Search results

Ramirez-Villegas, J.; Besserve, M.; Murayama, Y.; Evrard, H.; Oeltermann, A.; Logothetis, N.: Coupling of hippocampal theta and ripples with pontogeniculooccipital waves. Nature 589 (7840), pp. 96 - 102 (2021)

Ramirez-Villegas, J.; Besserve, M.; Murayama, Y.; Evrard, H.; Oeltermann, A.; Logothetis, N.: Author Correction: Coupling of hippocampal theta and ripples with pontogeniculooccipital waves. Nature 588 (7839), p. E34 (2020)

Kapoor, V.; Besserve, M.; Logothetis, N.; Panagiotaropoulos, T.: Parallel and functionally segregated processing of task phase and conscious content in the prefrontal cortex. Communications Biology 1, 215, pp. 1 - 12 (2018)

Ramirez-Villegas, J.; Willeke, K.; Logothetis, N.; Besserve, M.: Dissecting the Synapse- and Frequency-Dependent Network Mechanisms of In Vivo Hippocampal Sharp Wave-Ripples. Neuron 100 (5), pp. 1224 - 1240 (2018)

Hindriks, R.; Arsiwalla, X.; Panagiotaropoulos, T.; Besserve, M.; Verschure, P.; Logothetis, N.; Deco, G.: Discrepancies between multi-electrode LFP and CSD phase-patterns: A forward modeling study. Frontiers in Neural Circuits 10, 51, pp. 1 - 18 (2016)

Ramirez-Villegas, J.; Logothetis, N.; Besserve, M.: Diversity of sharp-wave–ripple LFP signatures reveals differentiated brain-wide dynamical events. Proceedings of the National Academy of Sciences of the United States of America 112 (46), pp. E6379 - E6387 (2015)

Besserve, M.; Lowe, S.; Logothetis, N.; Schölkopf, B.; Panzeri, S.: Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer. PLoS Biology 13 (9), pp. 1 - 29 (2015)

Laurent, F.; Valderrama, M.; Besserve, M.; Guillard , M.; Lachaux, J.-P.; Martinerie, J.; Forence, G.: Multimodal information improves the rapid detection of mental fatigue. Biomedical Signal Processing and Control 8 (4), pp. 400 - 408 (2013)

Laurent, F.; Valderrama, M.; Besserve, M.; Guillard, M.; Lachaux, J.-P.; Martinerie, J.; Forence, G.: Multimodal information improves the rapid detection of mental fatigue. Biomedical Signal Processing and Control 8 (4), pp. 400 - 408 (2013)

Balduzzi, D.; Ortega, P.; Besserve, M.: Metabolic cost as an organizing principle for cooperative learning. Advances in Complex Systems 16 (02n03), 1350012, pp. 1 - 18 (2013)

Logothetis, N.; Eschenko, O.; Murayama, Y.; Augath, M.; Steudel, T.; Evrard, H.; Besserve, M.; Oeltermann, A.: Hippocampal-cortical interaction during periods of subcortical silence. Nature 491 (7425), pp. 547 - 553 (2012)

Chavez, M.; Besserve, M.; Le Van Quyen, M.: Dynamics of excitable neural networks with heterogeneous connectivity. Progress in Biophysics and Molecular Biology 105 (1-2), pp. 29 - 33 (2011)

Besserve, M.; Martinerie, J.: Extraction of functional information from ongoing brain electrical activity: Extraction en temps-réel d'informations fonctionnelles à partir de l'activité électrique cérébrale. IRBM 32 (1), pp. 27 - 34 (2011)

Besserve, M.; Schölkopf, B.; Logothetis, N.; Panzeri, S.: Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. Journal of Computational Neuroscience 29 (3), pp. 547 - 566 (2010)

Besserve, M.; Sun, R.; Janzing, D.; Schölkopf, B.: A theory of independent mechanisms for extrapolation in generative models. In: 35th AAAI Conference on Artificial Intelligence: A Virtual Conference. 35th AAAI Conference on Artificial Intelligence: A Virtual Conference, February 02, 2021 - February 09, 2021. (accepted)

Besserve, M.; Mehrjou, A.; Sun, R.; Schölkopf, B.: Counterfactuals uncover the modular structure of deep generative models. In: Eighth International Conference on Learning Representations (ICLR 2020). Eighth International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, April 26, 2020 - April 30, 2020. (2020)

Geiger, P.; Besserve, M.; Winkelmann, J.; Proissl, C.; Schölkopf, B.: Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. In: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), 49, pp. 286 - 295. 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22, 2019 - July 25, 2019. Curran, Red Hook, NY, USA (2019)

Besserve, M.; Sun, R.; Schölkopf, B.: Intrinsic disentanglement: an invariance view for deep generative models. In: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models. ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, July 14, 2018 - July 15, 2018. (2018)

Besserve, M.; Shajarisales, N.; Schölkopf, B.; Janzing, D.: Group invariance principles for causal generative models. In: International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, pp. 557 - 565 (Eds. Storkey , A.; Perez-Cruz, F.). 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Playa Blanca, Spain, April 09, 2018 - April 11, 2018. International Machine Learning Society, Madison, WI, USA (2018)

Shajarisales, N.; Janzing, D.; Schölkopf, B.; Besserve, M.: Telling Cause from Effect in Deterministic Linear Dynamical Systems. In: International Conference on Machine Learning, 7-9 July 2015, Lille, France, pp. 285 - 294 (Eds. Bach , F.; Blei, D.). 32nd International Conference on Machine Learning (ICML 2015), Lille, France. International Machine Learning Society, Madison, WI, USA (2015)