Project
abstract:
Learning is a brain network phenomenon thought to arise from
synergistic interactions between multiple brain regions. Although
central, this hypothesis has never been fully tested, yet. Indeed,
progress has been limited by the lack of approaches for studying
brain interactions beyond pairwise relations, the so-called higher
order interactions (HOIs). Our objective is to build a theoretical
and data analysis framework to demonstrate the role of HOIs in
human brain networks supporting causal learning. The Hinteract
project will be composed of three scientific work packages (WPs).
WP1 will develop a novel informational theoretical approach to
infer task-related (functional) HOIs from neural time series and
will characterise HOIs supporting causal learning using a
multiscale dataset including magnetoencephalography (MEG) and
intracranial stereo-electroencephalography (SEEG) data. WP2 will
develop a network science formalism to analyse the structure and
dynamics of functional HOIs patterns, and it will characterise the
hierarchical organisation of learning-related HOIs inferred in
WP1. WP3 will compile and share the neuroinformatics tools
developed in the project and it will make it interoperable with
the EBRAINS infrastructure. Overall, our project will reveal
whether causal learning is supported by cerebral HOIs, and produce
a theoretical and computational framework for the study of HOIs in
brain networks that will be shared with the scientific community.
Workpackages:
WP1:
Inference of learning-related high-order interactions in the brain
WP2:
Analysis of learning-related high-order interactions in the brain
using network science approaches
WP3:
Integrated neuroinformatics tools for HOI analysis and sharing on
EBRAINS infrastructure
The
hired postdoctoral researcher will mainly work on WP2,
i.e., on the development of new formalisms and methods to apply to
higher order interaction patterns identified in the data analyzed
in WP1. We are looking for a candidate with a strong background in
statistical physics, complex networks, computational neuroscience
and programming, and a strong interest in interdisciplinary work.
Having already worked on higher order networks is a plus but not
required.
The
hired postdoctoral researcher will be based in the CPT on the
Luminy Campus (south of Marseille) but will also devote a
significant part of their time at the INT in the La Timone campus.
Position
starting date: October
1st, 2024 (negotiable)
Duration
of the position: 1
year, renewable
Deadline for applications: June 15th, 2024
TO APPLY:
- please send CV and motivation letter, and
- have your supervisors send directly two letters of
recommendation,
within
June 15th, 2024 to
alain.barrat @ cpt.univ-mrs.fr and andrea.brovelli
@ univ-amu.fr