SINNAPS

The main objective of the present project is to derive laws governing neuronal integration in networks, through morphological and activity-related changes, and for neurons with both myelinated and unmyelinated axons. The experimental collaborators of the project are the Haas lab, providing data on voltage propagation in dendrites, the Khadra Lab, providing expertise on computational modeling and the MBP Hypomyelination consortium providing data on myelin adaptation. The work will be divided into three different objectives: 1. Understand the specific dynamics of voltage propagation and ionic concentration in small neuronal compartments through numerical simulations in 2D and 3D domains. We will use the Discrete Duality Finite Volume Method to simulate the Poisson Nernst-Planck system of equation in multi-domains frameworks representing various neuronal compartments. 2. Determine the rules underlying synaptic pruning and reinforcement after neuronal stimulation. Using the Haas lab data, we aim at deciphering the rules underlying synaptic plasticity due to incoming inputs, and to build a stochastic plasticity model, at the scale of a single synapse, incorporating these features. 3. Determine the rules underlying adaptative myelination, and especially how electrical activity in axons shapes myelin sheath length and thickness. Together with A. Khadra, we will build the first axon-oligodendrocyte model relating axon electrical activity to myelin formation. Using the data of the MBP Hypomyelination consortium, coming from a series of mouse lines lacking myelin, we will test our hypothesis that the rules underlying myelin adaptation in are going toward a ’rescue of the system’, to maintain a quasi-normal electrical activity. The long term goal of this project is to understand how synaptic laws at the neuronal level shape the complex yet robust neuronal network architectures observed in the brain.