Nicolas Levernier

Attractor neural networks and the resolution of a brain’s self-positioning system

Team: Hervé Rouault (CPT/INMED), Jérôme Epstein (INMED)

His background

2019 - 2020 | Postdoctoral internship, Gravitropism : from transportation to redistribution
of growth hormone in plant after inclination - IUSTI in Marseille

2017 - 2019 | Postdoctoral internship, Cytoskeletal instabilités - Université de Genève

2014 - 2017 | PhD (physics), First-passage time of non-markovian processes - LJP and LPTMC (UPMC)

2013 - 2014 | Master 2 ICFP, Physique Macroscopique et Complexité - ENS

2010 - 2013 | Theoretical
Physics in Ecole Polytechnique

About his postdoctoral project

Self-localization based on internal and/or external sensory information is essential to animal survival. The hippocampus plays a crucial role in this process by forming a mental map of the environment. The factors influencing internal spatial coding resolution are poorly understood. Recent large-scale in vivo recordings of hippocampal neuronal activity in mice navigating virtual reality environments show local variations in spatial coding resolution. They could result from genuine differences within a single internal map or possible switches between different maps with different spatial resolutions. To solve this issue, we propose to use inference models based on functional connectivity (Ising models) as a neural activity decoder to decipher at high speed the neuronal assemblies or maps in use. Map switching will be revealed as instabilities (or flickers). In a second step, we will build an attractor neuronal network model fed by both external and internal information to investigate mechanisms of spatial resolution modulation.

 
amidex
amu
anr
cnrs
ECM blanc
inserm
investissement d’avenir