Centrale Marseille



e m m m a n u e l . d a u c e @ c e n t r a l e - m a r s e i l l e . f r

Institut de Neurosciences des Systèmes
Inserm UMR1106
Faculté de médecine
Aix-Marseille Université
27, Bd Jean Moulin
13005 Marseille cédex
(00-33) 4 91 05 47 30



HDR (2016) Mémoire / Présentation

Thème de recherche

Emmanuel Daucé is associate professor at the Ecole Centrale de Marseille, doing his research in Computational Neuroscience at the Institut de Neurosciences des Systèmes (France), a joint research unit (Inserm/ CNRS / Aix-Marseille Université). His research lies at the crosssroad of machine learning, artificial intelligence and neuroscience, seeking to develop innovative computational models and methods though remaining consistent with the principles of biological systems.

He graduated from the Ecole Nationale Supérieure d'Electronique, d'Electrotechnique, d'Informatique et d'Hydraulique de Toulouse (1995), and obtained a Ph.D in Knowledge Representation and Formal reasoning from the Ecole Nationale Supérieure de l'Aeronautique et de l'Espace (2000), under the supervision of Bernard Doyon (Inserm) and Manuel Samuelides (ISAE), on learning and plasticity in artificial neural networks with random recurrent connectivity graphs ( Daucé et al, 1998 ). He contributed to extend the model to multiple populations (Daucé et al. ,2001), and spatio-temporal sequence learning (Daucé et al., 2002).
He joined the Institut des Sciences du Mouvement in Marseille in 2001, where he contributed to develop neurally plausible reinforcement schemes in closed-loop control systems (Daucé, 2004, Daucé and Dutech, 2010), address spike-timing dependent plasticity in balanced networks of spiking neurons (Henry et al, 2006, Daucé, 2014) and develop models of dynamic retention in discrete neural-fields (Daucé, 2004).
He more recently joined Viktor Jirsa's group at the Institut de Neurosciences des Systèmes, at the Faculté de Médecine de La Timone (Marseille), where he contributed to develop on-line learning methods for non-stationary data streams - adapted to the case of Brain Computer Interfaces (Daucé and Thomas, 2014), and participated in modelling brain non-stationarities with simple neural-mass dynamics on large-scale connectivity graphs (Golos et al., 2015).

HDR : apprentissage et contrôle dans les architectures neuronales

The brain, beyond its primary sensori-motor and regulation functions, is an outstanding adaptive system, capable of developping novel responses in novel situations. The principles of machine learning, a fast-developping domain, are at stake for a better understanding of the learning processes in the brain. Computational models of learning have provided several success stories, from which the "layered neural networks" are the most famous ones. This HDR dissertation presents different kinds neural networks models, displaying a more strict obedience to the biological constraints, in particular regarding the recurrent aspect of the neuronal interaction graph, the discreteness of the signals emitted by the neurons and the local aspect of the plasticity rules that govern the synaptic changes. We show in particular how recurrent neural networks organize their sensory input in different regions, how the the synaptic plasticity drives the network toward a more "simple" collective activity, allowing a better separation and prediction of the sensory stimuli, and how motor learning can rely on matching motor primitives with sensory data to organize the physical environment. Several projects are proposed, aiming at expanding some of those ideas into large-scale brain activity models, or also for the design of brain-computer interfaces.

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