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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
1392015
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
1122017
Likelihood-free inference with emulator networks
JM Lueckmann, G Bassetto, T Karaletsos, JH Macke
Proceedings of Machine Learning Research 96, 32–53, 2019
652019
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
632020
SBI--A toolkit for simulation-based inference
A Tejero-Cantero, J Boelts, M Deistler, JM Lueckmann, C Durkan, ...
arXiv preprint arXiv:2007.09114, 2020
542020
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
492012
Benchmarking Simulation-Based Inference
JM Lueckmann, J Boelts, DS Greenberg, PJ Gonçalves, JH Macke
Proceedings of The 24th International Conference on Artificial Intelligence …, 2021
332021
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
322018
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
Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv
PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ...
112019
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
102020
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
Flexible and efficient simulation-based inference for models of decision-making
J Boelts, JM Lueckmann, R Gao, JH Macke
bioRxiv, 2021
22021
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
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
12022
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
Inferring the parameters of neural simulations from high-dimensional observations
M Nonnenmacher, JM Lueckmann, G Bassetto, PJ Goncalves, JH Macke
Computational and Systems Neuroscience (Cosyne) 2019, 138-139, 2019
2019
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Articles 1–20