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This is an incomplete list of papers that used NEST, describe NEST, or mention NEST.

If you have used or discussed NEST in one of your publications, please send us the reference and we will add it to this list.



Abdelghani, M., Abbas, J. and Jung, R.,Peripheral Nerve Interface Applications, EMG/ENG, Jaeger, D. & Jung, R. (ed.), Encyclopedia of Computational Neuroscience, Springer New York, 2014, pp. 1-10, 10.1007/978-1-4614-7320-6_199-1
Pfeil, T., Exploring the potential of brain-inspired computing, Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany, 2015 urn:nbn:de:bsz:16-heidok-182585
S. Zaytsev, Y. V., Morrison, A. and Deger, M., Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity, ARXIV, 2015,
Sukhinin DI, Engel AK, Manger P and Hilgetag CC (2015),Building the Ferretome, bioRxiv. Cold Spring Harbor Labs Journals.
Sadeh S and Rotter S (2015), Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics, PLoS Comput Biol., 01, 2015. Vol. 11(1), pp. e1004045. Public Library of Science.
Probst D, Petrovici MA, Bytschok I, Bill J, Pecevski D, Schemmel J and Meier K (2015), Probabilistic Inference in Discrete Spaces Can Be Implemented into Networks of LIF Neurons, Frontiers in Computational Neuroscience. Vol. 9(13),
Hagen E, Ness TV, Khosrowshahi A, Sørensen C, Fyhn M, Hafting T, Franke F and Einevoll GT (2015),ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms , Journal of Neuroscience Methods . (0), pp. - .
Arnoldt H, Chang S, Jahnke S, Urmersbach B, Taschenberger H and Timme M (2015), When Less Is More: Non-monotonic Spike Sequence Processing in Neurons, PLoS Comput Biol., 02, 2015. Vol. 11(2), pp. e1004002. Public Library of Science.


Pyka, M., Klatt, S. and Cheng, S., Parametric Anatomical Modeling: A method for modeling the anatomical layout of neurons and their projections, Frontiers in Neuroanatomy, 2014, Vol. 8(91),10.3389/fnana.2014.00091
Eppler JM (2014), NEST Code Generation - Motivation and prior work, 12/08/2014 - 12/09/2014, 2014.
Diesmann M (2014), Status of the Network simulator NEST, 09/03/2014 - 09/05/2014, 2014.
van Albada S and Diesmann M (2014), NEST HPC status - technology and theory, 11/25/2014 - 11/26/2014, 2014.
Hahn G, Bujan AF, Frégnac Y, Aertsen A and Kumar A (2014), Communication through Resonance in Spiking Neuronal Networks, PLoS Comput Biol., 08, 2014. Vol. 10(8), pp. e1003811. Public Library of Science.
Jahnke S, Memmesheimer R-M and Timme M (2014), Oscillation-Induced Signal Transmission and Gating in Neural Circuits, PLoS Comput Biol., 12, 2014. Vol. 10(12), pp. e1003940. Public Library of Science.
Kunkel S, Helias M, Diesmann M and Morrison A (2014),Supercomputer simulations of spiking neuronal networks, 02/05/2014 - 02/06/2014, 2014.
Kunkel S, Helias M, Diesmann M and Morrison A (2014), Specifying supercomputers for brain-scale neuronal network simulations, 05/26/2014 - 05/27/2014, 2014.
Petrovici MA, Vogginger B, Müller P, Breitwieser O, Lundqvist M, Muller L, Ehrlich M, Destexhe A, Lansner A, Schüffny R, Schemmel J and Meier K (2014), Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms, PLoS ONE., 10, 2014. Vol. 9(10), pp. e108590. Public Library of Science.
Wybo W, Boccalini D, Torben-Nielsen B and Gewaltig M-O (2014), A sparse reformulation of the Green's function formalism allows efficient simulations of morphological neuron models, Frontiers in Systems Neuroscience. (35),
Vornanen I, Hyttinen JAK and Lenk K (2014), The effect of longer range connections on neuronal network dynamics, Frontiers in Neuroinformatics. (9),
Plesser HE, Kunkel S, Helias M, Diesmann M and Morrison A (2014), The NEST 4g kernel: highly scalable simulation code from laptops to supercomputers, 01/27/2014 - 01/29/2014, 2014.
van Albada, S., Helias, M. and Diesmann, M. One-to-one relationship between effective connectivity and correlations in asynchronous networks, Conference Presentation, In Proceedings, Bernstein Conference, Göttingen(Germany), 09/03/2014 - 09/05/2014, G-Node, doi:10.12751/nncn.bc2014.0134, 2014
van Albada, S., Helias, M. and Diesmann, M. Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations, Article, arXiv:1411.4770 , 2014
Davison, A. PyNN: A Python API for Neural Network Modeling, in Jaeger, D. and Jung, R., ed.,'Encyclopedia of Computational Neuroscience', Springer New York, doi:10.1007/978-1-4614-7320-6_261-5, 2014
Jitsev, J., Tittgemeyer, M. and Morrison, A. Distinct plasticity mechanisms in the basal ganglia and their functional role in reinforcement learning, Poster, 9th European Forum of Neuroscience, Milano(Italy), 07/05/2014 - 07/09/2014,, 2014
Kriener, B., Enger, H., Tetzlaff, T., Plesser, H. E., Gewaltig, M.-O. and Einevoll, G. T. Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses, Article, Frontiers in computational neuroscience (8), doi: 10.3389/fncom.2014.00136, 2014
Pfeil, T., Jordan, J., Tetzlaff, T., Grübl, A., Schemmel, J., Diesmann, M. and Meier, K. The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study, Article, arXiv:1411.7916, 2014
Schenck, W., Adinets, A., Zaytsev, Y., Pleiter, D. and Morrison, A. Performance Model for Large-Scale Neural Simulations with NEST, Poster, Supercomputing 2014, New Orleans(USA), 11/16/2014 - 11/21/2014,, 2014
Ebert M, Hauptmann C and Tass PA (2014) Coordinated reset stimulation in a large-scale model of the STN-GPe circuit. Article, Front. Comput. Neurosci. 8:154. doi: 10.3389/fncom.2014.00154
Toledo-Suárez, C.; Duarte, R. & Morrison, A. (2014) Liquid computing on and off the edge of chaos with a striatal microcircuit, Article, Frontiers in Computational Neuroscience, 8:130., doi:10.3389/fncom.2014.00130
Duarte, R. C. F. & Morrison, A. (2014) Dynamic stability of sequential stimulus representations in adapting neuronal networks. Frontiers in Computational Neuroscience, Article (10.3389/fncom.2014.00124), DOI=10.3389/fncom.2014.00124
Deger, M.; Schwalger, T.; Naud, R. & Gerstner, W. (2014) Fluctuations and information filtering in coupled populations of spiking neurons with adaptation, Article, Phys. Rev. E, American Physical Society, 2014, 90, 062704.
Schmidt, Maximilian and van Albada, Sacha and Bakker, Rembrandt and Diesmann, Markus (2014) A spiking multi-area network model of macaque visual cortex, InProceedings, Annual meeting of the SfN, Washington, DC(USA), 11/15/2014 – 11/19/2014,
Charlotte Le Mouel, Kenneth D. Harris, Pierre Yger, J Comput Neurosci. (2014) Supervised learning with decision margins in pools of spiking neurons, J Comput Neurosci. 2014; 37(2): 333–344, Published online 2014 May 28. doi: 10.1007/s10827-014-0505-9
Christian Tomm, Michael Avermann, Carl Petersen, Wulfram Gerstner, Tim P. Vogels (2014) Connection-type-specific biases make uniform random network models consistent with cortical recordings, Journal of Neurophysiology Oct 2014,112(8)1801-1814. DOI: 10.1152/jn.00629.2013
Jochen M. Eppler & Jannis Schücker (2014) Simulating large-scale spiking neuronal networks with NEST Lecture (Invited), Computation Neuroscience Conference 2014, CNS14, Quebec, Canada, 07/23/2014 – 07/31/2014
Naveau, M., Butz-Ostendorf, M. (2014). Simulating structural plasticity of large scale networks in NEST Abstracts from the Twenty Third Annual Computational Neuroscience Meeting: CNS*2014, BMC Neurosci. 2014; 15(Suppl 1): P194. Published online Jul 21, 2014. doi: 10.1186/1471-2202-15-S1-P194
Chapuis A & Tetzlaff T (2014) The variability of tidewater-glacier calving: origin of event-size and interval distributions Journal of Glaciology 60(222):622–634, doi:10.3189/2014JoG13J215
Chen W, De Schutter E (2014) Python-based geometry preparation and simulation visualization toolkits for STEPS PLOS One, doi:10.3389/fninf.2014.00037
Djurfeldt M, Davison A, Eppler J (2014) Efficient generation of connectivity in neuronal networks from simulator-independent descriptions Front Neuroinform, doi:10.3389/fninf.2014.00043
Furber S.B, Galluppi F, Temple S, Plana L A(2014) The SpiNNaker Project The Proceedings of the IEEE 1-14, doi:10.1109/JPROC.2014.2304638
Gewaltig MO, Cannon R (2014) Current practice in software development for computational neuroscience and how to improve it PLoS Comput Biol 10(1):e1003376, doi:10.1371/journal.pcbi.1003376
Helias M, Tetzlaff T, Diesmann M (2014) The correlation structure of local neuronal networks intrinsically results from recurrent dynamics PLoS Comput Biol 10(1):e1003428, doi:10.1371/journal.pcbi.1003428
Jahnke S, Memmesheimer R-M, Timme M (2014) Hub-activated signal transmission in complex networks arXiv, doi:10.1103/PhysRevE.89.030701
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 Front Comput Neurosci 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 and Helias M (2014) Spiking network simulation code for petascale computers Front. Neuroinform. 8:78. doi: 10.3389/fninf.2014.00078
Minkovich K, Thibeault C M, O'Brien M J, Nogin A (2014) HRLSim: A high performance spiking neural network simulator for GPGPU clusters Neural Networks and Learning Systems, IEEE Transactions on 25:316-331, doi:10.1007/s00521-013-1408-9
Muller L, Reynaud A, Chavane F, Destexhe A (2014) The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave Nature Communications, doi:10.1038/ncomms4675
Rothganger F, Warrender C, Trumbo D, Aimone J (2014) N2A: A computational tool for modeling from neurons to algorithms Front Neural Circuits 8:1, doi:10.3389/fncir.2014.00001
Sousa M, Aguiar P (2014) Building, simulating and visualizing large spiking neural networks with NeuralSyns Neurocomputing 123:280-372, doi:10.1016/j.neucom.2013.07.034
Strack B, Jacobs K, Cios K (2014) Simulating vertical and horizontal inhibition with short-term dynamics in a multi-column multi-layer model of neocortex International Journal of Neural Systems, doi:10.1142/S0129065714400024
Tully P, Hennig M, Lansner A (2014) Synaptic and nonsynaptic plasticity approximating probabilistic inference Frontiers in Synaptic Neuroscience, doi:10.3389/fnsyn.2014.00008
Vella M, Cannon R, Crook S, Davison A, Ganapathy G, Robinson H, Silver R, Gleeson P (2014) libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience Front Neuroinform, doi:10.3389/fninf.2014.00038
Waźny M, Wojcik G (2014) Shifting spatial attention—Numerical model of Posner experiment Neurocomputing, doi:10.1016/j.neucom.2013.12.043
Yim M, Kumar A, Aersten A, Rotter S (2014) Impact of correlated inputs to neurons: modeling observations from in vivo intracellular recordings Journal of Computational Neuroscience, doi:10.1007/s10827-014-0502-z
Yuan C-W, Khouri L, Grothe B, Leibold C (2014) Neuronal Adaptation Translates Stimulus Gaps into a Population Code PLOS One, doi:10.1371/journal.pone.0095705
Zaytsev Y, Morrison A (2014) CyNEST: a maintainable Cython-based interface for the NEST simulator Frontiers in Neuroinformatics 8:23, doi:10.3389/fninf.2014.00023
van Albada SJ, Kunkel S, Morrison A, Diesmann M (2014) Integrating brain structure and dynamics on supercomputers in Brain-Inspired Computing (Lecture Notes in Computer Science), pg. 22-32, Grandinetti L, Lippert T, Petkov Nicolai (ed.), Springer International Publishing, doi:10.1007/978-3-319-12084-3_3


Plesser H, Eppler JM and Gewaltig M (2013). 20 Years of NEST: A Mature Brain Simulator. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00106
Nowke, C.; Hentschel, B.; Kuhlen, T.; Schmidt, M.; van Albada, S.; Eppler, J. M.; Bakker, R. & Diesmann, M (2013) Interactive visualization of brain-scale spiking activity, InProceedings, BMC neuroscience 14(Suppl 1), P110 – (2013) doi:10.1186/1471-2202-14-S1-P110
Davison AP, Djurfeldt M, Eppler JM, Gleeson P, Hull M and Muller EB (2013). An integration layer for neural simulation: PyNN in the software forest Front Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00020
Amir A, Datta P, Risk W P, Cassidy A S, Kusnitz J A, Esser S K, Andreopoulos A, Wong, T M, Flickner M, Alvarez-Icaza R, McQuinn E, Shaw B, Pass N, Modha D.S. (2013) Cognitive computing programming paradigm: A corelet language for composing networks of neurosynaptic cores Neural Networks (IJCNN), The 2013 International Joint Conference on, doi:10.1109/IJCNN.2013.6707078
Antolík J, Davison A (2013) Integrated workflows for spiking neuronal network simulations [ Front Neuroinform 7:34, doi:10.3389/fninf.2013.00034]
Baptista D, Morgado-Dias F (2013) A survey of artificial neural network training tools Neural Computing and Applications 10:1-7, doi:10.1007/s00521-013-1408-9
Beeman D (2013) History of neural simulation software Springer Series in Computational Neuroscience 9:33-71, doi:10.1007/978-1-4614-1424-7_3
Bekolay T, Bergstra J, Hunsberger E, DeWolf T, Terrence S, Rasmussen D, Choo X, Voelker A, Eliasmith C (2013) Nengo: a Python tool for building large-scale functional brain models Front Neuroinform 7:48, doi:10.3389/fninf.2013.00048
Cohen M, Gulbinaite R (2013) Five methodological challenges in cognitive electrophysiology Neuroimage (in press),
Crook S, Davidson A, Plesser H(2013) Learning from the Past: Approaches for Reproducibility in Computational Neuroscience 20 Years of Computational Neuroscience, doi:10.1007/978-1-4614-1424-7_4
De Schutter E (2013) Colloborative Modeling in Neuroscience: Time to go open Model? Neuroinformatics 11(2):135-136, doi:10.1007/s12021-013-9181-6
Diesmann M (2013) The road to brain-scale simulations on K BioSupercomputing Newsletter 8:8
Eliasmith C, Oliver Trujillo O (2013) The use and abuse of large-scale brain models Current Opinion in Neurobiology 25:1-6, doi:10.1016/j.conb.2013.09.009
Farkhooi F, Froese A, Muller E, Menzel R, Nawrot M (2013) Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways PLOS Comput Biol 9(10):e1003251, doi:10.1371/journal.pcbi.1003251
Ferreiroa R and Sanchez E (2013) Contrast Enhancement Mechanisms in the Retinothalamic Circuitry Natural and Artificial Models in Computation and Biology 7930:26-36, doi:10.1007/978-3-642-38637-4_4
Grytskyy D, Tetzlaff T, Diesmann M, Helias M (2013) A unified view on weakly correlated recurrent networks Frontiers Comput. Neurosci. 7:131, doi:10.3389/fncom.2013.00131
Heiberg T, Kriener B, Tetzlaff T, Casti A, Einevoll GT, Plesser H-E (2013) Firing-rate models capture essential response dynamics of LGN relay cells Journal of Computational Neuroscience (epub before print), doi:10.1007/s10827-013-0456-6
Helias M, Tetzlaff T, Diesmann M (2013) Echoes in correlated neural systems New Journal of Physics 15(2):023002, doi:10.1088/1367-2630/15/2/023002
Hernandez O, Zurek E (2013) Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON's programming language hoc BMC Medical Education 13(1):1-9, doi:10.1186/1472-6920-13-70
Holt A, Netoff T (2013) Computational modeling of epilepsy for an experimental neurologist Experimental Neurology 244:75-86,
Hull M, Willshaw D (2013) morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python Front Neuroinform 7:47, doi:10.3389/fninf.2013.00047
Jahnke S, Memmesheimer R-M, Timme M (2013) Propagating synchrony in feed-forward networks Front Comput Neurosci 7:153, doi:10.3389/fncom.2013.00153
Lee J, Tsunada J, Cohen YE (2013) A model of the differential representation of signal novelty in the local field potentials and spiking activity of the ventrolateral prefrontal cortex Neural Comput 25(1):157-185, doi:10.1162/NECO_a_00388
Lengler J, Jug F, Steger A (2013) Reliable neuronal systems: The importance of heterogeneity PLOS One 8(12):e80694, doi:10.1109/TNNLS.2013.2276056
Leon P, Knock S, Woodman M, Domide L, Mersmann J, Mcintosh A, Jirsal V (2013) The Virtual Brain: a simulator of primate brain network dynamics Frontiers in Neuroinformatics 7(10), 10.3389/fninf.2013.000
Lindahl M, Sarvestani I, Ekeberg O, Kotaleski J (2013) Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways Frontiers in Computational Neuroscience 7(76): 1-19, doi:10.3389/fncom.2013.00076
Mattioni M, Le Novere N (2013) Integration of Biochemical and Electrical Signaling-Multiscale Model of the Medium Spiny Neuron of the Striatum PLOS One 8(7):e66811, doi:10.1371/journal.pone.0066811
Mäki-Marttunen T, Acimovic J, Ruohonen K, Linne M-L (2013) Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework PLOS One 8(7):e69373, doi:10.1371/journal.pone.0069373
Mohemmed A, Schliebs S, Matsuda S, Nikola K (2013) Training spiking neural networks to associate spatio-temporal input–output spike patterns Neurocomputing 107:3-10, doi:10.1016/j.neucom.2012.08.034
Morén J, Shibata T, Doya K(2013) The Mechanism of Saccade Motor Pattern Generation Investigated by a Large-Scale Spiking Neuron Model of the Superior Colliculus PLOS One 8(2):e57134, doi:10.1371/journal.pone.0057134
Mulas M, Massobrio P (2013) NeuVision: A novel simulation environment to model spontaneous and stimulus-evoked activity of large-scale neuronal networks Neurocomputing (in press), doi:
Parekh R, Ascoli G (2013) Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience Neuron 77(6):1017-1038, doi:
Pernice V, Deger M, Cardanobile S and Rotter S (2013) The relevance of network micro-structure for neural dynamics Front Comput Neurosci 7:72, doi:10.3389/fncom.2013.00072
Potjans T, Diesmann M (2013) Multi-population Network Models of the Cortical Microcircuit Advances in Cognitive Neurodynamics (III):91-96, doi:10.1007/978-94-007-4792-0_13
Richmond P, Cope A, Gurney K, Allerton D (2013) From model specification to simulation of biologically constrained networks of spiking neurons Neuroinformatics, doi:10.1007/s12021-013-9208-z
Saito J H, Mari J F, Pedrino E, Destro-Filho B, Nicoletti M C (2013) Simulated Activation Patterns of Biological Neurons Cultured onto a Multi-Electrode Array Based on a Modified Izhikevich's Model Fundamenta Informaticae 124(1):111-132, doi:10.3233/FI-2013-827
Schultze-Kraft M, Diesmann M, Grün S, Helias M (2013) Noise suppression and surplus synchrony by coincidence detection PLoS Comput Biol 9:e1002904
Sharp T, Furber S (2013) Correctness and performance of the SpiNNaker architecture The 2013 International Joint Conference on Neural Networks (IJCNN) 1-8, doi:10.1109/IJCNN.2013.6706988
Sreenivasa M, Murai A, Nakamura Y (2013) Modeling and identification of the human arm stretch reflex using a realistic spiking neural network and musculoskeletal model 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): 329-334, doi:10.1109/IROS.2013.6696372
Stevens J-L, Elver M, Bednar J (2013) An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook Front Neuroinform 7:44, doi:10.3389/fninf.2013.00044
Stimberg M, Goodman F M D, Benichoux V, Brette R (2014) Equation-oriented specification of neural models for simulations Front Neuroinform 8:6, doi:10.3389/fninf.2014.00006
Tang Y, Zhang B, Wu J, Hu T, Zhou J, Liu F (2013) Parallel architecture and optimization for discrete-event simulation of spike neural networks Science China Technological Sciences 56(2):509-517, doi:10.1007/s11431-012-5084-2
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 (in press), doi:
Vlachos I, Zaytsev Y, Spreizer S, Aertsen A, Kumar A (2013) Neural system prediction and identification challenge Front Neuroinform 7:43, doi:10.3389/fninf.2013.00043
Wagatsuma N, Potjans T C, Diesmann M, Sakai K, Fukai T (2013) Spatial and feature-based attention in a layered cortical microcircuit model PLoS ONE 8:12, doi:10.1371/journal.pone.0080788
Yger P, Harris K D (2013) The Convallis rule for unsupervised learning in cortical networks PLOS Comput Bio 9:10, doi:10.1371/journal.pcbi.1003272
Zaytsev YV, Morrison A (2013) Increasing quality and managing complexity in neuroinformatics software development with continuous integration Front Neuroinform 6:31, doi:10.3389/fninf.2012.00031


Avermann M, Tomm C, Mateo C, Gerstner W, Petersen CCH (2012) Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex J Neurophysiol 107(11):3116-3134, doi:10.1152/jn.00917.2011
Benjaminsson S, Lansner A (2012) Nexa: a scalable neural simulator with integrated analysis Network 23(4):254-271, doi:10.3109/0954898X.2012.737087
Bray LCJ, Anumandla SR, Thibeault CM, Hoang RV, Goodman PH, Dascalu SM, Bryant BD, Harris FC (2012) Real-time human-robot interaction underlying neurorobotic trust and intent recognition Neural Networks 32:130-137
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 S (2012) Creating, documenting and sharing network models Network: Computation in Neural Systems 23(4):131-149, doi:10.3109/0954898X.2012.722743
Deger M, Helias M, Boucsein C, Rotter S (2012) Statistical properties of superimposed stationary spike trains Journal of Computational Neuroscience 32(3):443-4–63, doi:10.1007/s10827-011-0362-8
Deger M, Helias M, Rotter S (2012) Effective generators for superpositions of non-Poissonian spike trains F1000 Posters 2012, 3:1400 (poster)
Deger M, Helias M, Rotter S, Diesmann M (2012) Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex PLoS Comput Biol 8(9):e1002689, doi:10.1371/journal.pcbi.1002689
Dinkelbach HU, Vitay J, Beuth F, Hamker FH (2012) Comparison of GPU- and CPU-implementations of mean-firing rate neural networks on parallel hardware Network 23(4):212-236
Djurfeldt M (2012) The connection-set algebra - a novel formalism for the representation of connectivity structure in neuronal network models Neuroinformatics 10(3):287-304, doi:10.1007/s12021-012-9146-1
Ferreiroa R, Sánchez E, Martínez L (2012) Realistic model of the dLGN push-pull circuitry Neurocomputing 0, doi:10.1016/j.neucom.2012.07.037
Galluppi F, Davies S, Rast A, Sharp T, Plana LA, Furber S (2012) A hierachical configuration system for a massively parallel neural hardware platform in Proceedings of the 9th conference on Computing Frontiers, New York, NY, USA, pgs. 183-192, doi:10.1145/2212908.2212934
Gerstein GL, Williams ER, Diesmann M, Grün S, Trengove C (2012) Detecting synfire chains in parallel spike data J Neurosci Methods 206(1):54-64
Gewaltig MO, Morrison A, Plesser HE (2012) NEST by example: an introduction to the neural simulation tool NEST Chapt. 18 in Nicolas Le Novére Computational Systems Biology, Springer, ISBN 978-94-007-3857-7
Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A and Diesmann M (2012) Supercomputers ready for use as discovery machines for neuroscience Front. Neuroinform. 6:26, doi: 10.3389/fninf.2012.00026
Henker S, Partzsch J, Schüffny R (2012) Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks J Comput Neurosci 32(2):309-326, doi:10.1007/s10827-011-0353-9
Jahnke S, Timme M, Memmesheimer RM (2012) Guiding Synchrony through Random Networks Phys Rev X 2(4):041016, doi:10.1103/PhysRevX.2.041016
Jitsev J, Abraham Nobi, Morrison A., Tittgemeyer M (2012) Learning from Delayed Reward und Punishment in a Spiking Neural Network Model of Basal Ganglia with Opposing D1/D2 Plasticity in Artificial Neural Networks and Machine Learning – ICANN 2012, Springer Berlin Heidelberg, vol. 7552, pgs. 459-466, doi:10.1007/978-3-642-33269-2_58
Jitsev J, Morrison A, Tittgemeyer M (2012) Learning from positive and negative rewards in a spiking neural network model of basal ganglia in Neural Networks (IJCNN), The 2012 International Joint Conference on, pgs. 1-8, doi:10.1109/IJCNN.2012.6252834
de Kamps M (2012) Towards truly human-level intelligence in artificial applications Cognitive Systems Research 14(1):1-9, doi:10.1016/j.cogsys.2011.01.003
Kriener B (2012), How synaptic weights determine stability of synchrony in networks of pulse-coupled excitatory and inhibitory oscillators Chaos 22:033143, doi:10.1063/1.4749794
Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A and Diesmann M (2012) Meeting the memory challenges of brain-scale network simulation Front. Neuroinform. 5:35, doi:10.3389/fninf.2011.00035
Kunkel S, Helias M, Potjans TC, Eppler JM, Plesser HE, Diesmann M, Morrison A (2012) Memory Consumption of Neuronal Network Simulators at the Brain Scale in Binder K, Münster G, Kremer M (Eds) NIC Symposium 2012 Proceedings NIC Series Vol. 45, page 81, Jülich, Germany, ISBN 978-3-89336-758-0
Lansner A, Diesmann M (2012) Virtues, pitfalls, and methodology of neuronal network modeling and simulations on supercomputers Chapt. 10 in Nicolas Le Novére Computational Systems Biology, Springer, ISBN 978-94-007-3857-7
Mattioni M, Cohen U, Le Novère N (2012) Neuronvisio: A Graphical User Interface with 3D Capabilities for NEURON Front Neuroinf 6:20, doi:10.3389/fninf.2012.00020
Mohemmed A, Schliebs S, Matsuda S, Kasabov N (2012) Span: spike pattern association neuron for learning spatio-temporal spike patterns Int J Neural Syst 22(4):1250012, doi:10.1142/S0129065712500128
Mohemmed A, Schliebs S, Matsuda S, Kasabov N (2012) Training spiking neural networks to associate spatio-temporal input–output spike patterns Neurocomputing 0, doi:10.1016/j.neucom.2012.08.034
Muller L, Destexhe A (2012) Propagating waves in thalamus, cortex and the thalamocortical system: Experiments and models J Physiol Paris 106(5–6):222–238
Okun M, Yger P, Marguet SL, Gerard-Mercier F, Benucci A, Katzner S, Busse L, Carandini M, Harris KD (2012) Population rate dynamics and multineuron firing patterns in sensory cortex J Neurosci 32(48):17108--17119, doi:10.1523/JNEUROSCI.1831-12.2012
Pernice V, Staude B, Cardanobile S, Rotter S (2012) Recurrent interactions in spiking networks with arbitrary topology Physical Review E, 85(3):031916, doi:10.1103/PhysRevE.85.031916
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 Neuromorphic Engineering 6:90, doi:10.3389/fnins.2012.00090
Phoka E, Wildie M, Schultz SR, Barahona M (2012) Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model J Comput Neurosci (online first), doi:10.1007/s10827-012-0388-6
Potjans TC, Diesmann M (2012) The cell-type specific cortical microcircuit: Relating structure and activity in a full-scale spiking network model Cerebral Cortex (online first), doi:10.1093/cercor/bhs358
Probst D, Maass W, Markram H, Gewaltig MO (2012) Liquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball in Artificial Neural Networks and Machine Learning – ICANN 2012, Springer Berlin Heidelberg, vol. 7552, pgs. 209-216, doi:10.1007/978-3-642-33269-2_27
Rast AD, Navaridas J, Jin X, Galluppi F, Plana, LA, Miguel-Alonso J, Patterson C, Luján M, Furber S (2012) Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System International Journal of Parallel Programming 40:553-582, doi:10.1007/s10766-011-0180-7
Ren Q, Kolwankar KM, Samal A, Jost J (2012) Hopf bifurcation in the evolution of networks driven by spike-timing-dependent plasticity Phys Rev E 86:056103, doi:10.1103/PhysRevE.86.056103
Rougier NP, Fix J (2012) DANA: distributed numerical and adaptive modelling framework Network 23(4):237-253, doi:10.3109/0954898X.2012.721573
Schmitt O, Eipert P (2012) neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome Neuroinformatics (online first), doi:10.1007/s12021-012-9141-6
Stetter O, Battaglia D, Soriano J, Geisel T (2012) Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals PLoS Comput Biol 8(8):e1002653, doi:10.1371/journal.pcbi.1002653
Tetzlaff T, Helias M, Einevoll GT, Diesmann M (2012) Decorrelation of neural-network activity by inhibitory feedback PLoS Comput Biol 8(8): e1002596, doi:10.1371/journal.pcbi.1002596 (preprint with high-quality figures)
Trengove C, van Leeuwen C, Diesmann M (2012) High-capacity embedding of synfire chains in a cortical network model J Comput Neurosci (online first), doi:10.1007/s10827-012-0413-9
Vlachos I, Aertsen A, Kumar A (2012) Beyond statistical significance: Implications of network structure on neural activity PLoS Comp Bio 8(1):e1002311, doi:10.1371/journal.pcbi.1002311
Voges N, Perrinet L (2012) Complex dynamics in recurrent cortical networks based on spatially realistic connectivities Front Comput Neurosci 6:41, doi:10.3389/fncom.2012.00041
Waddington A, Appleby PA, De Kamps M, Cohen N (2012) Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity Front Comput Neurosci 6:88, doi:10.3389/fncom.2012.00088
Yen Y, Chen L, Huang Y, Lo CC (2012) Models of cortico-basal ganglia circuits and synaptic plasticity for transcranial magnetic stimulation in Complex Medical Engineering (CME), 2012 ICME International Conference on, pgs. 96-100, doi:10.1109/ICCME.2012.6275678
Yuan CW, Leibold C (2012) Recurrent coupling improves discrimination of temporal spike patterns Front Comput Neurosci 6:25, doi:10.3389/fncom.2012.00025


Yim MY, Aertsen A, Kumar A (2011) Significance of input correlations in striatum function PLoS Comp Bio 7(11):e1002254
Kumar A, Cardanobile S, Rotter S, Aertsen A (2011) The role of inhibition in generating and controlling oscillations in the basal ganglia related to parkinson's disease Front Syst Neurosci 5:86
Mohemmed A, Schliebs S, Matsuda Satoshi, Kasabov N (2011) Method for Training a Spiking Neuron to Associate Input-Output Spike Trains in Iliadis L, Jayne C (Eds) Engineering Applications of Neural Networks, IFIP Advances in Information and Communication Technology, Springer Berlin Heidelberg, ISBN:978-3-642-23956-4 vol. 363, pg. 219-228, doi:10.1007/978-3-642-23957-1_25
Deger M, Helias M, Boucsein C, Rotter S (2011) Statistical properties of superimposed stationary spike trains J Comput Neurosci 32(3):443-463, doi: 10.1007/s10827-011-0362-8
Simonov AY, Kazantsev VB (2011) Model of the appearance of avalanche bioelectric discharges in neural networks of the brain JETP Letters 93(8):470-475
Linden H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M, Einevoll GT (2011) Modeling the spatial reach of the LFP Neuron 72(5):859-872. doi: 10.1016/j.neuron.2011.11.006
Hanuschkin A, Diesmann M, Morrison A (2011) A reafferent and feed-forward model of song syntax generation in the Bengalese finch J Comput Neurosci 31(3):509-532. doi: 10.1007/s10827-011-0318-z
Wagatsuma N, Potjans TC, Diesmann M, Fukai T (2011) Layer-dependent attentional processing by top-down signals in a visual cortical microcircuit model Front Comput Neurosci 5:31. doi: 10.3389/fncom.2011.00031
Yamauchi S, Kim H and Shinomoto S (2011) Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times Front Comput Neurosci 5:42. doi: 10.3389/fncom.2011.00042
Henker S, Partzsch J, Schüffny R (2011) Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks J Comput Neurosci doi: 10.1007/s10827-011-0353-9
Vlachos I, Herry C, Lüthi A, Aertsen A, Kumar A (2011) Context-Dependent Encoding of Fear and Extinction Memories in a Large-Scale Network Model of the Basal Amygdala. PLoS Comput Biol 7(3): e1001104. doi:10.1371/journal.pcbi.1001104
Kunkel S, Diesmann M, Morrison A (2011) Limits to the development of feed-forward structures in large recurrent neuronal networks Front. Comput. Neurosci. 4:160. doi: 10.3389/fncom.2010.00160
Brüderle D, Petrovici M, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grübl A, Wendt K, Müller E, Schwartz MO, de Oliveira D, Jeltsch S, Fieres J, Schilling M, Müller P, Breitwieser O, Petkov V, Muller L, Davison A, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zühl L, Mayr C, Destexhe A, Diesmann M, Potjans T, Lansner A, Schüffny R, Schemmel J, Meier K (2011) A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems Biol Cybern 104(4-5):263-296
Indiveri G, Linares-Barranco B, Hamilton TJ, van Schaik A, Etienne-Cummings R, Delbruck T, Liu SC, Dudek P, Häfliger P, Renaud S, Schemmel J, Cauwenberghs C, Arthur J, Hynna K, Folowosele F, Saighi S, Serrano-Gotarredona T, Wijekoon J, Wang Y, Boahen K (2011) Neuromorphic silicon neuron circuits Front. Neurosci. 5:73. doi: 10.3389/fnins.2011.00073
Potjans W, Diesmann M, Morrison A (2011) An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning PLoS Comput Biol 7(5): e1001133
Golloa LL, Mirassoa C and Villab AEP (2011) Dynamic control for synchronization of separated cortical areas through thalamic relay NeuroImage 52(3): 947-955
Helias et al. (2011) Finite post synaptic potentials cause a fast neuronal response Front Neurosci 5:19
Hanuschkin A, Herrmann J M, Morrison A and Diesmann M (2011) Compositionality of arm movements can be realized by propagating synchrony. Journal of Computational Neuroscience. doi:10.1007/s10827-010-0285-9


Kremkow J, Aertsen A, Kumar A (2010) Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition J Neurosci 30:15760–8
Deger M, Helias M, Cardanobile S, Atay FM, Rotter S (2010) Nonequilibrium dynamics of stochastic point processes with refractoriness PRE 82(2):021129, doi: 10.1103/PhysRevE.82.021129
Padraig Gleeson, R. Angus Silver and Volker Steuber (2010) Computer Simulation Environments in Vassilis Cutsuridis, Bruce Graham, Stuart Cobb and Imre Vida (Eds) Hippocampal Microcircuits: A Computational Modeler’s Resource Book Springer Series in Computational Neuroscience, 2010, Volume 5, Part II, 593-609, DOI: 10.1007/978-1-4419-0996-1_2
Nordlie E, Tetzlaff T, Einevoll GT (2010) Rate dynamics of leaky integrate-and-fire neurons with strong synapses. Front. Comput. Neurosci. 4:149, doi:10.3389/fncom.2010.00149
Yger P, Boustani S E, Destexhe A and Frégnac Y (2010) Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons. Journal of Computational Neuroscience DOI: 10.1007/s10827-010-0310-z
Schrader S, Diesmann M, Morrison A (2010) A compositionality machine realized by a hierarchic architecture of synfire chains. Front. Comput. Neurosci. 4:154
Bruederle et al. (2010) A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems arXiv:1011.2861v1
Potjans W, Morrison A and Diesmann M (2010). Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Front. Comput. Neurosci. 4:141
Moritz Helias, Moritz Deger, Stefan Rotter, Markus Diesmann (2010) Instantaneous Non-Linear Processing by Pulse-Coupled Threshold Units PLoS Comput Biol 6(9): e1000929
Alexander Hanuschkin, Susanne Kunkel, Moritz Helias, Abigail Morrison and Markus Diesmann (2010) A general and efficient method for incorporating precise spike times in globally time-driven simulations Front. Neuroinform. 4:113. doi:10.3389/fninf.2010.00113
Moritz Helias, Moritz Deger, Markus Diesmann and Stefan Rotter (2010) Equilibrium and response properties of the integrate-and-fire neuron in discrete time Front. Comput. Neurosci., doi: 10.3389/neuro.10.029.2009
Nordlie E, Plesser HE (2010) Visualizing neuronal network connectivity with connectivity pattern tables Front Neuroinform 3:39
Mikael Djurfeldt, Johannes Hjorth, Jochen M. Eppler, Niraj Dudani, Moritz Helias, Tobias C. Potjans, Upinder S. Bhalla, Markus Diesmann, Jeanette Hellgren Kotaleski and Örjan Ekeberg (2010) Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework Neuroinformatics 8(1):43-60, DOI 10.1007/s12021-010-9064-z
Andrew Davison, Eilif Muller, Daniel Brüderle, and Jens Kremkow (2010) A common language for neuronal networks in software and hardware Institute of Neuromorphic Engineering, DOI: 10.2417/1201001.1712


Jochen M. Eppler, A Python interface to NEST Institute of Neuromorphic Engineering, DOI: 10.2417/1200912.1703
Olivier Marre, Pierre Yger, Andrew P. Davison, and Yves Frégnac, Reliable Recall of Spontaneous Activity Patterns in Cortical Networks, J. Neurosci..2009; 29: 14596-14606
Goodman DF and Brette R (2009) The Brian simulator. Front. Neurosci.doi:10.3389/neuro.01.026.2009
Boustani SE, Marre O, Béhuret S, Baudot P, Yger P, Bal T, Destexhe A, Frégnac Y (2009) Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons PLoS Comput Biol 5(9): e1000519
Schrader S, Gewaltig MO, Koerner U, Koerner E, (2009) Cortext: A columnar model of bottom-up and top-down processing in the neocortex Neural Networks, in press
Nordlie E, Gewaltig M-O, Plesser HE (2009) Towards reproducible descriptions of neuronal network models PLoS Comput Biol 5(8):e1000456
Plesser HE, Diesmann M (2009) Simplicity and efficiency of integrate-and-fire neuron models Neural Comput 21(2):353-359
Potjans W, Morrison A, Diesmann M (2009) A spiking neural network model of an actor-critic learning agent Neural Comput 21(2):301-39


Schrader S, Grün S, Diesmann M, Gerstein GL (2008) Detecting synfire chain activity using massively parallel spike train recording J Neurophysiol 100(4):2165-76
De Schutter E (2008) Why Are Computational Neuroscience and Systems Biology So Separate? PLoS Comput Biol 4(5): e1000078
Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg O, Lansner A (2008) Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer IBM Journal of Research and Development 52(1-2):31-42
Davison AP, Brüderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L and Yger P (2009). 'PyNN: a common interface for neuronal network simulators Front. Neuroinform. 2:11
Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO (2009) PyNEST: a convenient interface to the NEST simulator Front Neuroinformatics (2008) 2:12 pdf
Goedeke S, Diesmann M (2008) The mechanism of synchronization in feed-forward neuronal networks New J Phys 10(1):015007 (10pp)
Helias M, Rotter S, Gewaltig MO, Diesmann M (2008) Structural plasticity controlled by calcium based correlation detection Front Comput Neurosci 2:7
Kriener B, Tetzlaff T, Aertsen A, Diesmann M, Rotter S (2008) Correlations and population dynamics in cortical networks Neural Comput 20(9):2185-226 pdf
Kumar A, Rotter S, Aertsen A (2008) Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model J Neurosci 28(20):5268-80 pdf
Kumar A, Schrader S, Aertsen A, Rotter S (2008) The high-conductance state of cortical networks Neural Comput 20(1):1-43 pdf
Morrison A, Diesmann M (2008) Maintaining causality in discrete time neuronal network simulations In: Beim Graben P et al. eds. Lectures in supercomputational neuroscience: dynamics in complex brain networks Chapter IV.10 p. 267-278. Springer preprint
Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing Biol Cybern 98(6):459-78
Scorcioni R, Hamilton DJ, Ascoli GA (2008) Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit Neural Netw 21(8):1153-63.
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 Comput 20(9):2133-2184 pdf


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, Lansner 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 J Comput Neurosci 23(3):349-98
Bruederle D, Gruebl A, Meier K, Mueller E, Schemmel J (2007) A software framework for tuning the dynamics of neuromorphic silicon towards biology In: Proc. of IWANN 2007, Springer LNCS 4507:479-486 pdf
Cannon RC, Gewaltig MO, Gleeson P, Bhalla US, Cornelis H, Hines ML, Howell FW, Muller E, Stiles JR, Wils S, De Schutter E (2007) Interoperability of neuroscience modeling software: current status and future directions Neuroinformatics 5(2):127-38
Djurfeldt M, Lansner A (2007) Report of the 1st INCF workshop on large-scale modeling of the nervous system pdf
Eppler JM, Plesser HE, Morrison A, Diesmann M, Gewaltig MO (2007) Multithreaded and distributed simulation of large biological neuronal networks In: Proc. of EuroPVM/MPI 2007, Springer LNCS 4757:391-392
Gewaltig MO, Diesmann M (2007) NEST Scholarpedia 2(4):1430
Gleeson P, Steuber V, Silver A (2007) neuroConstruct: A tool for modeling networks of neurons in 3D space Neuron 54:219-235, doi:10.1016/j.neuron.2007.03.025
Morrison A, Aertsen A, Diesmann M (2007) Spike-timing-dependent plasticity in balanced random networks Neural Comput 19(6):1437-67 pdf
Morrison A, Straube S, Plesser HE, Diesmann M (2007) Exact subthreshold integration with continuous spike times in discrete-time neural network simulations Neural Comput 19(1):47-79 pdf
Muller E, Buesing L, Schemmel J, Meier K (2007) Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theories Neural Comput 19(11):2958-3010
Plesser HE, Eppler JM, Morrison A, Diesmann M, Gewaltig MO (2007) Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers In: Proc. of Euro-Par Parallel Processing 2007, Springer LNCS 4641:672-681


Guerrero-Rivera R, Morrison A, Diesmann M, Pearce, T C (2006) Programmable Logic Construction Kits for Hyper Real-time Neuronal Modeling Neural Computation 18:2651--2679
Backofen R, Borrmann HG, Deck W, Dedner A, De Raedt L, Desch K, Diesmann M, Geier M, Greiner A, Hess WR, Honerkamp J, Jankowski S, Krossing I, Liehr AW, Karwath A, Kloefkorn R, Pesche R, Potjans T, Roettger MC, Schmiedt-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 29:81--87
Gewaltig M-O and Diesmann M (2006) Exploring large-scale models of neural systems with the Neural Simulation Tool NEST Computational Neuroscience Meeting CNS*06, S49, Edinburgh, UK
Eppler J M, Morrison M, Diesmann M, Plesser, H E, and Gewaltig, M-O Parallel and Distributed Simulation of Large Biological Neural Networks with NEST Computational Neuroscience Meeting CNS*06, S48, Edingburgh, UK
Morrison A, Aertsen A and Diesmann M (2006) Spike-timing dependent plasticity in balanced random networks Computational Neuroscience Meeting CNS*06, T79, Edinburgh, UK
Plesser, H E, Morrison A, Straube S, and Diesmann M (2006) Precise and efficient discrete time neural network simulation Computational Neuroscience Meeting CNS*06, S51, Edinburgh, UK


Goedeke, S.; Geisel, T. & Diesmann, M. (2005) On neuronal mechanisms determining synchronization dynamics in cortical feed-forward networks. In: Zimmermann, H. & Kriegelstein, K. (ed.) Proceedings of the 30th Göttingen Neurobiology Conference, Neuroforum Supplement 1, 210B pdf
Mayor, J. & Gerstner, W. (2005) Noise-enhanced computation in a model of a cortical column. Neuroreport 16 : 1237-1240 pdf
Morrison, A.; Hake, J.; Straube, S.; Plesser, H.E. & Diesmann, M. (2005) Precise spike timing with exact subthreshold integration in discrete time network simulations. Proceedings of the 30th Göttingen Neurobiology Conference, 205B pdf
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 abstract
Schöner, D.; Tetzlaff, T.; Aertsen, A. & Diesmann, M. (2005) Dynamics of rate and correlation in balanced random networks. Proceedings of the 30th Göttingen Neurobiology Conference, pdf
Tetzlaff, T.; Morrison, A.; Timme, M. & Diesmann, M. (2005) Heterogeneity breaks global synchrony in large networks. Proceedings of the 30th Göttingen Neurobiology Conference, pdf


Morrison, A.; Aertsen, A. & Diesmann, M. (2004) Stability of Plastic Recurrent Networks. The Monte Verita Workshop on Spike-Timing Dependent Plasticity (STDP)
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
Veredas, F. J., Vico, F. J. & Alonso, J. M (2004) A computational tool to simulate correlated activity in neural circuits Journal of Neuroscience Methods 136: 23–32 pdf


Aviel, Y.; Mehring, C.; Abeles, M. & Horn, D. (2003) On embedding synfire chains in a balanced network. Neural computation 15 : 1321-1340 abstract
Schmucker, M.; Körner, U.; Körner, E.; Gewaltig, M. & Wachtler, T. (2003) A model of rapid surface detection in primate visual cortex. In: Elsner, N. & Zimmermann, H. (ed.) The Neurosciences from Basic Research to Therapy: Proceedings of the 29th Göttingen Neurobiology Conference, 666 , Thieme
Tetzlaff, T.; Buschermöhle, M.; Geisel, T. & Diesmann, M. (2003) The spread of rate and correlation in stationary cortical networks. Neurocomputing 52--54 : 949-954 abstract


Diesmann, M. & Gewaltig, M. (2002) NEST: An Environment for Neural Systems Simulations. In: Plesser, T. & Macho, V. (ed.) Forschung und wisschenschaftliches Rechnen, Beiträge zum Heinz-Billing-Preis 2001, 58 : 43-70 , Ges. für Wiss. Datenverarbeitung pdf
Marc-Oliver Gewaltig; Ursula Körner & Edgar Körner (2002) A model of surface detection and orientation tuning in primate visual cortex. In: Eric de Schutter (ed.) Computational Neuroscience: Trends in Research 2002, Elsevier Science
Marc-Oliver Gewaltig; Andreas Richter & Rüdiger Kupper (2002) BLISS: Towards the simulation of brain-like systems. Neurocomputing 44--46 : 805-810 abstract
Tetzlaff, T.; Geisel, T. & Diesmann, M. (2002) The ground state of cortical feed-forward networks. Neurocomputing 44--46 : 673-678 abstract


Diesmann, M.; Gewaltig, M.; Rotter, S. & Aertsen, A. (2001) State space analysis of synchronous spiking in cortical neural networks. Neurocomputing 38--40 : 565-571 abstract
Gewaltig, M.; Diesmann, M. & Aertsen, A. (2001) Propagation of cortical synfire activity: survival probability in single trials and stability in the mean. Neural networks : the official journal of the International Neural Network Society 657-673 pdf


Gewaltig, M. (2000) Evolution of Synchronous Spike Volleys in Cortical Networks -- Network Simulations and Continuous Probabilistic Models. , Shaker , ISBN-10: 3826575091


Diesmann M, Gewaltig M-O, Aertsen A. (1999) Stable propagation of synchronous spiking in cortical neural networks Nature 402, 529-533 (2 December 1999), doi:10.1038/990101
Rotter S, Diesmann M (1999) Exact Digital Simulation of Time-Invariant Linear Systems with Applications to Neuronal Modeling Biological Cybernetics 81:381-402 abstract


Xavier Giannakopoulos (1997) Study of a Neuromimetical Network for Recognizing Complex Sounds. pdf


Diesmann, M.; Gewaltig, M. & Aertsen, A. (1995) SYNOD: an Environment for Neural Systems Simulations. Language Interface and Tutorial. , Weizmann Institute of Science pdf
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