (2013) NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro. Neurobiology of Disease 59:267–276.
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  • Yousaf M., Wyller J., Tetzlaff T., Einevoll GT. (2013) Effect of localized input on bump solutions in a two-population neural-field model. Nonlinear Analysis: Real World Applications 14:997–1025.
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  • Bakker R., Wachtler T., Diesmann M. (2012) CoCoMac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics 6:30.
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  • Berger D., Pazienti A., Flores FJ., Nawrot MP., Maldonado PE., Grün S. (2012) Viewing strategy of Cebus monkeys during free exploration of natural images. Brain Research 1434:34–46.
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  • Bezgin G., Vakorin VA., van Opstal AJ., McIntosh AR., Bakker R. (2012) Hundreds of brain maps in one atlas: registering coordinate-independent primate neuro-anatomical data to a standard brain. NeuroImage 62:67–76.
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  • Crook SM., Bednar JA., Berger S., Cannon R., Davison AP., Djurfeldt M., Eppler J., Kriener B., Furber S., Graham B., Plesser HE., Schwabe L., Smith L., Steuber V., van Albada SJ. (2012) Creating, documenting and sharing network models. Network: Computation in Neural Systems 23:131–149.
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  • Deger M., Helias M., Rotter S., Diesmann M. (2012) Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex. PLoS Computational Biology 8:e1002689.
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  • 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.
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  • Helias M., Kunkel S., Masumoto G., Igarashi J., Eppler JM., Ishii S., Fukai T., Morrison A., Diesmann M. (2012) Supercomputers ready for use as discovery machines for neuroscience. Frontiers in Neuroinformatics 6:26.
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  • Kunkel S., Potjans TC., Eppler JM., Plesser HE., Morrison A., Diesmann M. (2012) Meeting the memory challenges of brain-scale network simulation. Frontiers in Neuroinformatics 5:35.
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  • Lansner A., Diesmann M. (2012) Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers Computational Systems Biology Dordrecht : Springer Netherlands 283-315

  • Pfeil T., Potjans TC., Schrader S., Potjans W., Schemmel J., Diesmann M., Meier K. (2012) Is a 4-bit synaptic weight resolution enough? – constraints on enabling spike-timing dependent plasticity in neuromorphic hardware. Frontiers in Neuroscience 6:90.
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  • Shimazaki H., Amari S., Brown EN., Grün S. (2012) State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data. PLoS Computational Biology 8:e1002385.
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  • Denker M., Roux S., Lindén H., Diesmann M., Riehle A., Grün S. (2011) The Local Field Potential Reflects Surplus Spike Synchrony. Cerebral Cortex 21:2681–2695.
    DOI: 10.1093/cercor/bhr040.

  • Hanuschkin A., Diesmann M., Morrison A. (2011) A reafferent and feed-forward model of song syntax generation in the Bengalese finch. Journal of Computational Neuroscience 31:509–532.
    DOI: 10.1007/s10827-011-0318-z.

  • Hanuschkin A., Herrmann JM., Morrison A., Diesmann M. (2011) Compositionality of arm movements can be realized by propagating synchrony. Journal of Computational Neuroscience 30:675–697.
    DOI: 10.1007/s10827-010-0285-9.

  • Helias M., Deger M., Rotter S., Diesmann M. (2011) Finite post synaptic potentials cause a fast neuronal response. Frontiers in Neuroscience 5:19.
    DOI: 10.3389/fnins.2011.00019.

  • Ishii S., Diesmann M., Doya K. (2011) Multi-scale, multi-modal neural modeling and simulation. Neural Networks 24:917.
    DOI: 10.1016/j.neunet.2011.07.004.

  • Ito J., Maldonado P., Singer W., Grün S. (2011) Saccade-Related Modulations of Neuronal Excitability Support Synchrony of Visually Elicited Spikes. Cerebral Cortex 21:2482–2497.
    DOI: 10.1093/cercor/bhr020.

  • von Kapri A., Rick T., Potjans TC., Diesmann M., Kuhlen T. (2011) Towards the Visualization of Spiking Neurons in Virtual Reality. Studies in Health Technology and Informatics: 685–687.
    DOI: 10.3233/978-1-60750-706-2-685.

  • 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.

  • Potjans W., Diesmann M., Morrison A. (2011) An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning. PLoS Computational Biology 7:e1001133.
    DOI: 10.1371/journal.pcbi.1001133.

  • Schrader S., Diesmann M., Morrison A. (2011) A compositionality machine realized by a hierarchic architecture of synfire chains. Frontiers in Computational Neuroscience 4:154.
    DOI: 10.3389/fncom.2010.00154.

  • Wagatsuma N., Potjans TC., Diesmann M., Fukai T. (2011) Layer-dependent attentional processing by top-down signals in a visual cortical microcircuit model. Frontiers in Computational Neuroscience 5:31.
    DOI: 10.3389/fncom.2011.00031.


  • Berger D., Borgelt C., Louis S., Morrison A., Grün S. (2010) Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains. Computational Intelligence and Neuroscience 2010:1–18.
    DOI: 10.1155/2010/439648.

  • Deger M., Helias M., Cardanobile S., Atay FM., Rotter S. (2010) Nonequilibrium dynamics of stochastic point processes with refractoriness. Physical Review E 82.
    DOI: 10.1103/PhysRevE.82.021129.

  • Denker M., Finke R., Schaupp F., Grün S., Menzel R. (2010) Neural correlates of odor learning in the honeybee antennal lobe. European Journal of Neuroscience 31:119–133.
    DOI: 10.1111/j.1460-9568.2009.07046.x.

  • Denker M., Riehle A., Diesmann M., Grün S. (2010) Estimating the contribution of assembly activity to cortical dynamics from spike and population measures. Journal of Computational Neuroscience 29:599–613.
    DOI: 10.1007/s10827-010-0241-8.

  • Djurfeldt M., Hjorth J., Eppler JM., Dudani N., Helias M., Potjans TC., Bhalla US., Diesmann M., Hellgren Kotaleski J., Ekeberg Ö. (2010) Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework. Neuroinformatics 8:43–60.
    DOI: 10.1007/s12021-010-9064-z.

  • Hanuschkin A., Kunkel S., Helias M., Morrison A., Diesmann M. (2010) A general and efficient method for incorporating precise spike times in globally time-driven simulations. Frontiers in Neuroinformatics 4:113.
    DOI: 10.3389/fninf.2010.00113.

  • Helias M., Deger M., Diesmann M., Rotter S. (2010) Equilibrium and response properties of the integrate-and-fire neuron in discrete time. Frontiers in Computational Neuroscience 3:29.
    DOI: 10.3389/neuro.10.029.2009.

  • Helias M., Deger M., Rotter S., Diesmann M. (2010) Instantaneous Non-Linear Processing by Pulse-Coupled Threshold Units. PLoS Computational Biology 6:e1000929.
    DOI: 10.1371/journal.pcbi.1000929.

  • Kilavik BE., Confais J., Ponce-Alvarez A., Diesmann M., Riehle A. (2010) Evoked Potentials in Motor Cortical Local Field Potentials Reflect Task Timing and Behavioral Performance. Journal of Neurophysiology 104:2338–2351.
    DOI: 10.1152/jn.00250.2010.

  • Kunkel S., Diesmann M., Morrison A. (2010) Limits to the development of feed-forward structures in large recurrent neuronal networks. Frontiers in Computational Neuroscience 4:160.
    DOI: 10.3389/fncom.2010.00160.

  • Louis S., Borgelt C., Grün S. (2010) Complexity distribution as a measure for assembly size and temporal precision. Neural Networks 23:705–712.
    DOI: 10.1016/j.neunet.2010.05.004.

  • Louis SG., Gerstein GL., Grün S., Diesmann M. (2010) Surrogate spike train generation through dithering in operational time. Frontiers in Computational Neuroscience 4:127.
    DOI: 10.3389/fncom.2010.00127.

  • Nordlie E., Plesser HE. (2010) Visualizing neuronal network connectivity with connectivity pattern tables. Frontiers in Neuroinformatics 3:39.
    DOI: 10.3389/neuro.11.039.2009.

  • Potjans W., Morrison A., Diesmann M. (2010) Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Frontiers in Computational Neuroscience 4:141.
    DOI: 10.3389/fncom.2010.00141.

  • Staude B., Rotter S. (2010) Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference. Frontiers in Computational Neuroscience 4:16.
    DOI: 10.3389/fncom.2010.00016.

  • Staude B., Rotter S., Grün S. (2010) CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains. Journal of Computational Neuroscience 29:327–350.
    DOI: 10.1007/s10827-009-0195-x.


  • Grün S. (2009) Data-Driven Significance Estimation for Precise Spike Correlation. Journal of Neurophysiology 101:1126–1140.
    DOI: 10.1152/jn.00093.2008.

  • Kilavik BE., Roux S., Ponce-Alvarez A., Confais J., Grün S., Riehle A. (2009) Long-Term Modifications in Motor Cortical Dynamics Induced by Intensive Practice. Journal of Neuroscience 29:12653–12663.
    DOI: 10.1523/JNEUROSCI.1554-09.2009.

  • Kriener B., Helias M., Aertsen A., Rotter S. (2009) Correlations in spiking neuronal networks with distance dependent connections. Journal of Computational Neuroscience 27:177–200.
    DOI: 10.1007/s10827-008-0135-1.

  • Norlie E., Gewaltig MO., Plesser HE. (2009) Towards Reproducible Descriptions of Neuronal Network Models. PLoS Comput Biol 5(8): e1000456.
    DOI: 10.1371/journal.pcbi.1000456

  • Plesser HE., Diesmann M. (2009) Simplicity and Efficiency of Integrate-and-Fire Neuron Models. Neural Computation 21:353–359.
    DOI: 10.1162/neco.2008.03-08-731.

  • Potjans W., Morrison A., Diesmann M. (2009) A Spiking Neural Network Model of an Actor-Critic Learning Agent. Neural Computation 21:301–339.
    DOI: 10.1162/neco.2008.08-07-593.

  • Sharott A., Moll CKE., Engler G., Denker M., Grün S., Engel AK. (2009) Different Subtypes of Striatal Neurons Are Selectively Modulated by Cortical Oscillations. Journal of Neuroscience 29:4571–4585.
    DOI: 10.1523/JNEUROSCI.5097-08.2009.


  • Clemens M., Helias M., Steinmetz T., Wimmer G. (2008) Multiple right-hand side techniques for the numerical simulation of quasistatic electric and magnetic fields. Journal of Computational and Applied Mathematics 215:328–338.
    DOI: 10.1016/j.cam2006.04.072

  • Eppler JM., Helias M., Muller E., Diesmann M., Gewaltig MO. (2008) PyNEST: a convenient interface to the NEST simulator. Frontiers in Neuroinformatics 2:12.
    DOI: 10.3389/neuro.11.012.2008.

  • Goedeke S., Diesmann M. (2008) The mechanism of synchronization in feed-forward neuronal networks. New Journal of Physics 10:015007.
    DOI: 10.1088/1367-2630/10/1/015007.

  • Helias M., Rotter S., Gewaltig MO., Diesmann M. (2008) Structural plasticity controlled by calcium based correlation detection. Frontiers in Computational Neuroscience 2:7.
    DOI: 10.3389/neuro.10.007.2008.

  • Kriener B., Tetzlaff T., Aertsen A., Diesmann M., Rotter S. (2008) Correlations and Population Dynamics in Cortical Networks. Neural Computation 20:2185–2226.
    DOI: 10.1162/neco.2008.02-07-474.

  • Maldonado P., Babul C., Singer W., Rodriguez E., Berger D., Grün S. (2008) Synchronization of Neuronal Responses in Primary Visual Cortex of Monkeys Viewing Natural Images. Journal of Neurophysiology 100:1523–1532.
    DOI: 10.1152/jn.00076.2008.

  • Morrison A., Diesmann M., Gerstner W. (2008) Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics 98:459–478.
    DOI: 10.1007/s00422-008-0233-1.

  • Pazienti A., Maldonado PE., Diesmann M., Grün S. (2008) Effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Research 1225:39–46.
    DOI: 10.1016/j.brainres.2008.04.073.

  • 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.

  • Staude B., Rotter S., Grün S. (2008) Can Spike Coordination Be Differentiated from Rate Covariation? Neural Computation 20:1973–1999.
    DOI: 10.1162/neco.2008.06-07-550.

  • Tetzlaff T., Rotter S., Stark E., Abeles M., Aertsen A., Diesmann M. (2008) Dependence of Neuronal Correlations on Filter Characteristics and Marginal Spike Train Statistics. Neural Computation 20:2133–2184.
    DOI: 10.1162/neco.2008.05-07-525.


  • Berger D., Warren D., Normann R., Arieli A., Grün S. (2007) Spatially organized spike correlation in cat visual cortex. Neurocomputing 70:2112–2116.
    DOI: 10.1016/j.neucom.2006.10.141.

  • Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower JM., Diesmann M., Morrison A., Goodman PH., Harris FC Jr., Zirpe M., Natschläger T., Pecevski D., Ermentrout B., Djurfeldt M., Lasner A., Rochel O., Vieville T., Muller E., Davison AP., El Boustani S., Destexhe A. (2007) Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience 23(3): 349-398.
    DOI: 10.1007/s10827-007-0038-6

  • Denker M., Roux S., Timme M., Riehle A., Grün S. (2007) Phase synchronization between LFP and spiking activity in motor cortex during movement preparation. Neurocomputing 70:2096–2101.
    DOI: 10.1016/j.neucom.2006.10.088.

  • Morrison A., Aertsen A., Diesmann M. (2007) Spike-Timing-Dependent Plasticity in Balanced Random Networks. Neural Computation 19:1437–1467.
    DOI: 10.1162/neco.2007.19.6.1437.

  • Morrison A., Straube S., Plesser HE., Diesmann M. (2007) Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations. Neural Computation 19:47–79.
    DOI: 10.1162/neco.2007.19.1.47.

  • Nawrot MP., Boucsein C., Rodriguez-Molina V., Aertsen A., Grün S., Rotter S. (2007) Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro. Neurocomputing 70:1717–1722.
    DOI: 10.1016/j.neucom.2006.10.101.

  • Pipa G., Riehle A., Grün S. (2007) Validation of task-related excess of spike coincidences based on NeuroXidence. Neurocomputing 70:2064–2068.
    DOI: 10.1016/j.neucom.2006.10.142.

  • Plesser HE., Eppler JM., Morrison A., Diesmann M., Gewaltig MO. (2007) Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers. Euro-Par 2007, Proceedings of the 13th International Euro-Par Conference, LCNS Springer 4641: 672-681.
    DOI: 10.1007/978-3-540-74466-5_71


  • Backofen R., Borrmann HG., Deck W., Dedner A., Raedt LD., Desch K., Diesmann M., Geier M., Greiner A., Hess WR., Honerkamp J., Jankowski S., Krossing I., Liehr AW., Karwath A., Klöfkorn R., Pesché R., Potjans T., Röttger MC., Schmidt-Thieme L., Schneider G., Voß B., Wiebelt B., Wienemann P., Winterer VH. (2006) A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative. PIK - Praxis der Informationsverarbeitung und Kommunikation 29:81–87.
    DOI: 10.1515/PIKO.2006.81.

  • Guerrero-Rivera R., Morrison A., Diesmann M., Pearce TC. (2006) Programmable Logic Construction Kits for Hyper-Real-Time Neuronal Modeling. Neural Computation 18:2651–2679.
    DOI: 10.1162/neco.2006.18.11.2651.

  • Pazienti A., Grün S. (2006) Robustness of the significance of spike synchrony with respect to sorting errors. Journal of Computational Neuroscience 21:329–342.
    DOI: 10.1007/s10827-006-8899-7.

  • Steinmetz T., Helias M., Wimmer G., Fichte LO., Clemens M. (2006) Electro-quasistatic field simulations based on a discrete electromagnetism formulation. IEEE Transactions on Magnetics 42:755–758.
    DOI: 10.1109/TMAG.2006.872488.


  • Czanner G., Grün S., Iyengar S. (2005) Theory of the Snowflake Plot and Its Relations to Higher-Order Analysis Methods. Neural Computation 17:1456–1479.
    DOI: 10.1162/0899766053723041.

  • Morrison A., Mehring C., Geisel T., Aertsen A., Diesmann M. (2005) Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing. Neural Computation 17:1776–1801.
    DOI: 10.1162/0899766054026648.


  • Denker M., Timme M., Diesmann M., Wolf F., Geisel T. (2004) Breaking Synchrony by Heterogeneity in Complex Networks. Physical Review Letters 92.
    DOI: 10.1103/PhysRevLett.92.074103.

  • Helias M., Pfannkuche D. (2004) Tunneling of quasiholes in the fractional quantum Hall regime. Diploma Thesis.
    DOI: 10.48550/arXiv.cond-mat/0403126

  • Tetzlaff T., Morrison A., Geisel T., Diesmann M. (2004) Consequences of realistic network size on the stability of embedded synfire chains. Neurocomputing 58–60:117–121.
    DOI: 10.1016/j.neucom.2004.01.031.


  • Gál V., Grün S., Tetzlaff R. (2003) ANALYSIS OF MULTIDIMENSIONAL NEURAL ACTIVITY VIA CNN-UM. International Journal of Neural Systems 13:479–487.
    DOI: 10.1142/S0129065703001789.

  • Grün S., Riehle A., Aertsen A., Diesmann M. (2003) Temporal scales of cortical interactions. Nova Acta Leopoldina NF 88:189–206.
    Link: pdf auf Researchgate

  • Grün S., Riehle A., Diesmann M. (2003) Effect of cross-trial nonstationarity on joint-spike events. Biological Cybernetics 88:335–351.
    DOI: 10.1007/s00422-002-0386-2.

  • Mehring C., Hehl U., Kubo M., Diesmann M., Aertsen A. (2003) Activity dynamics and propagation of synchronous spiking in locally connected random networks. Biological Cybernetics 88:395–408.
    DOI: 10.1007/s00422-002-0384-4.

  • Pipa G., Diesmann M., Grün S. (2003) Significance of joint-spike events based on trial-shuffling by efficient combinatorial methods. Complexity 8:79–86.
    DOI: 10.1002/cplx.10085.

  • Pipa G., Grün S. (2003) Non-parametric significance estimation of joint-spike events by shuffling and resampling. Neurocomputing 52–54:31–37.
    DOI: 10.1016/S0925-2312(02)00823-8.

  • Schneider G., Grün S. (2003) Analysis of higher-order correlations in multiple parallel processes. Neurocomputing 52–54:771–777.
    DOI: 10.1016/S0925-2312(02)00772-5.

  • Tetzlaff T., Buschermöhle M., Geisel T., Diesmann M. (2003) The spread of rate and correlation in stationary cortical networks. Neurocomputing 52–54:949–954.
    DOI: 10.1016/S0925-2312(02)00854-8.


  • Egert U., Knott T., Schwarz C., Nawrot M., Brandt A., Rotter S., Diesmann M. (2002) MEA-Tools: an open source toolbox for the analysis of multi-electrode data with MATLAB. Journal of Neuroscience Methods 117:33–42.
    DOI: 10.1016/S0165-0270(02)00045-6.

  • Grün S., Diesmann M., Aertsen A. (2002) Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance. Neural Computation 14:43–80.
    DOI: 10.1162/089976602753284455.

  • Grün S., Diesmann M., Aertsen A. (2002) Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data. Neural Computation 14:81–119.
    DOI: 10.1162/089976602753284464.

  • Tetzlaff T., Geisel T., Diesmann M. (2002) The ground state of cortical feed-forward networks. Neurocomputing 44–46:673–678.
    DOI: 10.1016/S0925-2312(02)00456-3.


  • Diesmann M., Gewaltig MO., Rotter S., Aertsen A. (2001) State space analysis of synchronous spiking in cortical neural networks. Neurocomputing 38–40:565–571.
    DOI: 10.1016/S0925-2312(01)00409-X.

  • Gewaltig MO., Diesmann M., Aertsen A. (2001) Propagation of cortical synfire activity: survival probability in single trials and stability in the mean. Neural Networks 14:657–673.
    DOI: 10.1016/S0893-6080(01)00070-3.

  • Gewaltig MO., Diesmann M., Aertsen A. (2001) Cortical synfire-activity: configuration space and survival probability. Neurocomputing 38–40:621–626.
    DOI: 10.1016/S0925-2312(01)00454-4.


  • Riehle A., Grammont F., Diesmann M., Grün S. (2000) Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation. Journal of Physiology-Paris 94:569–582.
    DOI: 10.1016/S0928-4257(00)01100-1.


  • Diesmann M., Gewaltig MO., Aertsen A. (1999) Stable propagation of synchronous spiking in cortical neural networks. Nature 402:529–533.
    DOI: 10.1038/990101.

  • Grün S., Diesmann M., Grammont F., Riehle A., Aertsen A. (1999) Detecting unitary events without discretization of time. Journal of Neuroscience Methods 94:67–79.
    DOI: 10.1016/S0165-0270(99)00126-0.

  • Rotter S., Diesmann M. (1999) Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biological Cybernetics 81:381–402.
    DOI: 10.1007/s004220050570.


  • Riehle A., Grün S., Diesmann M., Aertsen A. (1997) Spike Synchronization and Rate Modulation Differentially Involved in Motor Cortical Function. Science 278:1950–1953.
    DOI: 10.1126/science.278.5345.1950.


  • Aertsen A., Diesmann M., Gewaltig M. (1996) Propagation of synchronous spiking activity in feedforward neural networks. Journal of Physiology-Paris 90:243–247.
    DOI: 10.1016/S0928-4257(97)81432-5.


  • Martignon L., Von Hassein H., Grün S., Aertsen A., Palm G. (1995) Detecting higher-order interactions among the spiking events in a group of neurons. Biological Cybernetics 73:69–81.
    DOI: 10.1007/BF00199057.

Publication list 2021

Publication list 2020

Last Modified: 12.09.2022