| 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, 1289-1299, 2017 | 144 | 2017 |
| 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 | 86 | 2020 |
| Likelihood-free inference with emulator networks JM Lueckmann, G Bassetto, T Karaletsos, JH Macke Symposium on Advances in Approximate Bayesian Inference, 32-53, 2019 | 85 | 2019 |
| Visual pursuit behavior in mice maintains the pursued prey on the retinal region with least optic flow CD Holmgren, P Stahr, DJ Wallace, KM Voit, EJ Matheson, J Sawinski, ... Elife 10, e70838, 2021 | 14 | 2021 |
| Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... | 11 | 2019 |
| A Bayesian model for identifying hierarchically organised states in neural population activity P Putzky, F Franzen, G Bassetto, JH Macke Advances in Neural Information Processing Systems, 3095-3103, 2014 | 8 | 2014 |
| Characterizing retinal ganglion cell responses to electrical stimulation using generalized linear models S Sekhar, P Ramesh, G Bassetto, E Zrenner, JH Macke, DL Rathbun Frontiers in Neuroscience 14, 2020 | 6 | 2020 |
| 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 |
| Flexible statistical inference for mechanistic models of neural dynamics P Goncalves, JM Lueckmann, G Bassetto, K Oecal, M Nonnenmacher, ... Bonn Brain 3 Conference 2018, Bonn, Germany, 2018 | 1 | 2018 |
| Electrophysiology Analysis, Bayesian G Bassetto, JH Macke Encyclopedia of Computational Neuroscience, 1-5, 2020 | | 2020 |
| Inferring the parameters of neural simulations from high-dimensional observations M Nonnenmacher, JM Lueckmann, G Bassetto, P Goncalves, JH Macke Computational and Systems Neuroscience (Cosyne) 2019, 138-139, 2019 | | 2019 |
| Using bayesian inference to estimate receptive fields from a small number of spikes G Bassetto, J Macke Computational and Systems Neuroscience Meeting (COSYNE 2017), 64-64, 2017 | | 2017 |
| Full Bayesian inference for model-based receptive field estimation, with application to primary visual cortex G Bassetto, J Macke Bernstein Conference 2016, 117-118, 2016 | | 2016 |
| Anatomical basis of spiking correlation in upper layers of somatosensory cortex U Czubayko, G Bassetto, RT Narayanan, M Oberlaender, JH Macke, ... 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), 2015 | | 2015 |
| A statistical characterization of neural population responses in V1 G Basseto, F Sandhaeger, A Ecker, JH Macke Bernstein Conference 2015, 146-147, 2015 | | 2015 |
| Assesment, integration and implementation of computationally efficient models to simulate biological neuronal networks on parallel hardware G Bassetto | | 2013 |
| Training deep neural density estimators to identify mechanistic models of neural dynamics. Open Website PJ Goncalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Ocal, ... | | |
| Training deep neural density estimators to PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... | | |
| Supplement for: A Bayesian model for identifying hierarchically organised states in neural population activity P Putzky, F Franzen, G Bassetto, JH Macke | | |
Jakob MackeMachine Learning in Science, Tübingen UniversityVerified email at uni-tuebingen.de
