| Year |
Citation |
Score |
| 2022 |
Layer M, Senk J, Essink S, van Meegen A, Bos H, Helias M. NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models. Frontiers in Neuroinformatics. 16: 835657. PMID 35712677 DOI: 10.3389/fninf.2022.835657 |
0.484 |
|
| 2022 |
Stapmanns J, Kühn T, Dahmen D, Luu T, Honerkamp C, Helias M. Erratum: Self-consistent formulations for stochastic nonlinear neuronal dynamics [Phys. Rev. E 101, 042124 (2020)]. Physical Review. E. 105: 059901. PMID 35706324 DOI: 10.1103/PhysRevE.105.059901 |
0.344 |
|
| 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 |
0.481 |
|
| 2021 |
van Meegen A, Kühn T, Helias M. Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions. Physical Review Letters. 127: 158302. PMID 34678014 DOI: 10.1103/PhysRevLett.127.158302 |
0.333 |
|
| 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 |
0.311 |
|
| 2020 |
Gilson M, Dahmen D, Moreno-Bote R, Insabato A, Helias M. The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks. Plos Computational Biology. 16: e1008127. PMID 33044953 DOI: 10.1371/journal.pcbi.1008127 |
0.51 |
|
| 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 |
0.483 |
|
| 2020 |
Stapmanns J, Kühn T, Dahmen D, Luu T, Honerkamp C, Helias M. Self-consistent formulations for stochastic nonlinear neuronal dynamics. Physical Review. E. 101: 042124. PMID 32422832 DOI: 10.1103/Physreve.101.042124 |
0.512 |
|
| 2020 |
Dahmen D, Gilson M, Helias M. Capacity of the covariance perceptron Journal of Physics A. 53: 354002. DOI: 10.1088/1751-8121/Ab82Dd |
0.412 |
|
| 2020 |
Helias M, Dahmen D. Statistical Field Theory for Neural Networks Arxiv: Disordered Systems and Neural Networks. DOI: 10.1007/978-3-030-46444-8 |
0.327 |
|
| 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 |
0.564 |
|
| 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 |
0.349 |
|
| 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 |
0.503 |
|
| 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 |
0.461 |
|
| 2018 |
Schuecker J, Goedeke S, Helias M. Optimal Sequence Memory in Driven Random Networks Physical Review X. 8: 41029. DOI: 10.1103/Physrevx.8.041029 |
0.326 |
|
| 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 |
0.585 |
|
| 2017 |
Rostami V, Porta Mana P, Grün S, Helias M. Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models. Plos Computational Biology. 13: e1005762. PMID 28968396 DOI: 10.1371/Journal.Pcbi.1005762 |
0.617 |
|
| 2017 |
Kühn T, Helias M. Locking of correlated neural activity to ongoing oscillations. Plos Computational Biology. 13: e1005534. PMID 28604771 DOI: 10.1371/Journal.Pcbi.1005534 |
0.539 |
|
| 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 |
0.47 |
|
| 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 |
0.479 |
|
| 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 |
0.495 |
|
| 2016 |
Torre E, Canova C, Denker M, Gerstein G, Helias M, Grün S. ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains. Plos Computational Biology. 12: e1004939. PMID 27420734 DOI: 10.1371/Journal.Pcbi.1004939 |
0.481 |
|
| 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 |
0.425 |
|
| 2016 |
Dahmen D, Bos H, Helias M. Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium Physical Review X. 6: 31024. DOI: 10.1103/Physrevx.6.031024 |
0.467 |
|
| 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 |
0.425 |
|
| 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 |
0.51 |
|
| 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 |
0.482 |
|
| 2015 |
Chua Y, Morrison A, Helias M. Modeling the calcium spike as a threshold triggered fixed waveform for synchronous inputs in the fluctuation regime. Frontiers in Computational Neuroscience. 9: 91. PMID 26283954 DOI: 10.3389/Fncom.2015.00091 |
0.473 |
|
| 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 |
0.492 |
|
| 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 |
0.55 |
|
| 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 |
0.491 |
|
| 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 |
0.509 |
|
| 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 |
0.594 |
|
| 2014 |
Chua Y, Helias M, Morrison A. Calcium current improves coincidence detection of the LIF model Bmc Neuroscience. 15: 86. DOI: 10.1186/1471-2202-15-S1-P86 |
0.474 |
|
| 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 |
0.456 |
|
| 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 |
0.631 |
|
| 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 |
0.491 |
|
| 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 |
0.425 |
|
| 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 |
0.558 |
|
| 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 |
0.472 |
|
| 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 |
0.492 |
|
| 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 |
0.527 |
|
| 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 |
0.574 |
|
| 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 |
0.584 |
|
| 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 |
0.502 |
|
| 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 |
0.383 |
|
| 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 |
0.393 |
|
| 2012 |
Deger M, Helias M, Boucsein C, Rotter S. Statistical properties of superimposed stationary spike trains. Journal of Computational Neuroscience. 32: 443-63. PMID 21964584 DOI: 10.1007/S10827-011-0362-8 |
0.537 |
|
| 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 |
0.569 |
|
| 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 |
0.558 |
|
| 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 |
0.549 |
|
| 2011 |
Deger M, Helias M, Boucsein C, Rotter S. Effective neuronal refractoriness dominates the statistics of superimposed spike trains Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P273 |
0.588 |
|
| 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 |
0.537 |
|
| 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 |
0.571 |
|
| 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 |
0.511 |
|
| 2010 |
Deger M, Helias M, Cardanobile S, Atay FM, Rotter S. Nonequilibrium dynamics of stochastic point processes with refractoriness. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 82: 021129. PMID 20866797 DOI: 10.1103/Physreve.82.021129 |
0.353 |
|
| 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 |
0.483 |
|
| 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 |
0.447 |
|
| 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 |
0.571 |
|
| 2010 |
Helias M, Tetzlaff T, Diesmann M. Neurons hear their echo Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P47 |
0.554 |
|
| 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 |
0.53 |
|
| 2009 |
Kriener B, Helias M, Aertsen A, Rotter S. Correlations in spiking neuronal networks with distance dependent connections. Journal of Computational Neuroscience. 27: 177-200. PMID 19568923 DOI: 10.1007/S10827-008-0135-1 |
0.473 |
|
| 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 |
0.438 |
|
| 2009 |
Deger M, Cardanobile S, Helias M, Rotter S. The Poisson process with dead time captures important statistical features of neural activity Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P110 |
0.309 |
|
| 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 |
0.476 |
|
| 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 |
0.439 |
|
| 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 |
0.404 |
|
| 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 |
0.495 |
|
| 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 |
0.396 |
|
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