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Giacomo Bassetto
Giacomo Bassetto
research center caesar, an associate of the Max Planck Society, Bonn, Germany
Verified email at caesar.de
Title
Cited by
Cited by
Year
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
1362017
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
Symposium on Advances in Approximate Bayesian Inference, 32-53, 2019
802019
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
132021
Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv
PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ...
112019
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
82014
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
62020
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
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
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
12018
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
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Articles 1–20