Computational Neuroscience

The Computational Neuroscience team is a research team in computer science modeling neural networks, with the aim to study synergies between different kinds of learning. Our objective is to better understand these synergies and the impact of certain dysfunctions by realizing efficient computer models and by driving reproducible experiments dedicated to the emulation of autonomous behaviors and the realization of cognitive functions. They have an impact in the fields of Machine Learning, Artificial Intelligence and Situated Cognition, but they also question our neuroscientific and medical colleagues and offer them new objects of study at the level of neuronal and behavioral phenomena. Our research can be presented according to themes corresponding to learn to predict values and to learn to control behavior.

Selected publications

A global framework for a systemic view of brain modeling
Frederic Alexandre
Brain Inf.. 2021-02-16
10.1186/s40708-021-00126-4

Teach Your Robot Your Language! Trainable Neural Parser for Modeling Human Sentence Processing: Examples for 15 Languages
Xavier Hinaut, Johannes Twiefel
IEEE Trans. Cogn. Dev. Syst.. 2020-06-01
10.1109/TCDS.2019.2957006

New journal for reproduction and replication results
Etienne B. Roesch, Nicolas Rougier
Nature. 2020-05-01
10.1038/d41586-020-01328-2

A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning
Pramod Kaushik, Jérémie Naudé, Surampudi Bapi Raju, Frédéric Alexandre
. 2020-02-07
10.1101/2020.02.06.936997

Interacting roles of lateral and medial Orbitofrontal cortex in decision-making and learning : A system-level computational model
Bhargav Teja Nallapu, Frédéric Alexandre
. 2019-12-06
10.1101/867515

Coordination over a unique medium of exchange under information scarcity
Aurélien Nioche, Basile Garcia, Germain Lefebvre, Thomas Boraud, Nicolas P. Rougier, Sacha Bourgeois-Gironde
Palgrave Commun. 2019-12-01
10.1057/s41599-019-0362-2

Identification of distinct pathological signatures induced by patient-derived α-synuclein structures in non-human primates
M. Bourdenx, A. Nioche, S. Dovero, M.-L. Arotcarena, S. Camus, G. Porras, M.-L. Thiolat, N. P. Rougier, A. Prigent, P. Aubert, S. Bohic, C. Sandt, F. Laferrière, E. Doudnikoff, N. Kruse, B. Mollenhauer, S. Novello, M. Morari, T. Leste-Lasserre, I. Trigo Damas, M. Goillandeau, C. Perier, C. Estrada, N. Garcia-Carrillo, A. Recasens, N. N. Vaikath, O. M. A. El-Agnaf, M. Trinidad Herrero, P. Derkinderen, M. Vila, J. A. Obeso, B. Dehay, E. Bezard
. 2019-10-31
10.1101/825216

Challenge to test reproducibility of old computer code
Konrad Hinsen, Nicolas Rougier
Nature. 2019-10-01
10.1038/d41586-019-03296-8

Learning to Parse Grounded Language using Reservoir Computing
Xavier Hinaut, Michael Spranger
2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 2019-08-01
10.1109/DEVLRN.2019.8850718

Section focused on machine learning methods for high-level cognitive capabilities in robotics
Tetsunari Inamura, Hiroki Yokoyama, Emre Ugur, Xavier Hinaut, Michael Beetze, Tadahiro Taniguchi
Advanced Robotics. 2019-06-03
10.1080/01691864.2019.1625183

A Robust Model of Gated Working Memory
Anthony Strock, Xavier Hinaut, Nicolas P. Rougier
. 2019-03-28
10.1101/589564

ReScience C: A Journal for Reproducible Replications in Computational Science
Nicolas P. Rougier, Konrad Hinsen
Reproducible Research in Pattern Recognition. 2019-01-01
10.1007/978-3-030-23987-9_14

Using Conceptors to Transfer Between Long-Term and Short-Term Memory
Anthony Strock, Nicolas Rougier, Xavier Hinaut
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. 2019-01-01
10.1007/978-3-030-30493-5_2