Year |
Citation |
Score |
2022 |
Braun W, Memmesheimer RM. High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1. Plos Computational Biology. 18: e1009891. PMID 35176028 DOI: 10.1371/journal.pcbi.1009891 |
0.352 |
|
2020 |
Klos C, Kalle Kossio YF, Goedeke S, Gilra A, Memmesheimer RM. Dynamical Learning of Dynamics. Physical Review Letters. 125: 088103. PMID 32909804 DOI: 10.1103/Physrevlett.125.088103 |
0.657 |
|
2019 |
Pallasdies F, Goedeke S, Braun W, Memmesheimer RM. From single neurons to behavior in the jellyfish . Elife. 8. PMID 31868586 DOI: 10.7554/eLife.50084 |
0.338 |
|
2019 |
Manz P, Goedeke S, Memmesheimer RM. Dynamics and computation in mixed networks containing neurons that accelerate towards spiking. Physical Review. E. 100: 042404. PMID 31770941 DOI: 10.1103/PhysRevE.100.042404 |
0.528 |
|
2018 |
Viriyopase A, Memmesheimer RM, Gielen S. Analyzing the competition of gamma rhythms with delayed pulse-coupled oscillators in phase representation. Physical Review. E. 98: 022217. PMID 30253475 DOI: 10.1103/PhysRevE.98.022217 |
0.528 |
|
2018 |
Kossio FYK, Goedeke S, van den Akker B, Ibarz B, Memmesheimer RM. Growing Critical: Self-Organized Criticality in a Developing Neural System. Physical Review Letters. 121: 058301. PMID 30118252 DOI: 10.1103/PhysRevLett.121.058301 |
0.405 |
|
2016 |
Thalmeier D, Uhlmann M, Kappen HJ, Memmesheimer RM. Learning Universal Computations with Spikes. Plos Computational Biology. 12: e1004895. PMID 27309381 DOI: 10.1371/journal.pcbi.1004895 |
0.534 |
|
2016 |
Viriyopase A, Memmesheimer RM, Gielen S. Cooperation and competition of gamma oscillation mechanisms. Journal of Neurophysiology. 116: 232-51. PMID 26912589 DOI: 10.1152/jn.00493.2015 |
0.438 |
|
2016 |
Abbott LF, DePasquale B, Memmesheimer RM. Building functional networks of spiking model neurons. Nature Neuroscience. 19: 350-5. PMID 26906501 DOI: 10.1038/Nn.4241 |
0.473 |
|
2015 |
Jahnke S, Timme M, Memmesheimer RM. A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 35: 16236-58. PMID 26658873 DOI: 10.1523/Jneurosci.3977-14.2015 |
0.562 |
|
2014 |
Jahnke S, Memmesheimer RM, Timme M. Oscillation-induced signal transmission and gating in neural circuits. Plos Computational Biology. 10: e1003940. PMID 25503492 DOI: 10.1371/Journal.Pcbi.1003940 |
0.611 |
|
2014 |
Memmesheimer RM, Rubin R, Olveczky BP, Sompolinsky H. Learning precisely timed spikes. Neuron. 82: 925-38. PMID 24768299 DOI: 10.1016/j.neuron.2014.03.026 |
0.427 |
|
2014 |
Jahnke S, Memmesheimer RM, Timme M. Hub-activated signal transmission in complex networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 030701. PMID 24730779 DOI: 10.1103/Physreve.89.030701 |
0.685 |
|
2014 |
Breuer D, Timme M, Memmesheimer RM. Statistical physics of neural systems with nonadditive dendritic coupling Physical Review X. 4. DOI: 10.1103/Physrevx.4.011053 |
0.693 |
|
2013 |
Jahnke S, Memmesheimer RM, Timme M. Propagating synchrony in feed-forward networks. Frontiers in Computational Neuroscience. 7: 153. PMID 24298251 DOI: 10.3389/Fncom.2013.00153 |
0.659 |
|
2013 |
Jahnke S, Memmesheimer R, Timme M. Oscillation induced propagation of synchrony in structured neural networks Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P390 |
0.718 |
|
2013 |
Breuer D, Timme M, Memmesheimer R. Computational optimum of recurrent neural circuits at intermediate numbers of nonlinear dendritic branches Bmc Neuroscience. 14: P273. DOI: 10.1186/1471-2202-14-S1-P273 |
0.676 |
|
2012 |
Memmesheimer RM, Timme M. Non-additive coupling enables propagation of synchronous spiking activity in purely random networks. Plos Computational Biology. 8: e1002384. PMID 22532791 DOI: 10.1371/Journal.Pcbi.1002384 |
0.745 |
|
2012 |
Jahnke S, Timme M, Memmesheimer RM. Guiding Synchrony through Random Networks Physical Review X. 2. DOI: 10.1103/Physrevx.2.041016 |
0.712 |
|
2011 |
van den Akker B, Ibarz B, Memmesheimer R. Self-organized criticality in a model for developing neural networks Bmc Neuroscience. 12: P221. DOI: 10.1186/1471-2202-12-S1-P221 |
0.331 |
|
2010 |
Memmesheimer RM. Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions. Proceedings of the National Academy of Sciences of the United States of America. 107: 11092-7. PMID 20511534 DOI: 10.1073/pnas.0909615107 |
0.545 |
|
2010 |
Memmesheimer RM, Timme M. Stable and unstable periodic orbits in complex networks of spiking neurons with delays Discrete and Continuous Dynamical Systems. 28: 1555-1588. DOI: 10.3934/Dcds.2010.28.1555 |
0.719 |
|
2009 |
Jahnke S, Memmesheimer RM, Timme M. How Chaotic is the Balanced State? Frontiers in Computational Neuroscience. 3: 13. PMID 19936316 DOI: 10.3389/Neuro.10.013.2009 |
0.712 |
|
2008 |
Jahnke S, Memmesheimer RM, Timme M. Stable irregular dynamics in complex neural networks. Physical Review Letters. 100: 048102. PMID 18352336 DOI: 10.1103/Physrevlett.100.048102 |
0.723 |
|
2007 |
Memmesheimer R, Timme M. Non-additive coupling enables stable propagation of synchronous spiking in purely random networks Bmc Neuroscience. 8. DOI: 10.1186/1471-2202-8-S2-P31 |
0.705 |
|
2006 |
Memmesheimer RM, Timme M. Designing the dynamics of spiking neural networks. Physical Review Letters. 97: 188101. PMID 17155580 DOI: 10.1103/Physrevlett.97.188101 |
0.717 |
|
2006 |
Memmesheimer RM, Timme M. Designing complex networks Physica D: Nonlinear Phenomena. 224: 182-201. DOI: 10.1016/J.Physd.2006.09.037 |
0.71 |
|
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