| Lyapunov spectra of chaotic recurrent neural networks R Engelken, F Wolf, LF Abbott Physical Review Research 5 (4), 043044, 2023 | 60 | 2023 |
| Dynamical models of cortical circuits F Wolf, R Engelken, M Puelma-Touzel, JDF Weidinger, A Neef Current opinion in neurobiology 25, 228-236, 2014 | 59 | 2014 |
| A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons” R Engelken, F Farkhooi, D Hansel, C van Vreeswijk, F Wolf F1000Research 5, 2016 | 17 | 2016 |
| Curriculum learning as a tool to uncover learning principles in the brain D Kepple, R Engelken, K Rajan International Conference on Learning Representations, 2022 | 15 | 2022 |
| Input correlations impede suppression of chaos and learning in balanced firing-rate networks R Engelken, A Ingrosso, R Khajeh, S Goedeke, LF Abbott PLOS Computational Biology 18 (12), e1010590, 2022 | 8 | 2022 |
| Dimensionality and entropy of spontaneous and evoked rate activity R Engelken, F Wolf APS March Meeting Abstracts 2017, P5. 007, 2017 | 4 | 2017 |
| Chaotic neural circuit dynamics R Engelken Georg-August-Universität Göttingen, 2017 | 4 | 2017 |
| Comment on “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons” R Engelken, F Farkhooi, D Hansel, C van Vreeswijk, F Wolf bioRxiv, 017798, 2015 | 3 | 2015 |
| SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks R Engelken Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians R Engelken Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 2 | 2023 |
| Dynamical entropy production in cortical circuits with different network topologies R Engelken, M Monteforte, F Wolf BMC Neuroscience 14 (Suppl 1), P421, 2013 | 1 | 2013 |
| A time-resolved theory of information encoding in recurrent neural networks R Engelken, S Goedeke Advances in Neural Information Processing Systems 35, 35490-35503, 2022 | | 2022 |
| Boosting of neural circuit chaos at the onset of collective oscillations A Palmigiano, R Engelken, F Wolf bioRxiv, 2022.08. 28.505598, 2022 | | 2022 |
| How network structure shapes dynamics and learning in recurrent neural networks M Ding, R Engelken APS March Meeting Abstracts 2022, N00. 341, 2022 | | 2022 |
| Dynamics and trainability of recurrent neural networks with partial symmetry and antisymmetry M Ding, R Engelken JOURNAL OF COMPUTATIONAL NEUROSCIENCE 49 (SUPPL 1), S68-S69, 2021 | | 2021 |
| Controlling chaos in balanced neural circuits with input spike trains R Engelken, F Wolf APS March Meeting Abstracts 2016, E41. 006, 2016 | | 2016 |
| Input spike trains suppress chaos in balanced neural circuits R Engelken, M Monteforte, F Wolf APS March Meeting Abstracts 2015, J33. 003, 2015 | | 2015 |
| The Transition to Control in Spiking Networks R Engelken, F Wolf | | |
| Reservoirs of Stability: Dynamic Flux Tubes in Cortical Circuits M Monteforte, R Engelken, F Wolf | | |
Fred WolfMax Planck Institute for Dynamics and Self-Organization and Göttingen UniversityVerified email at ds.mpg.de
