Peer-reviewed publications

2024

Amunts K., Axer M., Banerjee S., Bitsch L., Bjaalie JG., Brauner P., Brovelli A., Calarco N., Carrere M., Caspers S., Charvet CJ., Cichon S., Cools R., Costantini I., D’Angelo EU., De Bonis G., Deco G., DeFelipe J., Destexhe A., Dickscheid T., Diesmann M., Düzel E., Eickhoff SB., Einevoll G., Eke D., Engel AK., Evans AC., Evers K., Fedorchenko N., Forkel SJ., Fousek J., Friederici AD., Friston K., Furber S., Geris L., Goebel R., Güntürkün O., Hamid AIA., Herold C., Hilgetag CC., Hölter SM., Ioannidis Y., Jirsa V., Kashyap S., Kasper BS., d’Exaerde AdK., Kooijmans R., Koren I., Kotaleski JH., Kiar G., Klijn W., Klüver L., Knoll AC., Krsnik Z., Kämpfer J., Larkum ME., Linne M-L., Lippert T., Abdullah JM., Maio PD., Magielse N., Maquet P., Mascaro ALA., Marinazzo D., Mejias J., Meyer-Lindenberg A., Migliore M., Michael J., Morel Y., Morin FO., Muckli L., Nagels G., Oden L., Palomero-Gallagher N., Panagiotaropoulos F., Paolucci PS., Pennartz C., Peeters LM., Petkoski S., Petkov N., Petro LS., Petrovici MA., Pezzulo G., Roelfsema P., Ris L., Ritter P., Rockland K., Rotter S., Rowald A., Ruland S., Ryvlin P., Salles A., Sanchez-Vives MV., Schemmel J., Senn W., de Sousa AA., Ströckens F., Thirion B., Uludağ K., Vanni S., van Albada SJ., Vanduffel W., Vezoli J., Vincenz-Donnelly L., Walter F., Zaborszky L. (2024) The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing. Imaging Neuroscience DOI: 10.1162/imag_a_00137
Farisco M., Baldassarre G., Cartoni E., Leach A., Petrovici M. A., Rosemann A., Salles A., Stahl B., van Albada, S. J. (2024) A method for the ethical analysis of brain-inspired AI. Artificial Intelligence Review 57(6), 1-27
DOI: 10.1007/s10462-024-10769-4
Gutzen R., De Bonis G., De Luca C., Pastorelli E., Capone C., Mascaro ALA., Resta F., Manasanch A., Pavone FS., Sanchez-Vives MV., Mattia M., Grün S., Paolucci PS., Denker M. (2024) A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets. Cell Reports Methods 4, 100681
DOI: 10.1016/j.crmeth.2023.100681
Hocquet A., Wieber F., Gramelsberger G., Hinsen K., Diesmann M., Pasquini Santos F., Landström C., Peters B., Kasprowicz D., Borrelli A., Roth P., Lee CAL., Olteanu A. & Böschen S. (2024) Software in science is ubiquitous yet overlooked. Nat Comput Sci. https://doi.org/10.1038/s43588-024-00651-2
Köhler CA., Ulianych D., Grün S., Decker S., Denker M. (2024) Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture. eneuro 11(6)
DOI: 10.1523/ENEURO.0476-23.2024
Kusch L, Diaz-Pier S, Klijn W, Sontheimer K, Bernard C, Morrison A and Jirsa V (2024) Multiscale co-simulation design pattern for neuroscience applications. Front. Neuroinform. 18:1156683. doi: 10.3389/fninf.2024.1156683
Layer M., Helias M. and Dahmen D. (2024) Effect of Synaptic Heterogeneity on Neuronal Coordination PRX Life 2, 013013 DOI: 10.1103/PRXLife.2.013013
Morales-Gregorio A., Kurth AC., Ito J., Kleinjohann A., Barthélemy FV., Brochier T., Grün S., van Albada SJ. (2024) Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region. Cell Reports 43(7), 114371. DOI: 10.1016/j.celrep.2024.114371
Senden M., van Albada S. J., Pezzulo G., Falotico E., Hashim I., Kroner A., Kurth A.C., Lanillos P., Narayanan V., Pennartz C., Petrovici M.A., Steffen L., Weidler T., Goebel R. (2024) Modular-integrative modeling: a new framework for building brain models that blend biological realism and functional performance. National Science Review 11(5),
DOI: nwad318 10.1093/nsr/nwad318

2023

Aimone JB., Awile O., Diesmann M., Knight JC., Nowotny T. and Schürmann F. (2023) Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute. Front. Neuroinform. 17:1157418. DOI: 10.3389/fninf.2023.1157418
Bouhadjar Y., Siegel S., Tetzlaff T., Diesmann M, Waser R., Wouters DJ. (2023) Sequence learning in a spiking neuronal network with memristive synapses Neuromorphic Computing and Engineering 3(3):034014
DOI: 10.1088/2634-4386/acf1c4
Bouhadjar Y., Wouters DJ., Diesmann M., Tetzlaff T. (2023) Coherent noise enables probabilistic sequence replay in spiking neuronal networks PLoS Computational Biology 19(5): e1010989.
DOI: 10.1371/journal.pcbi.1010989

Capone C., De Luca C., De Bonis G., Gutzen R., Bernava I., Pastorelli E., Simula F., Lupo C., Tonielli L., Resta F., Allegra Mascaro AL., Pavone F., Denker M., Paolucci PS. (2023) Simulations approaching data: Cortical slow waves in inferred models of the whole hemisphere of mouse. Communications Biology 6(266).
DOI: 10.1038/s42003-023-04580-0
Golosio B., Villamar J., Tiddia G., Pastorelli E., Stapmanns J., Fanti V., Paolucci PS., Morrison A., Senk J. (2023) Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices. Applied Sciences 13(17):9598.
DOI: 10.3390/app13179598
Gutzen R., Grün S., Denker M. (2023) Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles. BioSystems 223:104813.
DOI: 10.1016/j.biosystems.2022.104813
Meirhaeghe N., Riehle A., Brochier T. (2023) Parallel movement planning is achieved via an optimal preparatory state in motor cortex. Cell Reports, Volume 42, 2:112136 DOI: 10.1016/j.celrep.2023.112136
Merger C., René A., Fischer K., Bouss P., NestlerS., Dahmen, D., Honerkamp C., Helias M. (2023) Learning Interacting Theories from Data. Phys. Rev. X 13, 041033. DOI: 10.1103/PhysRevX.13.041033
Morales-Gregorio A., van Meegen A., van Albada SJ. (2023) Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex. Cereb Cortex 33(16):9439-9449. DOI: 10.1093/cercor/bhad160
Nestler, S., Helias, M. and Gilson, M. (2023) Statistical temporal pattern extraction by neuronal architecture. Phys. Rev. Res. 5, 033177. DOI: 10.1103/PhysRevResearch.5.033177
Klinger CM., Denker M., Grün S., Hanke M., Oelltze-Jafra S., Ohl FW., Radny J., Rotter S., Scherberger H., Stein A., Wachtler T., Witte OW., Ritter P. (2023) Research Data Management and Data Sharing for Reproducible Research - Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience. eNeuro 10(2).
DOI: 10.1523/ENEURO.0215-22.2023
Quercia A., Morrison A., Scharr H., Assent I. (2023) SGD Biased towards Early Important Samples for Efficient Training. IEEE International Conference on Data Mining (ICDM), Shanghai, China, 2023, 1289-1294, DOI: 10.1109/ICDM58522.2023.00163
Schulte to Brinke T., Dick M., Duarte R., Morrison A. (2023) A refined information processing capacity metric allows an in-depth analysis of memory and nonlinearity trade-offs in neurocomputational systems. Scientific Reports 2023 Jun 29;13(1):10517.
DOI: 10.1038/s41598-023-37604-0
Siegel S., Bouhadjar Y., Tetzlaff T., Waser R., Dittmann R., Wouters D. (2023) System model of neuromorphic sequence learning on a memristive crossbar array. Neuromorphic Computing and Engineering 3(2):024002.
DOI: 10.1088/2634-4386/acca45
Timonidis N., Bakker R., Rubio-Teves M., Alonso-Martínez C., Garcia-Amado M., Clascá F., Tiesinga PHE. (2023) Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus. Front. Neuroinform. 17:1272243. DOI: 10.3389/fninf.2023.1272243
Wybo WAM., Tsai MC., Tran VAK., Illing B., Jordan J., Morrison A., Senn W. (2023) NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways. Proceedings of the National Academy of Sciences 120(32):e2300558120.
DOI: 10.1073/pnas.2300558120
Yamane Y., Ito J., Joana C., Fujita I., Tamura H., Maldonado PE., Doya K., Grün S. (2023) Neuronal Population Activity in Macaque Visual Cortices Dynamically Changes through Repeated Fixations in Active Free Viewing. eNeuro 10(10):ENEURO.0086-23.2023.
DOI: 10.1523/ENEURO.0086-23.2023
Zajzon B., Dahmen D., Morrison A., Duarte R. (2023) Signal denoising through topographic modularity of neural circuits. eLife 12:e77009.
DOI: 10.7554/eLife.77009
Zajzon B., Duarte R., Morrison A. (2023) Towards reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning. Frontiers in Integrative Neuroscience 17:935177.
DOI: 10.3389/fnint.2023.935177

2022

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. Frontiers in Neuroinformatics 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 Computational Biology 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 13.
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 9:77.
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 11:e68422.
DOI: 10.7554/eLife.68422
De Filippi E., Escrichs, A., Càmara E., Garrido C., Marins T., Sánchez-Fibla M., Gilson M., Deco G. (2022) Meditation-induced effects on whole-brain structural and effective connectivity. Brain Structure & Function 227(6):2087-2102.
DOI: 10.1007/s00429-022-02496-9
Feldotto B., Eppler JM., Jimenez-Romero C., Bignamini C., Gutierrez CE., Albanese U., Retamino E., Vorobev V., Zolfaghari V., Upton A., Sun Z., Yamaura H., Heidarinejad M., Klijn W., Morrison A., Cruz F., McMurtrie C., Knoll AC., Igarashi J., Yamazaki T., Doya K., Morin FO. (2022) Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure. Frontiers in Neuroinformatics 16:884180.
DOI: 10.3389/fninf.2022.884180
Fischer K., René A., Keup C., Layer M., Dahmen D., Helias M. (2022) Decomposing neural networks as mappings of correlation functions. Physical Review Research 4(4):043143.
DOI: 10.1103/PhysRevResearch.4.043143
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 110(12):1894-1988.
DOI: 10.1016/j.neuron.2022.05.18
Hagen E., Magnusson SH., Ness TV., Halnes G., Babu PN., Linssen C., Morrison A., Einevoll GT. (2022) Brain signal predictions from multi-scale networks using a linearized framework. PLoS Computational Biology 18(8):e1010353.
DOI: 10.1371/journal.pcbi.1010353
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. Frontiers in Neuroscience 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. Frontiers in Neuroinformatics 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. Scientific Reports 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. Frontiers in Neuroscience 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 Computational Biology 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. Neuromorphic Computing and Engineering 2:021001.
DOI: 10.1088/2634-4386/ac55fc
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. Frontiers in Neuroinformatics 16:835657.
DOI: 10.3389/fninf.2022.835657
Nowotny T., van Albada S., Fellous J.-M., Haas J. S., Jolivet R. B., Metzner C., Sharpee T. (2022) Editorial: Advances in Computational Neuroscience. Frontiers in Computational Neuroscience 15:824899.
DOI: 10.3389/fncom.2021.824899
Öberländer J., Bouhadjar Y., Morrison A. (2022) Learning and replaying spatiotemporal sequences: A replication study. Frontiers in Integrative Neuroscience 16:974177.
DOI: 10.3389/fnint.2022.974177
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. Frontiers in Neuroinformatics 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
Schulte to Brinke T., Duarte R. Morrison A. (2022) Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model. Frontiers in Integrative Neurosciences 16:923468.
DOI: 10.3389/fnint.2022.923468
Segadlo K., Epping B., van Meegen A., Dahmen D., Krämer M., Helias M. (2022) Unified field theoretical approach to deep and recurrent neuronal networks. Journal of Statistical Mechanics 103401.
DOI: 10.1088/1742-5468/ac8e57
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 Computational Biology 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 9(3):0505-21.
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. Physical Review Letters 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. Frontiers in Neuroinformatics 16:883333.
DOI: 10.3389/fninf.2022.883333
Trensch G., Morrison A. (2022) A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks. Frontiers in Neuroinformatics 16:884033.
DOI: 10.3389/fninf.2022.884033
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. Frontiers in Network Physiology 2:826345.
DOI: 10.3389/fnetp.2022.826345
Yegenoglu A., Subramoney A., Hater T., Jimenez-Romero C., Klijn W., Pérez Martín A., van der Vlag M., Herty M., Morrison A., Diaz S. (2022) Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. Frontiers in Computational Neuroscience 16:885207.
DOI: 10.3389/fncom.2022.885207

2021

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 2(3):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. Frontiers in Neuroscience 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 27(1):27-34.
DOI: 10.1515/nf-2020-0041
Keup C., Kühn T., Dahmen D. and Helias M. (2021) Transient Chaotic Dimensionality Expansion by Recurrent Networks. Physical Review 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. Frontiers in Neuroinformatics. 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 8(6):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. Frontiers in Neuroinformatics 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 Frontiers in Computational Neuroscience15:543872
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. Neuroforum 27(1):3-15.
DOI: 10.1515/nf-2020-0036

2020

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):e1007790.
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 Computational Biololgy 16(10):e1008127.
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
Jordan J., Helias M., Diesmann M., Kunkel S. (2020) Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Frontiers in Neuroinformatics 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. 34^th Conference on Neural Information Processing Systems, NeurIPS 2020, online 1.
http://hdl.handle.net/2128/26881
René A., Longtin A., Macke JH. (2020) Inference of a Mesoscopic Population Model from Population Spike Trains. Neural Computation 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. Physical Review 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. Physical Review E 101:042124.
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. Philosophical Transactions of the Royal Society B, Biological Sciences 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

2019

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 den Helmholtz-Zentren. Helmholtz Open Science Office 16.
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 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.
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. Scientific Reports 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 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. Frontiers in Computational Neuroscience 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(36):7034-7036.
DOI: 10.1523/JNEUROSCI.0656-19.2019

2018

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 51(4):50-59.
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 https://web.gin.g-node.org/INT/multielectrode_grasp
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 in 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

2017

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.
Duarte R., Seeholzer A., Zilles K., Morrison A. (2017) Synaptic patterning and the timescales of cortical dynamics. Current Opinion in Neurobiology 43:156–165.
DOI: 10.1016/j.conb.2017.02.007.
Hahne J., Dahmen D., Schuecker J., Frommer A., Bolten M., Helias M., Diesmann M. (2017) Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator. Frontiers in Neuroinformatics 11:34.
DOI: 10.3389/fninf.2017.00034.
Heers M., Helias M., Hedrich T., Dümpelmann M., Schulze-Bonhage A., Ball T. (2017) 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
Hinne M., Meijers A., Bakker R., Tiesinga PHE., Mørup M., van Gerven MAJ. (2017) The missing link: Predicting connectomes from noisy and partially observed tract tracing data. PLoS Computational Biology 13:e1005374.
DOI: 10.1371/journal.pcbi.1005374.
Hohenfeld C., Nellessen N., Dogan I., Kuhn H., Müller C., Papa F., Ketteler S., Goebel R., Heinecke A., Shah NJ., Schulz JB., Reske M., Reetz K. (2017) Cognitive Improvement and Brain Changes after Real-Time Functional MRI Neurofeedback Training in Healthy Elderly and Prodromal Alzheimer’s Disease Frontiers in neurology 8.
DOI: 10.3389/fneur.2017.00384
Ippen T., Eppler JM., Plesser HE., Diesmann M. (2017) Constructing Neuronal Network Models in Massively Parallel Environments. Frontiers in Neuroinformatics 11:30.
DOI: 10.3389/fninf.2017.00030.
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 7:1082.
DOI: 10.1038/s41598-017-01076-w.
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 5:183-214.
DOI: 10.1146/annurev-statistics-041715-033733.
Kühn T., Helias M. (2017) Locking of correlated neural activity to ongoing oscillations. PLoS Computational Biology 13:e1005534.
DOI: 10.1371/journal.pcbi.1005534.
Müller EJ., van Albada SJ., Kim JW., Robinson PA. (2017) Unified neural field theory of brain dynamics underlying oscillations in Parkinson’s disease and generalized epilepsies. Journal of Theoretical Biology 428:132–146.
DOI: 10.1016/j.jtbi.2017.06.016.
von Papen M., Dafsari H., Florin E., Gerick F., Timmermann L., Saur J. (2017) Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components. Journal of Neuroscience Methods 291:198-212.
DOI: 10.1016/j.jneumeth.2017.08.021
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.
DOI: 10.3389/fncom.2017.00041.
Rostami V., Ito J., Denker M., Grün S. (2017) [Re] Spike Synchronization and Rate Modulation Differentially Involved in Motor Cortical Function. ReScience 3(1).
DOI: 10.5281/zenodo.583814.
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.
Schuecker J., Schmidt M., van Albada SJ., Diesmann M., Helias M. (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLoS Computational Biology 13:e1005179.
DOI: 10.1371/journal.pcbi.1005179.
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.
Spreizer S., Angelhuber M., Bahuguna J., Aertsen A., Kumar A. (2017) Activity Dynamics and Signal Representation in a Striatal Network Model with Distance-Dependent Connectivity. eNeuro 4(4).
DOI: 10.1523/ENEURO.0348-16.2017.

2016

Bos H., Diesmann M., Helias M. (2016) Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. PLoS Computational Biology 12:e1005132.
DOI: 10.1371/journal.pcbi.1005132.
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.
Chua Y., Morrison A. (2016) Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation. Frontiers in Computational Neuroscience 10:76.
DOI: 10.3389/fncom.2016.00076.
Dahmen D., Bos H., Helias M. (2016) Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium. Physical Review X 6:031024.
DOI: 10.1103/PhysRevX.6.031024
Diaz-Pier S., Naveau M., Butz-Ostendorf M., Morrison A. (2016) Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity. Frontiers in Neuroanatomy 10:57.
DOI: 10.3389/fnana.2016.00057.
Grytskyy D., Diesmann M., Helias M. (2016) Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality. Physical Review E 93:062303.
DOI: 10.1103/PhysRevE.93.062303.
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.
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 2(1).
DOI:10.5281/zenodo.161526
Mochizuki Y., Onaga T., Shimazaki H., Shimokawa T., Tsubo Y., Kimura R., Saiki A., Sakai Y., Isomura Y., Fujisawa S., Shibata KI., Hirai D., Furuta T., Kaneko T., Takahashi S., Nakazono T., Ishino S., Sakurai Y., Kitsukawa T., Lee JW., Lee H., Jung MW., Babul C., Maldonado PE., Takahashi K., Arce-McShane FI., Ross CF., Sessle BJ., Hatsopoulos NG., Brochier T., Riehle A., Chorley P., Grün S., Nishijo H., Ichihara-Takeda S., Funahashi S., Shima K., Mushiake H., Yamane Y., Tamura H., Fujita I., Inaba N., Kawano K., Kurkin S., Fukushima K., Kurata K., Taira M., Tsutsui KI., Ogawa T., Komatsu H., Koida K., Toyama K., Richmond BJ., Shinomoto S. (2016) Similarity in Neuronal Firing Regimes across Mammalian Species. Journal of Neuroscience 36:5736–5747.
DOI: 10.1523/JNEUROSCI.0230-16.2016.
Morita K., Jitsev J., Morrison A. (2016) Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond. Behavioural Brain Research 311:110–121.
DOI: 10.1016/j.bbr.2016.05.017.
Pfeil T., Jordan J., Tetzlaff T., Grübl A., Schemmel J., Diesmann M., Meier K. (2016) Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study. Physical Review X 6:021023.
DOI: 10.1103/PhysRevX.6.021023.
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.
DOI: 10.1371/journal.pcbi.1004939.
Torre E., Quaglio P., Denker M., Brochier T., Riehle A., Grün S. (2016) Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task. Journal of Neuroscience 36:8329–8340.
DOI: 10.1523/JNEUROSCI.4375-15.2016.
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.
DOI: 10.1007/s10827-015-0581-5.
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.
DOI: 10.3389/fninf.2016.00031.
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.
DOI: 10.1021/acsami.6b04666
Yegenoglu A., Quaglio P., Torre E., Grün S., Endres D. (2016) Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains. In: Haemmerlé O., Stapleton G., Faron Zucker C. (eds) Graph-Based Representation and Reasoning. Lecture Notes in Computer Science 9717.
DOI: 10.1007/978-3-319-40985-6_1
Zehl L., Jaillet F., Stoewer A., Grewe J., Sobolev A., Wachtler T., Brochier TG., Riehle A., Denker M., Grün S. (2016) Handling Metadata in a Neurophysiology Laboratory. Frontiers in Neuroinformatics 10:26.
DOI: 10.3389/fninf.2016.00026.

2015

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.
DOI: 10.1371/journal.pcbi.1004490.
Bahuguna J., Aertsen A., Kumar A. (2015) Existence and Control of Go/No-Go Decision Transition Threshold in the Striatum. PLoS Computational Biology 11:e1004233.
DOI: 10.1371/journal.pcbi.1004233.
Bakker R., Tiesinga P., Kötter R. (2015) The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content. Neuroinformatics 13:353–366.
DOI: 10.1007/s12021-014-9258-x.
Chua Y., Morrison A., Helias M. (2015) Modeling the calcium spike as a threshold triggered fixed waveform for synchronous inputs in the fluctuation regime. Frontiers in Computational Neuroscience 9:91.
DOI: 10.3389/fncom.2015.00091.
Duarte R. (2015) Expansion and State-Dependent Variability along Sensory Processing Streams. Journal of Neuroscience 35:7315–7316.
DOI: 10.1523/JNEUROSCI.0874-15.2015.
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.
DOI: 10.3389/fninf.2015.00022.
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.
DOI: 10.1016/j.neuroimage.2015.04.008.
Muller E., Bednar JA., Diesmann M., Gewaltig MO., Hines M., Davison AP. (2015) Python in neuroscience. Frontiers in Neuroinformatics 9:11.
DOI: 10.3389/fninf.2015.00011.
Schuecker J., Diesmann M., Helias M. (2015) Modulated escape from a metastable state driven by colored noise. Physical Review E 92.
DOI: 10.1103/PhysRevE.92.052119.
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.
DOI: 10.1016/j.jneumeth.2014.11.005.
Tiesinga P., Bakker R., Hill S., Bjaalie JG. (2015) Feeding the human brain model. Current Opinion in Neurobiology 32:107–114.
DOI: 10.1016/j.conb.2015.02.003.
Zaytsev YV., Morrison A., Deger M. (2015) Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity. Journal of Computational Neuroscience 39:77–103.
DOI: 10.1007/s10827-015-0565-5.

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
Bezgin G., Rybacki K., van Opstal AJ., Bakker R., Shen K., Vakorin VA., McIntosh AR., Kötter R. (2014) Auditory–prefrontal axonal connectivity in the macaque cortex: Quantitative assessment of processing streams. Brain and Language 135:73–84.
DOI: 10.1016/j.bandl.2014.05.006.
Chapuis A., Tetzlaff T. (2014) The variability of tidewater-glacier calving: origin of event-size and interval distributions. Journal of Glaciology 60:622–634.
DOI: 10.3189/2014JoG13J215.
Cichon NB., Denker M., Grün S., Hanganu-Opatz IL. (2014) Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents. Frontiers in Neural Circuits 8:50.
DOI: 10.3389/fncir.2014.00050.
Djurfeldt M., Davison AP., Eppler JM. (2014) Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Frontiers in Neuroinformatics 8:43.
DOI: 10.3389/fninf.2014.00043.
Duarte R., Morrison A. (2014) Dynamic stability of sequential stimulus representations in adapting neuronal networks. Frontiers in Computational Neuroscience 8:124.
DOI: 10.3389/fncom.2014.00124.
Helias M., Tetzlaff T., Diesmann M. (2014) The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics. PLoS Computational Biology 10:e1003428.
DOI: 10.1371/journal.pcbi.1003428.
Ito J., Roy S., Liu Y., Cao Y., Fletcher M., Lu L., Boughter JD., Grün S., Heck DH. (2014) Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration. Nature Communications 5.
DOI: 10.1038/ncomms4572.
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.
DOI: 10.3389/fncom.2014.00136.
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.
DOI: 10.3389/fncom.2013.00187.
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.
DOI: 10.3389/fninf.2014.00078.
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.
DOI: 10.1371/journal.pcbi.1003928.
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.
DOI: 10.1093/cercor/bhs358.
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.
DOI: 10.3389/fncom.2014.00130.
Zaytsev YV., Morrison A. (2014) CyNEST: a maintainable Cython-based interface for the NEST simulator. Frontiers in Neuroinformatics 8:23.
DOI: 10.3389/fninf.2014.00023.

2013

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.
DOI: 10.3389/fncom.2013.00103
van Albada SJ., Robinson PA. (2013) Relationships between electroencephalographic spectral peaks across frequency bands. Frontiers in Human Neuroscience 7:56.
DOI: 10.3389/fnhum.2013.00056
Grytskyy D., Tetzlaff T., Diesmann M., Helias M. (2013) A unified view on weakly correlated recurrent networks. Frontiers in Computational Neuroscience 7:131.
DOI: 10.3389/fncom.2013.00131
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.
DOI: 10.1007/s10827-013-0456-6
Helias M., Tetzlaff T., Diesmann M. (2013) Echoes in correlated neural systems. New Journal of Physics 15:023002.
DOI: 10.1088/1367-2630/15/2/023002.
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.
DOI: 10.3389/fnsys.2013.00001
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.
DOI: 10.3389/fncom.2013.00039
Łęski S., Lindén H., Tetzlaff T., Pettersen KH., Einevoll GT. (2013) Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential. PLoS Computational Biology 9:e1003137.
DOI: 10.1371/journal.pcbi.1003137
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.
DOI: 10.1109/BioVis.2013.6664348
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.
DOI: 10.3389/fninf.2013.00009
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.
DOI: 10.1162/NECO_a_00432
Riehle A., Wirtssohn S., Grün S., Brochier T. (2013) Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements. Frontiers in Neural Circuits 7:48.
DOI: 10.3389/fncir.2013.00048
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
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.
DOI: 10.3389/fncom.2013.00132
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.
DOI: 10.1007/s10827-012-0413-9
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.
DOI: 10.1016/j.nbd.2013.07.018
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.
DOI: 10.1016/j.nonrwa.2012.08.013
Zaytsev YV., Morrison A. (2013) Increasing quality and managing complexity in neuroinformatics software development with continuous integration. Frontiers in Neuroinformatics 6:31.
DOI: 10.3389/fninf.2012.00031

2012

Bakker R., Wachtler T., Diesmann M. (2012) CoCoMac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics 6:30.
DOI: 10.3389/fninf.2012.00030.
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.
DOI: 10.1016/j.brainres.2011.10.013.
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.
DOI: 10.1016/j.neuroimage.2012.04.013.
Borgelt C., Braune C., Kötter T., Grün S. (2012) New algorithms for finding approximate frequent item sets. Soft Computing 16:903–917.
DOI: 10.1007/s00500-011-0776-2.
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.
DOI: 10.3109/0954898X.2012.722743.
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.
DOI: 10.1371/journal.pcbi.1002689.
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.
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.
DOI: 10.3389/fninf.2012.00026.
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.
DOI: 10.3389/fninf.2011.00035.
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
DOI:10.1007/978-94-007-3858-4_10
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.
DOI: 10.3389/fnins.2012.00090.
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.
DOI: 10.1371/journal.pcbi.1002385.
Tetzlaff T., Helias M., Einevoll GT., Diesmann M. (2012) Decorrelation of Neural-Network Activity by Inhibitory Feedback. PLoS Computational Biology 8:e1002596.
DOI: 10.1371/journal.pcbi.1002596.

2011

Brüderle D., Petrovici MA., Vogginger B., Ehrlich M., Pfeil T., Millner S., Grübl A., Wendt K., Müller E., Schwartz MO., de Oliveira DH., Jeltsch S., Fieres J., Schilling M., Müller P., Breitwieser O., Petkov V., Muller L., Davison AP., Krishnamurthy P., Kremkow J., Lundqvist M., Muller E., Partzsch J., Scholze S., Zühl L., Mayr C., Destexhe A., Diesmann M., Potjans TC., Lansner A., Schüffny R., Schemmel J., Meier K. (2011) A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biological Cybernetics 104:263–296.
DOI: 10.1007/s00422-011-0435-9.
Chiang AKI., Rennie CJ., Robinson PA., van Albada SJ., Kerr CC. (2011) Age trends and sex differences of alpha rhythms including split alpha peaks. Clinical Neurophysiology 122:1505–1517.
DOI: 10.1016/j.clinph.2011.01.040.
Deger M., Helias M., Boucsein C., Rotter S. (2011) Statistical properties of superimposed stationary spike trains. Journal of Computational Neuroscience 32:443–463.
DOI: 10.1007/s10827-011-0362-8.
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.

2010

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.

2009

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 Computational Biology 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.

2008

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.

2007

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.
Gewaltig MO., Diesmann M. (2007) NEST (NEural Simulation Tool). Scholarpedia, 2(4):1430.
DOI: 10.4249/scholarpedia.1430
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

2006

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.

2005

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.

2004

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.

2003

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.

2002

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.

2001

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.

2000

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.

1999

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.

1997

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.

1996

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.

1995

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: 17.07.2024

Institute for Advanced Simulation (IAS)

Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience

Peer-reviewed publications