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Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Neurosciences
Année
2014
Starting
sept. 2014
État
Open
Sujet
The Brain Code
Thesis advisor
SEBAG Michèle
Co-advisors
Sylvain Chevallier, Université Versailles St Quentin
Laboratory
Collaborations
CRICM, La Pitié Salpétrière, Denis Schwartz et Fabrizio de Vico Fallani
Abstract
Among the most fascinating applications of Machine Learning are Neuroimaging and
Brain Machine Interfaces, which use the traces of the subject mental activity to respectively
build a model of the brain functional structure, or convert the subject’s brainwave functional
data into the command law of a mechatronic device.

Among the main ML challenges faced by both above domains is the variability of the
brainwave data, among different subjects and among different sessions for a given subject.
Assuming that single subject brainwave data do involve recurrent information patterns, the PhD
aims at building a subject-dependent code exploiting the brainwave functional data regularities.

A non-linear coding approach, based on neural nets (auto-encoders and deep neural nets), will apply to brainwave data the feature design mechanisms at the root of deep learning. The question is
to enforce the interpretability of the non-linear code in order to enable its qualitative evaluation.

Context
Objectives
Work program
Extra information
Prerequisite
Détails
Expected funding
Research contract
Status of funding
Expected
Candidates
Utilisateur
michele-martine.sebag
Créé
Jeudi 12 juin 2014 16:06:32 CEST
dernière modif.
Jeudi 12 juin 2014 16:06:32 CEST

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Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr