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Jan-Matthis Lueckmann
Jan-Matthis Lueckmann
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Cited by
Year
Ostracism Online: A social media ostracism paradigm
W Wolf, A Levordashka, JR Ruff, S Kraaijeveld, JM Lueckmann, ...
Behavior Research Methods 47 (2), 361-373, 2015
1542015
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
1372017
SBI--A toolkit for simulation-based inference
A Tejero-Cantero, J Boelts, M Deistler, JM Lueckmann, C Durkan, ...
arXiv preprint arXiv:2007.09114, 2020
842020
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
812020
Likelihood-free inference with emulator networks
JM Lueckmann, G Bassetto, T Karaletsos, JH Macke
Proceedings of Machine Learning Research 96, 32–53, 2019
802019
Benchmarking Simulation-Based Inference
JM Lueckmann, J Boelts, DS Greenberg, PJ Gonçalves, JH Macke
Proceedings of The 24th International Conference on Artificial Intelligence …, 2021
542021
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
532012
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
332018
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
142016
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, 2020
122020
Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv
PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ...
112019
Flexible statistical inference for mechanistic models of neural dynamics. arXiv
JM Lueckmann, PJ Goncalves, G Bassetto, K Ocal, M Nonnenmacher, ...
Preprint, 2017
52017
Flexible and efficient simulation-based inference for models of decision-making
J Boelts, JM Lueckmann, R Gao, JH Macke
Elife 11, e77220, 2022
42022
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
42022
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
32019
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
32018
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
2
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
12019
Model selection via neural density estimation
J Boelts, JM Lueckmann, P Goncalves, H Sprekeler, JH Macke
Conference in Cognitive Computing 2018, Hannover, Germany, 2018
12018
26th annual computational neuroscience meeting (CNS* 2017): part 1
S Denham, P Poirazi, E De Schutter, K Friston, HK Chan, T Nowotny, ...
BMC Neuroscience 18 (1), 1-14, 2017
12017
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