| Year |
Citation |
Score |
| 2022 |
Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, Schüttler L, Gramelsberger G, Diesmann M, Plesser HE, van Albada SJ. Connectivity concepts in neuronal network modeling. Plos Computational Biology. 18: e1010086. PMID 36074778 DOI: 10.1371/journal.pcbi.1010086 |
0.784 |
|
| 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. A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations. Frontiers in Neuroinformatics. 16: 837549. PMID 35645755 DOI: 10.3389/fninf.2022.837549 |
0.42 |
|
| 2022 |
Pronold J, Jordan J, Wylie BJN, Kitayama I, Diesmann M, Kunkel S. Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring. Frontiers in Neuroinformatics. 15: 785068. PMID 35300490 DOI: 10.3389/fninf.2021.785068 |
0.409 |
|
| 2022 |
Heittmann A, Psychou G, Trensch G, Cox CE, Wilcke WW, Diesmann M, Noll TG. Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer. Frontiers in Neuroscience. 15: 728460. PMID 35126034 DOI: 10.3389/fnins.2021.728460 |
0.407 |
|
| 2022 |
Dahmen D, Layer M, Deutz L, Dąbrowska PA, Voges N, von Papen M, Brochier T, Riehle A, Diesmann M, Grün S, Helias M. Global organization of neuronal activity only requires unstructured local connectivity. Elife. 11. PMID 35049496 DOI: 10.7554/eLife.68422 |
0.681 |
|
| 2021 |
Dasbach S, Tetzlaff T, Diesmann M, Senk J. Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution. Frontiers in Neuroscience. 15: 757790. PMID 35002599 DOI: 10.3389/fnins.2021.757790 |
0.339 |
|
| 2021 |
Spreizer S, Senk J, Rotter S, Diesmann M, Weyers B. NEST Desktop, an Educational Application for Neuroscience. Eneuro. 8. PMID 34764188 DOI: 10.1523/ENEURO.0274-21.2021 |
0.796 |
|
| 2021 |
Stapmanns J, Hahne J, Helias M, Bolten M, Diesmann M, Dahmen D. Event-Based Update of Synapses in Voltage-Based Learning Rules. Frontiers in Neuroinformatics. 15: 609147. PMID 34177505 DOI: 10.3389/fninf.2021.609147 |
0.323 |
|
| 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 |
0.49 |
|
| 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 |
0.314 |
|
| 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 |
0.449 |
|
| 2019 |
Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, Shinomoto S. Reconstructing neuronal circuitry from parallel spike trains. Nature Communications. 10: 4468. PMID 31578320 DOI: 10.1038/S41467-019-12225-2 |
0.451 |
|
| 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 |
0.699 |
|
| 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 |
0.671 |
|
| 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 |
0.634 |
|
| 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 |
0.494 |
|
| 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 |
0.46 |
|
| 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 |
0.477 |
|
| 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 |
0.373 |
|
| 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 |
0.509 |
|
| 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 |
0.485 |
|
| 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 |
0.566 |
|
| 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 |
0.52 |
|
| 2018 |
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. International Neuroscience Initiatives through the Lens of High-Performance Computing Ieee Computer. 51: 50-59. DOI: 10.1109/Mc.2018.2141039 |
0.375 |
|
| 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 |
0.47 |
|
| 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 |
0.426 |
|
| 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 |
0.508 |
|
| 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 |
0.811 |
|
| 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 |
0.492 |
|
| 2017 |
Newton AJH, Seidenstein AH, McDougal RA, Pérez-Cervera A, Huguet G, M-Seara T, Haimerl C, Angulo-Garcia D, Torcini A, Cossart R, Malvache A, Skiker K, Maouene M, Ragognetti G, Lorusso L, ... ... Diesmann M, et al. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Bmc Neuroscience. 18. DOI: 10.1186/S12868-017-0372-1 |
0.759 |
|
| 2017 |
Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, ... ... Diesmann M, et al. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2 Bmc Neuroscience. 18. DOI: 10.1186/S12868-017-0371-2 |
0.592 |
|
| 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 |
0.315 |
|
| 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 |
0.724 |
|
| 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 |
0.432 |
|
| 2016 |
Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, ... ... Diesmann M, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6 |
0.753 |
|
| 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 |
0.417 |
|
| 2016 |
Pfeil T, Jordan J, Tetzlaff T, Grübl A, Schemmel J, Diesmann M, Meier K. Effect of heterogeneity on decorrelation mechanisms in spiking neural networks: A neuromorphic-hardware study Physical Review X. 6. DOI: 10.1103/Physrevx.6.021023 |
0.537 |
|
| 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 |
0.358 |
|
| 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 |
0.435 |
|
| 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 |
0.54 |
|
| 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 |
0.511 |
|
| 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 |
0.744 |
|
| 2015 |
Bos H, Schuecker J, Diesmann M, Helias M. Identifying and exploiting the anatomical origin of population rate oscillations in multi-layered spiking networks Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P97 |
0.451 |
|
| 2015 |
Grytskyy D, Diesmann M, Helias M. Functional consequences of non-equilibrium dynamics caused by antisymmetric and symmetric learning rules Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P96 |
0.447 |
|
| 2015 |
Jordan J, Tetzlaff T, Petrovici M, Breitwieser O, Bytschok I, Bill J, Schemmel J, Meier K, Diesmann M. Deterministic neural networks as sources of uncorrelated noise for probabilistic computations Bmc Neuroscience. 16: P62. DOI: 10.1186/1471-2202-16-S1-P62 |
0.394 |
|
| 2015 |
Trengove C, Leeuwen Cv, Diesmann M. Effective connectivity analysis explains metastable states of ongoing activity in cortically embedded systems of coupled synfire chains Bmc Neuroscience. 16: 61. DOI: 10.1186/1471-2202-16-S1-P61 |
0.42 |
|
| 2015 |
van Albada SJ, Helias M, Diesmann M. Limits to the scalability of cortical network models Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-O1 |
0.52 |
|
| 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 |
0.811 |
|
| 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 |
0.518 |
|
| 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 |
0.436 |
|
| 2014 |
Schuecker J, Diesmann M, Helias M. The transfer function of the LIF model: from white to filtered noise Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P146 |
0.389 |
|
| 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: 10.1186/1471-2202-14-S1-P123 |
0.829 |
|
| 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 |
0.362 |
|
| 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 |
0.447 |
|
| 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 |
0.315 |
|
| 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 |
0.382 |
|
| 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 |
0.705 |
|
| 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 |
0.424 |
|
| 2013 |
Grytskyy D, Tetzlaff T, Diesmann M, Helias M. Noise decouples covariances from interaction strength Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P164 |
0.505 |
|
| 2013 |
Kunkel S, Schmidt M, Eppler JM, Plesser HE, Igarashi J, Masumoto G, Fukai T, Ishii S, Morrison A, Diesmann M, Helias M. From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P163 |
0.816 |
|
| 2013 |
Denker M, Riehle A, Diesmann M, Grün S. Relating excess spike synchrony to LFP-locked firing rates modulations Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P150 |
0.677 |
|
| 2013 |
Hagen E, Stavrinou ML, Linden H, Tetzlaff T, van Albada S, Dahmen D, Diesmann M, Gruen S, Einevoll GT. Hybrid scheme for modeling LFPs from spiking cortical network models Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P119 |
0.561 |
|
| 2013 |
Helias M, Tetzlaff T, Diesmann M. Recurrence and external sources differentially shape network correlations Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P113 |
0.493 |
|
| 2013 |
van Albada SJ, Schrader S, Helias M, Diesmann M. Influence of different types of downscaling on a cortical microcircuit model Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P112 |
0.533 |
|
| 2013 |
Schmidt M, van Albada S, Bakker R, Diesmann M. Integrating multi-scale data for a network model of macaque visual cortex Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P111 |
0.447 |
|
| 2013 |
Nowke C, Hentschel B, Kuhlen T, Schmidt M, van Albada SJ, Eppler JM, Bakker R, Diesmann M. Interactive visualization of brain-scale spiking activity Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P110 |
0.79 |
|
| 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 |
0.518 |
|
| 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 |
0.34 |
|
| 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 |
0.503 |
|
| 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 |
0.803 |
|
| 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 |
0.578 |
|
| 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 |
0.401 |
|
| 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 |
0.796 |
|
| 2012 |
Grytskyy D, Helias M, Tetzlaff T, Diesmann M. Taming the model zoo: a unified view on correlations in recurrent networks Bmc Neuroscience. 13. DOI: 10.1186/1471-2202-13-S1-P147 |
0.504 |
|
| 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 |
0.805 |
|
| 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 |
0.674 |
|
| 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 |
0.361 |
|
| 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 |
0.344 |
|
| 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 |
0.696 |
|
| 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 |
0.363 |
|
| 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 |
0.693 |
|
| 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 |
0.641 |
|
| 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 |
0.462 |
|
| 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 |
0.44 |
|
| 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 |
0.307 |
|
| 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 |
0.356 |
|
| 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 |
0.381 |
|
| 2011 |
Potjans TC, Diesmann M. Robustness vs. flexibility: how do external inputs shape the activity in a data-based layered cortical network model? Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P74 |
0.422 |
|
| 2011 |
Helias M, Tetzlaff T, Diesmann M. Towards a unified theory of correlations in recurrent neural networks Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P73 |
0.516 |
|
| 2011 |
Bakker R, Potjans TC, Wachtler T, Diesmann M. Macaque structural connectivity revisited: CoCoMac 2.0 Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P72 |
0.392 |
|
| 2011 |
Kunkel S, Helias M, Diesmann M, Morrison A. Fail-safe detection of threshold crossings of linear integrate-and-fire neuron models in time-driven simulations Bmc Neuroscience. 12: 229. DOI: 10.1186/1471-2202-12-S1-P229 |
0.477 |
|
| 2011 |
Schultze-Kraft M, Diesmann M, Grün S, Helias M. Correlation transmission of spiking neurons is boosted by synchronous input Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P144 |
0.715 |
|
| 2011 |
Denker M, Davison A, Diesmann M, Grün S. Towards guiding principles in workflow design to facilitate collaborative projects involving massively parallel electrophysiological data Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P131 |
0.614 |
|
| 2011 |
Wagatsuma N, Potjans TC, Diesmann M, Fukai T. Layer dependent neural modulation of a realistic layered-microcircuit model in visual cortex based on bottom-up and top-down signals Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P114 |
0.387 |
|
| 2011 |
Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M, Einevoll GT. How local is the local field potential? Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-O8 |
0.652 |
|
| 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 |
0.501 |
|
| 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 |
0.782 |
|
| 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 |
0.454 |
|
| 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 |
0.625 |
|
| 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: 10.1186/1471-2202-10-S1-P231 |
0.678 |
|
| 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 |
0.81 |
|
| 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 |
0.653 |
|
| 2010 |
Helias M, Tetzlaff T, Diesmann M. Neurons hear their echo Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P47 |
0.509 |
|
| 2010 |
Denker M, Riehle A, Diesmann M, Grün S. Phase locking between excess spike synchrony and LFP is independent of rate covariation Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P4 |
0.701 |
|
| 2010 |
Hanuschkin A, Diesmann M, Morrison A. A reafferent model of song syntax generation in the Bengalese finch Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P33 |
0.435 |
|
| 2010 |
Kunkel S, Diesmann M, Morrison A. Random wiring limits the development of functional structure in large recurrent neuronal networks Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P108 |
0.47 |
|
| 2010 |
Grün S, Borgelt C, Gerstein G, Louis S, Diesmann M. Selecting appropriate surrogate methods for spike correlation analysis Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-O15 |
0.804 |
|
| 2010 |
Tetzlaff T, Helias M, Einevoll GT, Diesmann M. Decorrelation of low-frequency neural activity by inhibitory feedback Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-O11 |
0.504 |
|
| 2010 |
Trengove C, Van Leeuwen C, Diesmann M. High storage capacity of synfire chains in large-scale cortical networks of conductance-based spiking neurons Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-F1 |
0.426 |
|
| 2010 |
Gruen S, Louis S, Gerstein G, Diesmann M. Surrogates for spike correlation analysis through dithering in operational time Neuroscience Research. 68: e51. DOI: 10.1016/J.Neures.2010.07.472 |
0.719 |
|
| 2010 |
Diesmann M. Supercomputers as data integration facilities: brain-scale simulations Neuroscience Research. 68: e31. DOI: 10.1016/J.Neures.2010.07.379 |
0.308 |
|
| 2010 |
Denker M, Riehle A, Diesmann M, Grün S. Distinguishing the effects of firing rate co-modulation and excess spike synchrony on the spike–LFP relationship Neuroscience Research. 68: e438. DOI: 10.1016/J.Neures.2010.07.1941 |
0.613 |
|
| 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 |
0.533 |
|
| 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 |
0.377 |
|
| 2009 |
Tetzlaff T, Einevoll GT, Diesmann M. Synchronization and rate dynamics in embedded synfire chains: effect of network heterogeneity and feedback Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P258 |
0.475 |
|
| 2009 |
Schwalger T, Goedeke S, Diesmann M. Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P256 |
0.392 |
|
| 2009 |
Berger D, Borgelt C, Diesmann M, Gerstein G, Grün S. An accretion based data mining algorithm for identification of sets of correlated neurons Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P254 |
0.788 |
|
| 2009 |
Helias M, Deger M, Diesmann M, Rotter S. Finite synaptic potentials cause a non-linear instantaneous response of the integrate-and-fire model Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P225 |
0.606 |
|
| 2009 |
Lindén H, Pettersen KH, Tetzlaff T, Potjans T, Denker M, Diesmann M, Grün S, Einevoll GT. Estimating the spatial range of local field potentials in a cortical population model Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P224 |
0.564 |
|
| 2009 |
Potjans TC, Fukai T, Diesmann M. Implications of the specific cortical circuitry for the network dynamics of a layered cortical network model Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P159 |
0.389 |
|
| 2009 |
Potjansu W, Morrison A, Diesmann M. A spiking temporal-difference learning model based on dopamine-modulated plasticity Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P140 |
0.321 |
|
| 2009 |
Morrison A, Potjans TC, Kunkel S, Diesmann M. Towards large-scale neuronal network simulations on peta-scale computers Neuroscience Research. 65: S133. DOI: 10.1016/J.Neures.2009.09.650 |
0.47 |
|
| 2009 |
Potjans TC, Diesmann M. Target-specific connectivity enhances the stability of activity dynamics in a layered cortical network model Neuroscience Research. 65: S109. DOI: 10.1016/J.Neures.2009.09.496 |
0.367 |
|
| 2009 |
Diesmann M, Helias M, Deger M, Rotter S. The non-linear response of the integrate-and-fire neuron to finite synaptic potentials Neuroscience Research. 65: S78. DOI: 10.1016/J.Neures.2009.09.290 |
0.59 |
|
| 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 |
0.814 |
|
| 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 |
0.8 |
|
| 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 |
0.826 |
|
| 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 |
0.657 |
|
| 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 |
0.398 |
|
| 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 |
0.838 |
|
| 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 |
0.714 |
|
| 2008 |
Potjans TC, Diesmann M. Integration of anatomical and physiological connectivity data sets for layered cortical network models Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P60 |
0.362 |
|
| 2008 |
Goedeke S, Schwalger T, Diesmann M. Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks Bmc Neuroscience. 9: P143. DOI: 10.1186/1471-2202-9-S1-P143 |
0.534 |
|
| 2008 |
Hanuschkin A, Kunkel S, Helias M, Morrison A, Diesmann M. Comparison of methods to calculate exact spike times in integrate-and-fire neurons with exponential currents Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P131 |
0.469 |
|
| 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 |
0.451 |
|
| 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 |
0.518 |
|
| 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 |
0.473 |
|
| 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 |
0.67 |
|
| 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 |
0.732 |
|
| 2007 |
Gewaltig M, Diesmann M. NEST (NEural Simulation Tool) Scholarpedia. 2: 1430. DOI: 10.4249/Scholarpedia.1430 |
0.793 |
|
| 2007 |
Helias M, Rotter S, Gewaltig M, Diesmann M. A model for correlation detection based on Ca2+concentration in spines Bmc Neuroscience. 8. DOI: 10.1186/1471-2202-8-S2-P192 |
0.808 |
|
| 2007 |
Gruen S, Pazienti A, Diesmann M. Effectiveness of dithering to destroy spike coincidences Neuroscience Research. 58: S54. DOI: 10.1016/J.Neures.2007.06.317 |
0.354 |
|
| 2007 |
Diesmann M, Gewaltig M, Morrison A, Plesser H. Large scale simulations of cortical neuronal networks Neuroscience Research. 58: S9. DOI: 10.1016/J.Neures.2007.06.045 |
0.513 |
|
| 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 |
0.567 |
|
| 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 |
0.429 |
|
| 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. |
0.814 |
|
| 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. |
0.822 |
|
| 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 |
0.456 |
|
| 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 |
0.795 |
|
| 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 DOI: 10.1103/Physrevlett.92.074103 |
0.386 |
|
| 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 |
0.439 |
|
| 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 |
0.802 |
|
| 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 |
0.643 |
|
| 2003 |
Tetzlaff T, Buschermöhle M, Geisel T, Diesmann M. The spread of rate and correlation in stationary cortical networks Neurocomputing. 52: 949-954. DOI: 10.1016/S0925-2312(02)00854-8 |
0.502 |
|
| 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 |
0.633 |
|
| 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 |
0.749 |
|
| 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 |
0.758 |
|
| 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 |
0.751 |
|
| 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 |
0.496 |
|
| 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 |
0.835 |
|
| 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 DOI: 10.1002/0470846674.Ch15 |
0.832 |
|
| 2001 |
Riehle A, Grammont F, Diesmann M, Grün S. Erratum to “Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation” [Journal of Physiology-Paris 94 (5–6) pp. 569–582 (2000)]☆ Journal of Physiology-Paris. 95: 499. DOI: 10.1016/S0928-4257(01)00092-4 |
0.58 |
|
| 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 |
0.819 |
|
| 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 |
0.84 |
|
| 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 |
0.69 |
|
| 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 |
0.721 |
|
| 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 DOI: 10.1007/S004220050570 |
0.64 |
|
| 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 |
0.844 |
|
| 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 |
0.719 |
|
| 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 |
0.829 |
|
| 1970 |
Diesmann M. Perspectives and challenges of large-scale neuronal network simulations Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.133 |
0.503 |
|
| 1970 |
Diesmann M, Einevoll G, Potjans T, Lindén H, Grün S. Modeling the local field potential by a large-scale layered cortical network model Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.046 |
0.363 |
|
| 1970 |
Diesmann M, Morrison A, Potjans T, Kunkel S. Simulating macroscale brain circuits with microscale resolution Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.044 |
0.394 |
|
| 1970 |
Diesmann M, Morrison A, Potjans W. Implementing neuromodulated plasticity in distributed simulations Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.043 |
0.312 |
|
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