A mathematical approach to unsupervised learning in recurrent neural networks. (Une approche mathématique de l'apprentissage non-supervisé dans les réseaux de neurones récurrents). - Mathieu Galtier (2011)

A combination of STDP, Hebbian learning and synaptic scaling deriving from a theoretical learning principle - Mathieu Galtier; Gilles Wainrib (CoRR, 2012)

Hebbian Learning of Recurrent Connections: A Geometrical Perspective. - Mathieu Galtier; Olivier D. Faugeras; Paul C. Bressloff (Neural Comput., 2012)

A Biological Gradient Descent for Prediction Through a Combination of STDP and Homeostatic Plasticity. - Mathieu Galtier; Gilles Wainrib (Neural Comput., 2013)

Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes. - Mathieu Galtier; Camille Marini; Gilles Wainrib; Herbert Jaeger (Neural Networks, 2014)

Ideomotor feedback control in a recurrent neural network. - Mathieu Galtier (Biol. Cybern., 2015)

Slow feature analysis with spiking neurons and its application to audio stimuli. - Guillaume Bellec; Mathieu Galtier; Romain Brette; Pierre Yger (J. Comput. Neurosci., 2016)

A local Echo State Property through the largest Lyapunov exponent. - Gilles Wainrib; Mathieu Galtier (Neural Networks, 2016)

A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. - Stanislas Chambon; Mathieu Galtier; Pierrick Arnal; Gilles Wainrib; Alexandre Gramfort (CoRR, 2017)

Morpheo: Traceable Machine Learning on Hidden data. - Mathieu Galtier; Camille Marini (CoRR, 2017)

Domain adaptation with optimal transport improves EEG sleep stage classifiers. - Stanislas Chambon; Mathieu N. Galtier; Alexandre Gramfort (PRNI, 2018)

Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity. - Tomasz Górski; Romain Veltz; Mathieu Galtier; Hélissande Fragnaud; Jennifer S. Goldman; Bartosz Telenczuk; Alain Destexhe (J. Comput. Neurosci., 2018)

Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning. - Mathieu N. Galtier; Camille Marini (CoRR, 2019)

The future of digital health with federated learning. - Nicola Rieke; Jonny Hancox; Wenqi Li 001; Fausto Milletarì; Holger R. Roth; Shadi Albarqouni; Spyridon Bakas; Mathieu N. Galtier; Bennett A. Landman; Klaus H. Maier-Hein; Sébastien Ourselin; Micah J. Sheller; et al. (npj Digit. Medicine, 2020)

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (CoRR, 2022)

Collaborative Drug Discovery: Inference-level Data Protection Perspective. - Balazs Pejo; Mina Remeli; Adam Arany; Mathieu Galtier; Gergely Ács (Trans. Data Priv., 2022)

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (AAAI, 2023)

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. - Wouter Heyndrickx; Lewis H. Mervin; Tobias Morawietz; Noé Sturm; Lukas Friedrich; Adam Zalewski; Anastasia Pentina; Lina Humbeck; Martijn Oldenhof; Ritsuya Niwayama; Peter Schmidtke; Nikolas Fechner; et al. (J. Chem. Inf. Model., 2024)