| Year |
Citation |
Score |
| 2023 |
Halvagal MS, Zenke F. The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks. Nature Neuroscience. 26: 1906-1915. PMID 37828226 DOI: 10.1038/s41593-023-01460-y |
0.517 |
|
| 2022 |
Cramer B, Billaudelle S, Kanya S, Leibfried A, Grübl A, Karasenko V, Pehle C, Schreiber K, Stradmann Y, Weis J, Schemmel J, Zenke F. Surrogate gradients for analog neuromorphic computing. Proceedings of the National Academy of Sciences of the United States of America. 119. PMID 35042792 DOI: 10.1073/pnas.2109194119 |
0.449 |
|
| 2021 |
Wu YK, Zenke F. Nonlinear transient amplification in recurrent neural networks with short-term plasticity. Elife. 10. PMID 34895468 DOI: 10.7554/eLife.71263 |
0.441 |
|
| 2021 |
Payeur A, Guerguiev J, Zenke F, Richards BA, Naud R. Author Correction: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature Neuroscience. 24: 1780. PMID 34728832 DOI: 10.1038/s41593-021-00970-x |
0.735 |
|
| 2021 |
Payeur A, Guerguiev J, Zenke F, Richards BA, Naud R. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature Neuroscience. PMID 33986551 DOI: 10.1038/s41593-021-00857-x |
0.787 |
|
| 2021 |
Zenke F, Bohté SM, Clopath C, Comşa IM, Göltz J, Maass W, Masquelier T, Naud R, Neftci EO, Petrovici MA, Scherr F, Goodman DFM. Visualizing a joint future of neuroscience and neuromorphic engineering. Neuron. 109: 571-575. PMID 33600754 DOI: 10.1016/j.neuron.2021.01.009 |
0.713 |
|
| 2021 |
Zenke F, Vogels TP. The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks. Neural Computation. 1-27. PMID 33513328 DOI: 10.1162/neco_a_01367 |
0.804 |
|
| 2020 |
Cramer B, Stradmann Y, Schemmel J, Zenke F. The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. Ieee Transactions On Neural Networks and Learning Systems. PMID 33378266 DOI: 10.1109/TNNLS.2020.3044364 |
0.389 |
|
| 2019 |
Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, Clopath C, Costa RP, de Berker A, Ganguli S, Gillon CJ, Hafner D, Kepecs A, Kriegeskorte N, Latham P, ... ... Zenke F, et al. A deep learning framework for neuroscience. Nature Neuroscience. 22: 1761-1770. PMID 31659335 DOI: 10.1038/S41593-019-0520-2 |
0.729 |
|
| 2019 |
Neftci EO, Mostafa H, Zenke F. Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks Ieee Signal Processing Magazine. 36: 51-63. DOI: 10.1109/Msp.2019.2931595 |
0.509 |
|
| 2018 |
Gjoni E, Zenke F, Bouhours B, Schneggenburger R. Specific synaptic input strengths determine the computational properties of excitation - inhibition integration in a sound localization circuit. The Journal of Physiology. PMID 30051910 DOI: 10.1113/Jp276012 |
0.481 |
|
| 2018 |
Zenke F, Ganguli S. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. Neural Computation. 1-28. PMID 29652587 DOI: 10.1162/Neco_A_01086 |
0.683 |
|
| 2017 |
Zenke F, Poole B, Ganguli S. Continual Learning Through Synaptic Intelligence. Proceedings of Machine Learning Research. 70: 3987-3995. PMID 31909397 |
0.644 |
|
| 2017 |
Zenke F, Gerstner W, Ganguli S. The temporal paradox of Hebbian learning and homeostatic plasticity. Current Opinion in Neurobiology. 43: 166-176. PMID 28431369 DOI: 10.1016/J.Conb.2017.03.015 |
0.807 |
|
| 2017 |
Zenke F, Gerstner W. Hebbian plasticity requires compensatory processes on multiple timescales. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 372. PMID 28093557 DOI: 10.1098/Rstb.2016.0259 |
0.789 |
|
| 2015 |
Gilson M, Savin C, Zenke F. Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity. Frontiers in Computational Neuroscience. 9: 145. PMID 26648864 DOI: 10.3389/Fncom.2015.00145 |
0.644 |
|
| 2015 |
Zenke F, Agnes EJ, Gerstner W. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Communications. 6: 6922. PMID 25897632 DOI: 10.1038/Ncomms7922 |
0.755 |
|
| 2015 |
Ziegler L, Zenke F, Kastner DB, Gerstner W. Synaptic consolidation: from synapses to behavioral modeling. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 35: 1319-34. PMID 25609644 DOI: 10.1523/Jneurosci.3989-14.2015 |
0.806 |
|
| 2014 |
Zenke F, Gerstner W. Limits to high-speed simulations of spiking neural networks using general-purpose computers. Frontiers in Neuroinformatics. 8: 76. PMID 25309418 DOI: 10.3389/Fninf.2014.00076 |
0.726 |
|
| 2013 |
Lütcke H, Gerhard F, Zenke F, Gerstner W, Helmchen F. Inference of neuronal network spike dynamics and topology from calcium imaging data. Frontiers in Neural Circuits. 7: 201. PMID 24399936 DOI: 10.3389/Fncir.2013.00201 |
0.785 |
|
| 2013 |
Zenke F, Hennequin G, Gerstner W. Synaptic plasticity in neural networks needs homeostasis with a fast rate detector. Plos Computational Biology. 9: e1003330. PMID 24244138 DOI: 10.1371/Journal.Pcbi.1003330 |
0.819 |
|
| 2013 |
Vogels TP, Froemke RC, Doyon N, Gilson M, Haas JS, Liu R, Maffei A, Miller P, Wierenga CJ, Woodin MA, Zenke F, Sprekeler H. Inhibitory synaptic plasticity: spike timing-dependence and putative network function. Frontiers in Neural Circuits. 7: 119. PMID 23882186 DOI: 10.3389/Fncir.2013.00119 |
0.824 |
|
| 2011 |
Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science (New York, N.Y.). 334: 1569-73. PMID 22075724 DOI: 10.1126/Science.1211095 |
0.788 |
|
| 2011 |
Zenke F, Hennequin G, Sprekeler H, Vogels TP, Gerstner W. Plasticity and stability in recurrent neural networks Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P120 |
0.77 |
|
| Show low-probability matches. |