| Year |
Citation |
Score |
| 2020 |
Jordan J, Helias M, Diesmann M, Kunkel S. Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Frontiers in Neuroinformatics. 14: 12. PMID 32431602 DOI: 10.3389/fninf.2020.00012 |
0.72 |
|
| 2020 |
Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Correction to: Multi-scale account of the network structure of macaque visual cortex. Brain Structure & Function. PMID 32052112 DOI: 10.1007/s00429-019-02020-6 |
0.32 |
|
| 2019 |
Jordan J, Petrovici MA, Breitwieser O, Schemmel J, Meier K, Diesmann M, Tetzlaff T. Deterministic networks for probabilistic computing. Scientific Reports. 9: 18303. PMID 31797943 DOI: 10.1038/s41598-019-54137-7 |
0.64 |
|
| 2019 |
Dahmen D, Grün S, Diesmann M, Helias M. 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. PMID 31189590 DOI: 10.1073/pnas.1818972116 |
0.88 |
|
| 2019 |
Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness TV, Plesser HE, Schürmann F. The Scientific Case for Brain Simulations. Neuron. 102: 735-744. PMID 31121126 DOI: 10.1016/j.neuron.2019.03.027 |
0.88 |
|
| 2018 |
Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, et al. Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214. PMID 30976604 DOI: 10.1146/annurev-statistics-041715-033733 |
0.88 |
|
| 2018 |
Senk J, Carde C, Hagen E, Kuhlen TW, Diesmann M, Weyers B. VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output. Frontiers in Neuroinformatics. 12: 75. PMID 30467469 DOI: 10.3389/fninf.2018.00075 |
0.32 |
|
| 2018 |
Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. Plos Computational Biology. 14: e1006359. PMID 30335761 DOI: 10.1371/journal.pcbi.1006359 |
0.32 |
|
| 2018 |
Maksimov A, Diesmann M, van Albada SJ. Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models. Frontiers in Computational Neuroscience. 12: 44. PMID 30042668 DOI: 10.3389/fncom.2018.00044 |
0.32 |
|
| 2018 |
Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. Corrigendum: Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics. 12: 34. PMID 30008668 DOI: 10.3389/fninf.2018.00034 |
0.72 |
|
| 2018 |
van Albada SJ, Rowley AG, Senk J, Hopkins M, Schmidt M, Stokes AB, Lester DR, Diesmann M, Furber SB. 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. PMID 29875620 DOI: 10.3389/fnins.2018.00291 |
0.32 |
|
| 2018 |
Denker M, Zehl L, Kilavik BE, Diesmann M, Brochier T, Riehle A, Grün S. LFP beta amplitude is linked to mesoscopic spatio-temporal phase patterns. Scientific Reports. 8: 5200. PMID 29581430 DOI: 10.1038/s41598-018-22990-7 |
0.88 |
|
| 2018 |
Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics. 12: 2. PMID 29503613 DOI: 10.3389/fninf.2018.00002 |
0.72 |
|
| 2017 |
Krishnan J, Porta Mana P, Helias M, Diesmann M, Di Napoli E. Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons. Frontiers in Neuroinformatics. 11: 75. PMID 29379430 DOI: 10.3389/fninf.2017.00075 |
0.72 |
|
| 2017 |
Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Multi-scale account of the network structure of macaque visual cortex. Brain Structure & Function. PMID 29143946 DOI: 10.1007/s00429-017-1554-4 |
0.32 |
|
| 2017 |
Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M, Diesmann M. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator. Frontiers in Neuroinformatics. 11: 34. PMID 28596730 DOI: 10.3389/fninf.2017.00034 |
0.72 |
|
| 2017 |
Ippen T, Eppler JM, Plesser HE, Diesmann M. Constructing Neuronal Network Models in Massively Parallel Environments. Frontiers in Neuroinformatics. 11: 30. PMID 28559808 DOI: 10.3389/fninf.2017.00030 |
0.72 |
|
| 2017 |
Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M. Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. Plos Computational Biology. 13: e1005179. PMID 28146554 DOI: 10.1371/journal.pcbi.1005179 |
0.72 |
|
| 2016 |
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. High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. Neuron. 92: 628-631. PMID 27810006 DOI: 10.1016/j.neuron.2016.10.035 |
0.68 |
|
| 2016 |
Hagen E, Dahmen D, Stavrinou ML, Lindén H, Tetzlaff T, van Albada SJ, Grün S, Diesmann M, Einevoll GT. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. Cerebral Cortex (New York, N.Y. : 1991). PMID 27797828 DOI: 10.1093/cercor/bhw237 |
0.88 |
|
| 2016 |
Bos H, Diesmann M, Helias M. Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. Plos Computational Biology. 12: e1005132. PMID 27736873 DOI: 10.1371/journal.pcbi.1005132 |
0.72 |
|
| 2016 |
Grytskyy D, Diesmann M, Helias M. Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality. Physical Review. E. 93: 062303. PMID 27415276 DOI: 10.1103/PhysRevE.93.062303 |
0.72 |
|
| 2015 |
Schuecker J, Diesmann M, Helias M. Modulated escape from a metastable state driven by colored noise. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 92: 052119. PMID 26651659 DOI: 10.1103/PhysRevE.92.052119 |
1 |
|
| 2015 |
Trengove C, Diesmann M, Leeuwen CV. Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains. Journal of Computational Neuroscience. PMID 26560334 DOI: 10.1007/s10827-015-0581-5 |
1 |
|
| 2015 |
Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics. 9: 22. PMID 26441628 DOI: 10.3389/fninf.2015.00022 |
1 |
|
| 2015 |
van Albada SJ, Helias M, Diesmann M. Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. Plos Computational Biology. 11: e1004490. PMID 26325661 DOI: 10.1371/journal.pcbi.1004490 |
1 |
|
| 2015 |
Muller E, Bednar JA, Diesmann M, Gewaltig MO, Hines M, Davison AP. Python in neuroscience. Frontiers in Neuroinformatics. 9: 11. PMID 25926788 DOI: 10.3389/fninf.2015.00011 |
1 |
|
| 2014 |
Kunkel S, Schmidt M, Eppler JM, Plesser HE, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A, Diesmann M, Helias M. Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics. 8: 78. PMID 25346682 DOI: 10.3389/fninf.2014.00078 |
1 |
|
| 2014 |
Helias M, Tetzlaff T, Diesmann M. The correlation structure of local neuronal networks intrinsically results from recurrent dynamics. Plos Computational Biology. 10: e1003428. PMID 24453955 DOI: 10.1371/journal.pcbi.1003428 |
1 |
|
| 2014 |
Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex (New York, N.Y. : 1991). 24: 785-806. PMID 23203991 DOI: 10.1093/cercor/bhs358 |
1 |
|
| 2014 |
van Albada SJ, Kunkel S, Morrison A, Morriso A, Diesmann M. Integrating brain structure and dynamics on supercomputers Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8603: 22-32. DOI: 10.1007/978-3-319-12084-3_3 |
1 |
|
| 2013 |
Kriener B, Helias M, Rotter S, Diesmann M, Einevoll GT. How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime. Frontiers in Computational Neuroscience. 7: 187. PMID 24501591 DOI: 10.3389/fncom.2013.00187 |
1 |
|
| 2013 |
Wagatsuma N, Potjans TC, Diesmann M, Sakai K, Fukai T. Spatial and feature-based attention in a layered cortical microcircuit model. Plos One. 8: e80788. PMID 24324628 DOI: 10.1371/journal.pone.0080788 |
1 |
|
| 2013 |
Grytskyy D, Tetzlaff T, Diesmann M, Helias M. A unified view on weakly correlated recurrent networks. Frontiers in Computational Neuroscience. 7: 131. PMID 24151463 DOI: 10.3389/fncom.2013.00131 |
1 |
|
| 2013 |
Abeles M, Diesmann M, Flash T, Geisel T, Herrmann M, Teicher M. Compositionality in neural control: an interdisciplinary study of scribbling movements in primates. Frontiers in Computational Neuroscience. 7: 103. PMID 24062679 DOI: 10.3389/fncom.2013.00103 |
1 |
|
| 2013 |
Vlachos A, Helias M, Becker D, Diesmann M, Deller T. 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: 267-76. PMID 23932917 DOI: 10.1016/j.nbd.2013.07.018 |
1 |
|
| 2013 |
Schultze-Kraft M, Diesmann M, Grün S, Helias M. Noise suppression and surplus synchrony by coincidence detection. Plos Computational Biology. 9: e1002904. PMID 23592953 DOI: 10.1371/journal.pcbi.1002904 |
1 |
|
| 2013 |
Trengove C, van Leeuwen C, Diesmann M. High-capacity embedding of synfire chains in a cortical network model. Journal of Computational Neuroscience. 34: 185-209. PMID 22878688 DOI: 10.1007/s10827-012-0413-9 |
1 |
|
| 2013 |
Helias M, Tetzlaff T, Diesmann M. Echoes in correlated neural systems New Journal of Physics. 15. DOI: 10.1088/1367-2630/15/2/023002 |
1 |
|
| 2012 |
Bakker R, Wachtler T, Diesmann M. CoCoMac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics. 6: 30. PMID 23293600 DOI: 10.3389/fninf.2012.00030 |
1 |
|
| 2012 |
Tetzlaff T, Helias M, Einevoll GT, Diesmann M. Decorrelation of neural-network activity by inhibitory feedback. Plos Computational Biology. 8: e1002596. PMID 23133368 DOI: 10.1371/journal.pcbi.1002596 |
1 |
|
| 2012 |
Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A, Diesmann M. Supercomputers ready for use as discovery machines for neuroscience. Frontiers in Neuroinformatics. 6: 26. PMID 23129998 DOI: 10.3389/fninf.2012.00026 |
1 |
|
| 2012 |
Deger M, Helias M, Rotter S, Diesmann M. Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex. Plos Computational Biology. 8: e1002689. PMID 23028287 DOI: 10.1371/journal.pcbi.1002689 |
1 |
|
| 2012 |
Pfeil T, Potjans TC, Schrader S, Potjans W, Schemmel J, Diesmann M, Meier K. Is a 4-bit synaptic weight resolution enough? - constraints on enabling spike-timing dependent plasticity in neuromorphic hardware. Frontiers in Neuroscience. 6: 90. PMID 22822388 DOI: 10.3389/fnins.2012.00090 |
1 |
|
| 2012 |
Gerstein GL, Williams ER, Diesmann M, Grün S, Trengove C. Detecting synfire chains in parallel spike data. Journal of Neuroscience Methods. 206: 54-64. PMID 22361572 DOI: 10.1016/j.jneumeth.2012.02.003 |
1 |
|
| 2011 |
Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A, Diesmann M. Meeting the memory challenges of brain-scale network simulation. Frontiers in Neuroinformatics. 5: 35. PMID 22291636 DOI: 10.3389/fninf.2011.00035 |
1 |
|
| 2011 |
Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M, Einevoll GT. Modeling the spatial reach of the LFP. Neuron. 72: 859-72. PMID 22153380 DOI: 10.1016/j.neuron.2011.11.006 |
1 |
|
| 2011 |
Ishii S, Diesmann M, Doya K. Multi-scale, multi-modal neural modeling and simulation. Neural Networks : the Official Journal of the International Neural Network Society. 24: 917. PMID 21840687 DOI: 10.1016/j.neunet.2011.07.004 |
1 |
|
| 2011 |
Wagatsuma N, Potjans TC, Diesmann M, Fukai T. Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model. Frontiers in Computational Neuroscience. 5: 31. PMID 21779240 DOI: 10.3389/fncom.2011.00031 |
1 |
|
| 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, ... ... Diesmann M, et al. A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biological Cybernetics. 104: 263-96. PMID 21618053 DOI: 10.1007/s00422-011-0435-9 |
1 |
|
| 2011 |
Potjans W, Diesmann M, Morrison A. An imperfect dopaminergic error signal can drive temporal-difference learning. Plos Computational Biology. 7: e1001133. PMID 21589888 DOI: 10.1371/journal.pcbi.1001133 |
1 |
|
| 2011 |
Denker M, Roux S, Lindén H, Diesmann M, Riehle A, Grün S. The local field potential reflects surplus spike synchrony. Cerebral Cortex (New York, N.Y. : 1991). 21: 2681-95. PMID 21508303 DOI: 10.1093/cercor/bhr040 |
1 |
|
| 2011 |
Helias M, Deger M, Rotter S, Diesmann M. Finite post synaptic potentials cause a fast neuronal response. Frontiers in Neuroscience. 5: 19. PMID 21427776 DOI: 10.3389/fnins.2011.00019 |
1 |
|
| 2011 |
Kunkel S, Diesmann M, Morrison A. Limits to the development of feed-forward structures in large recurrent neuronal networks. Frontiers in Computational Neuroscience. 4: 160. PMID 21415913 DOI: 10.3389/fncom.2010.00160 |
1 |
|
| 2011 |
Hanuschkin A, Diesmann M, Morrison A. A reafferent and feed-forward model of song syntax generation in the Bengalese finch. Journal of Computational Neuroscience. 31: 509-32. PMID 21404048 DOI: 10.1007/s10827-011-0318-z |
1 |
|
| 2011 |
von Kapri A, Rick T, Potjans TC, Diesmann M, Kuhlen T. Towards the visualization of spiking neurons in virtual reality. Studies in Health Technology and Informatics. 163: 685-7. PMID 21335880 DOI: 10.3233/978-1-60750-706-2-685 |
1 |
|
| 2011 |
Schrader S, Diesmann M, Morrison A. A compositionality machine realized by a hierarchic architecture of synfire chains. Frontiers in Computational Neuroscience. 4: 154. PMID 21258641 DOI: 10.3389/fncom.2010.00154 |
1 |
|
| 2011 |
Hanuschkin A, Herrmann JM, Morrison A, Diesmann M. Compositionality of arm movements can be realized by propagating synchrony. Journal of Computational Neuroscience. 30: 675-97. PMID 20953686 DOI: 10.1007/s10827-010-0285-9 |
1 |
|
| 2011 |
Kilavik BE, Confais J, Ponce-Alvarez A, Diesmann M, Riehle A. Evoked potentials in motor cortical local field potentials reflect task timing and behavioral performance (Journal of Neurophysiology (2010) 104 (2338-2351) DOI:10.1152/jn.00250.2010) Journal of Neurophysiology. 105: 501. DOI: 10.1152/jn-z9k-0551-corr.2011 |
1 |
|
| 2010 |
Potjans W, Morrison A, Diesmann M. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Frontiers in Computational Neuroscience. 4: 141. PMID 21151370 DOI: 10.3389/fncom.2010.00141 |
1 |
|
| 2010 |
Louis S, Gerstein GL, Grün S, Diesmann M. Surrogate spike train generation through dithering in operational time. Frontiers in Computational Neuroscience. 4: 127. PMID 21060802 DOI: 10.3389/fncom.2010.00127 |
1 |
|
| 2010 |
Hanuschkin A, Kunkel S, Helias M, Morrison A, Diesmann M. A general and efficient method for incorporating precise spike times in globally time-driven simulations. Frontiers in Neuroinformatics. 4: 113. PMID 21031031 DOI: 10.3389/fninf.2010.00113 |
1 |
|
| 2010 |
Kilavik BE, Confais J, Ponce-Alvarez A, Diesmann M, Riehle A. Evoked potentials in motor cortical local field potentials reflect task timing and behavioral performance. Journal of Neurophysiology. 104: 2338-51. PMID 20884766 DOI: 10.1152/jn.00250.2010 |
1 |
|
| 2010 |
Helias M, Deger M, Rotter S, Diesmann M. Instantaneous non-linear processing by pulse-coupled threshold units. Plos Computational Biology. 6. PMID 20856583 DOI: 10.1371/journal.pcbi.1000929 |
1 |
|
| 2010 |
Denker M, Riehle A, Diesmann M, Grün S. Estimating the contribution of assembly activity to cortical dynamics from spike and population measures. Journal of Computational Neuroscience. 29: 599-613. PMID 20480218 DOI: 10.1007/s10827-010-0241-8 |
1 |
|
| 2010 |
Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Kotaleski JH, Ekeberg O. Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics. 8: 43-60. PMID 20195795 DOI: 10.1007/s12021-010-9064-z |
1 |
|
| 2010 |
Helias M, Deger M, Diesmann M, Rotter S. Equilibrium and Response Properties of the Integrate-and-Fire Neuron in Discrete Time. Frontiers in Computational Neuroscience. 3: 29. PMID 20130755 DOI: 10.3389/neuro.10.029.2009 |
1 |
|
| 2009 |
Plesser HE, Diesmann M. Simplicity and efficiency of integrate-and-fire neuron models. Neural Computation. 21: 353-9. PMID 19431263 DOI: 10.1162/neco.2008.03-08-731 |
1 |
|
| 2009 |
Potjans W, Morrison A, Diesmann M. A spiking neural network model of an actor-critic learning agent. Neural Computation. 21: 301-39. PMID 19196231 DOI: 10.1162/neco.2008.08-07-593 |
1 |
|
| 2008 |
Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO. PyNEST: A Convenient Interface to the NEST Simulator. Frontiers in Neuroinformatics. 2: 12. PMID 19198667 DOI: 10.3389/neuro.11.012.2008 |
1 |
|
| 2008 |
Helias M, Rotter S, Gewaltig MO, Diesmann M. Structural plasticity controlled by calcium based correlation detection. helias@bccn.uni-freiburg.de. Frontiers in Computational Neuroscience. 2: 7. PMID 19129936 DOI: 10.3389/neuro.10.007.2008 |
1 |
|
| 2008 |
Schrader S, Grün S, Diesmann M, Gerstein GL. Detecting synfire chain activity using massively parallel spike train recording. Journal of Neurophysiology. 100: 2165-76. PMID 18632888 DOI: 10.1152/jn.01245.2007 |
1 |
|
| 2008 |
Pazienti A, Maldonado PE, Diesmann M, Grün S. Effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Research. 1225: 39-46. PMID 18547547 DOI: 10.1016/j.brainres.2008.04.073 |
1 |
|
| 2008 |
Morrison A, Diesmann M, Gerstner W. Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics. 98: 459-78. PMID 18491160 DOI: 10.1007/s00422-008-0233-1 |
1 |
|
| 2008 |
Kriener B, Tetzlaff T, Aertsen A, Diesmann M, Rotter S. Correlations and population dynamics in cortical networks. Neural Computation. 20: 2185-226. PMID 18439141 DOI: 10.1162/neco.2008.02-07-474 |
1 |
|
| 2008 |
Tetzlaff T, Rotter S, Stark E, Abeles M, Aertsen A, Diesmann M. Dependence of neuronal correlations on filter characteristics and marginal spike train statistics. Neural Computation. 20: 2133-84. PMID 18439140 DOI: 10.1162/neco.2008.05-07-525 |
1 |
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| 2008 |
Goedeke S, Diesmann M. The mechanism of synchronization in feed-forward neuronal networks New Journal of Physics. 10. DOI: 10.1088/1367-2630/10/1/015007 |
1 |
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| 2008 |
Grün S, Abeles M, Diesmann M. Impact of higher-order correlations on coincidence distributions of massively parallel data Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5286: 96-114. DOI: 10.1007/978-3-540-88853-6-8 |
1 |
|
| 2007 |
Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, et al. Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience. 23: 349-98. PMID 17629781 DOI: 10.1007/s10827-007-0038-6 |
1 |
|
| 2007 |
Morrison A, Aertsen A, Diesmann M. Spike-timing-dependent plasticity in balanced random networks. Neural Computation. 19: 1437-67. PMID 17444756 DOI: 10.1162/neco.2007.19.6.1437 |
1 |
|
| 2007 |
Morrison A, Straube S, Plesser HE, Diesmann M. Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Computation. 19: 47-79. PMID 17134317 DOI: 10.1162/neco.2007.19.1.47 |
1 |
|
| 2007 |
Pazienti A, Diesmann M, Grün S. Bounds of the ability to destroy precise coincidences by spike dithering Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4729: 428-437. DOI: 10.1007/978-3-540-75555-5_41 |
1 |
|
| 2007 |
Morrison A, Diesmann M. Maintaining causality in discrete time neuronal network simulations Understanding Complex Systems. 2008: 267-278. DOI: 10.1007/978-3-540-73159-7_10 |
1 |
|
| 2007 |
Plesser HE, Eppler JM, Morrison A, Diesmann M, Gewaltig MO. Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4641: 672-681. |
1 |
|
| 2007 |
Eppler JM, Plesser HE, Morrison A, Diesmann M, Gewaltig MO. Multithreaded and distributed simulation of large biological neuronal networks Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4757: 391-392. |
1 |
|
| 2006 |
Guerrero-Rivera R, Morrison A, Diesmann M, Pearce TC. Programmable logic construction kits for hyper-real-time neuronal modeling. Neural Computation. 18: 2651-79. PMID 16999574 DOI: 10.1162/neco.2006.18.11.2651 |
1 |
|
| 2005 |
Morrison A, Mehring C, Geisel T, Aertsen AD, Diesmann M. Advancing the boundaries of high-connectivity network simulation with distributed computing. Neural Computation. 17: 1776-801. PMID 15969917 DOI: 10.1162/0899766054026648 |
1 |
|
| 2004 |
Denker M, Timme M, Diesmann M, Wolf F, Geisel T. Breaking synchrony by heterogeneity in complex networks. Physical Review Letters. 92: 074103. PMID 14995855 |
1 |
|
| 2004 |
Tetzlaff T, Morrison A, Geisel T, Diesmann M. Consequences of realistic network size on the stability of embedded synfire chains Neurocomputing. 58: 117-121. DOI: 10.1016/j.neucom.2004.01.031 |
1 |
|
| 2003 |
Mehring C, Hehl U, Kubo M, Diesmann M, Aertsen A. Activity dynamics and propagation of synchronous spiking in locally connected random networks. Biological Cybernetics. 88: 395-408. PMID 12750902 DOI: 10.1007/s00422-002-0384-4 |
1 |
|
| 2003 |
Grün S, Riehle A, Diesmann M. Effect of cross-trial nonstationarity on joint-spike events. Biological Cybernetics. 88: 335-51. PMID 12750896 DOI: 10.1007/s00422-002-0386-2 |
1 |
|
| 2003 |
Pipa G, Diesmann M, Grün S. Significance of joint-spike events based on trial-shuffling by efficient combinatorial methods Complexity. 8: 79-86. DOI: 10.1002/cplx.10085 |
1 |
|
| 2003 |
Tetzlaff T, Buschermöhle M, Geisel T, Diesmann M. The spread of rate and correlation in stationary cortical networks Neurocomputing. 52: 949-954. |
1 |
|
| 2002 |
Egert U, Knott T, Schwarz C, Nawrot M, Brandt A, Rotter S, Diesmann M. MEA-Tools: an open source toolbox for the analysis of multi-electrode data with MATLAB. Journal of Neuroscience Methods. 117: 33-42. PMID 12084562 DOI: 10.1016/S0165-0270(02)00045-6 |
1 |
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| 2002 |
Grün S, Diesmann M, Aertsen A. Unitary events in multiple single-neuron spiking activity: II. Nonstationary data. Neural Computation. 14: 81-119. PMID 11747535 DOI: 10.1162/089976602753284464 |
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| 2002 |
Grün S, Diesmann M, Aertsen A. Unitary events in multiple single-neuron spiking activity: I. Detection and significance. Neural Computation. 14: 43-80. PMID 11747534 DOI: 10.1162/089976602753284455 |
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| 2002 |
Tetzlaff T, Geisel T, Diesmann M. The ground state of cortical feed-forward networks Neurocomputing. 44: 673-678. DOI: 10.1016/S0925-2312(02)00456-3 |
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| 2001 |
Gewaltig MO, Diesmann M, Aertsen A. 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. 14: 657-73. PMID 11665761 DOI: 10.1016/S0893-6080(01)00070-3 |
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| 2001 |
Aertsen A, Diesmann M, Gewaltig MO, Grün S, Rotter S. Neural dynamics in cortical networks--precision of joint-spiking events. Novartis Foundation Symposium. 239: 193-204; discussion . PMID 11529312 |
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| 2001 |
Gewaltig MO, Diesmann M, Aertsen A. Cortical synfire-activity: Configuration space and survival probability Neurocomputing. 38: 621-626. DOI: 10.1016/S0925-2312(01)00454-4 |
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| 2001 |
Diesmann M, Gewaltig MO, Rotter S, Aertsen A. State space analysis of synchronous spiking in cortical neural networks Neurocomputing. 38: 565-571. DOI: 10.1016/S0925-2312(01)00409-X |
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| 2000 |
Riehle A, Grammont F, Diesmann M, Grün S. Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation. Journal of Physiology, Paris. 94: 569-82. PMID 11165921 DOI: 10.1016/S0928-4257(00)01100-1 |
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| 1999 |
Grün S, Diesmann M, Grammont F, Riehle A, Aertsen A. Detecting unitary events without discretization of time. Journal of Neuroscience Methods. 94: 67-79. PMID 10638816 DOI: 10.1016/S0165-0270(99)00126-0 |
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| 1999 |
Rotter S, Diesmann M. Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biological Cybernetics. 81: 381-402. PMID 10592015 |
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| 1999 |
Diesmann M, Gewaltig MO, Aertsen A. Stable propagation of synchronous spiking in cortical neural networks. Nature. 402: 529-33. PMID 10591212 DOI: 10.1038/990101 |
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| 1997 |
Riehle A, Grün S, Diesmann M, Aertsen A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science (New York, N.Y.). 278: 1950-3. PMID 9395398 DOI: 10.1126/science.278.5345.1950 |
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| 1996 |
Aertsen A, Diesmann M, Gewaltig MO. Propagation of synchronous spiking activity in feedforward neural networks. Journal of Physiology, Paris. 90: 243-7. PMID 9116676 DOI: 10.1016/S0928-4257(97)81432-5 |
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