| Flexible statistical inference for mechanistic models of neural dynamics JM Lueckmann, PJ Goncalves, G Bassetto, K Öcal, M Nonnenmacher, ... Advances in neural information processing systems 30, 2017 | 164 | 2017 |
| Ostracism Online: A social media ostracism paradigm W Wolf, A Levordashka, JR Ruff, S Kraaijeveld, JM Lueckmann, ... Behavior Research Methods 47, 361-373, 2015 | 159 | 2015 |
| Training deep neural density estimators to identify mechanistic models of neural dynamics PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... Elife 9, e56261, 2020 | 111 | 2020 |
| SBI--A toolkit for simulation-based inference A Tejero-Cantero, J Boelts, M Deistler, JM Lueckmann, C Durkan, ... arXiv preprint arXiv:2007.09114, 2020 | 111 | 2020 |
| Likelihood-free inference with emulator networks JM Lueckmann, G Bassetto, T Karaletsos, JH Macke Proceedings of Machine Learning Research 96, 32–53, 2019 | 95 | 2019 |
| Benchmarking Simulation-Based Inference JM Lueckmann, J Boelts, DS Greenberg, PJ Gonçalves, JH Macke Proceedings of The 24th International Conference on Artificial Intelligence …, 2021 | 85 | 2021 |
| p53 Regulates the neuronal intrinsic and extrinsic responses affecting the recovery of motor function following spinal cord injury EM Floriddia, KI Rathore, A Tedeschi, G Quadrato, A Wuttke, ... Journal of Neuroscience 32 (40), 13956-13970, 2012 | 55 | 2012 |
| Can serial dependencies in choices and neural activity explain choice probabilities? JM Lueckmann, JH Macke, H Nienborg Journal of Neuroscience 38 (14), 3495-3506, 2018 | 37 | 2018 |
| Pre-stimulus phase and amplitude regulation of phase-locked responses are maximized in the critical state AE Avramiea, R Hardstone, JM Lueckmann, J Bím, HD Mansvelder, ... Elife 9, e53016, 2020 | 15 | 2020 |
| Flexible and efficient simulation-based inference for models of decision-making J Boelts, JM Lueckmann, R Gao, JH Macke Elife 11, e77220, 2022 | 14 | 2022 |
| Spatiotemporal dynamics of random stimuli account for trial-to-trial variability in perceptual decision making H Park, JM Lueckmann, K von Kriegstein, S Bitzer, SJ Kiebel Scientific reports 6 (1), 1-17, 2016 | 14 | 2016 |
| Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... | 12 | 2019 |
| Advances in Neural Information Processing Systems JM Lueckmann, PJ Goncalves, G Bassetto, K Öcal, M Nonnenmacher, ... Curran Associates, 30, 1289-1299, 2017 | 7 | 2017 |
| GATSBI: Generative adversarial training for simulation-based inference P Ramesh, JM Lueckmann, J Boelts, Á Tejero-Cantero, DS Greenberg, ... arXiv preprint arXiv:2203.06481, 2022 | 6 | 2022 |
| Likelihood-free inference with emulator networks. arXiv e-prints JM Lueckmann, G Bassetto, T Karaletsos, JH Macke arXiv preprint arXiv:1805.09294, 2018 | 6 | 2018 |
| Flexible statistical inference for mechanistic models of neural dynamics. arXiv JM Lueckmann, PJ Goncalves, G Bassetto, K Ocal, M Nonnenmacher, ... arXiv preprint arXiv:1711.01861, 2017 | 5 | 2017 |
| Comparing neural simulations by neural density estimation J Boelts, JM Lueckmann, PJ Goncalves, H Sprekeler, JH Macke 2019 Conference on Cognitive Computational Neuroscience, 1289-1299, 0 | 4 | |
| Statistical inference for analyzing sloppiness in neuroscience models M Deistler, GJ Pedro, JM Lueckmann, K Oecal, DS Greenberg, JH Macke Bernstein Conference 2019, Berlin, Germany, 2019 | 3 | 2019 |
| Robust statistical inference for simulation-based models in neuroscience M Nonnenmacher, PJ Goncalves, G Bassetto, JM Lueckmann, JH Macke Bernstein Conference 2018, Berlin, Germany, 2018 | 3 | 2018 |
| Amortised inference for mechanistic models of neural dynamics JM Lueckmann, PJ Gonçalves, C Chintaluri, WF Podlaski, G Bassetto, ... Computational and Systems Neuroscience (Cosyne) 2019, 108, 2019 | 1 | 2019 |
Jakob MackeMachine Learning in Science, Tübingen UniversityVerified email at uni-tuebingen.de
