#BAYESIAN IN COMPREHENSIVE META ANALYSIS SOFTWARE#

Parietal is also strongly involved in open-source software development Statistical and machine learning methods for large-scale data,.Encoding and decoding models for cognitive imaging,.Modeling for neuroimaging population studies,.This general theme splits into four research axes: Interest in machine learning techniques and applications of human Statistical inference based on neuroimaging data, with a particular The Parietal team focuses on mathematical methods for modeling and Understanding and simulation of the brain and the nervous systemДЎ Team members, visitors, external collaborators Research Scientists Other Research Topics and Application Domains 10.3.1 Internal or external Inria responsibilities.10.1.5 Leadership within the scientific community.Member of the conference program committees.HARMONIC and MULTIFRACTAL ANALYSES: From Mathematics to Quantitative Neurosciences.LIDL: Learning priors for Inverse Problem Resolution through Deep-Learning.Inria - AP-HP Chaire-IA EDS_PERI-OP: Building a data warehouse for perioperative health inside the EDS/AP-HP for testing, preventing and following cardiovascular and neurocognitive complications.LearnI: learning data integration, from discrete entities to signals.Knowledge and representations integration on the brain.BrAIN: Bridging Artificial Intelligence and Neuroscience.AI-Cog: AI for Aging Societies: From Basic Concepts to Practical Tools for AI-Facilitated Cognitive Training.: Standardization, sharing and analysis of MEEG data simplified by BIDS.DARLING: Distributed adaptation and learning over graph signals.

DirtyData: Data integration and cleaning for statistical analysis.Neuroref: Mathematical Models of Anatomy / Neuroanatomy / Diffusion MRI.9.3.2 Collaborations with major European organizations.9.2.1 Visits of international scientists.9.1.3 Participation in other international programs.8 Bilateral contracts and grants with industry.7.10 Implicit differentiation of Lasso-type models for hyperparameter optimization.7.9 NeuMiss networks: differentiable programming for supervised learning with missing values.7.8 DiCoDiLe: Distributed Convolutional Dictionary Learning.7.7 NeuroQuery, comprehensive meta-analysis of human brain mapping.7.6 Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.7.5 3D variable-density SPARKLING trajectories for high-resolution T2 в†-weighted Magnetic Resonance imaging.7.4 Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.7.3 Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control.7.2 Fine-grain atlases of functional modes for fMRI analysis.7.1 SubjectвЂђspecific segregation of functional territories based on deep phenotyping.Current challenges in human neuroimaging (acquisition+analysis).Macroscopic Functional cartography with functional Magnetic Resonance Imaging (fMRI).1 Team members, visitors, external collaborators.

Bayesian in comprehensive meta analysis