Thesis Project Form
Title (tentative): Realistic Neural Drive Modelling in various physiological conditions| Thesis advisor(s): Sanguineti Vittorio, Sofiane Boudaoud, Jeremy Laforet (Université de Technologie de Compiègne, France) | E-mail: |
| Address: Via All'Opera Pia, 13 - 16145 Genova | Phone: (+39) 010 33 56487 |
Description
Motivation and application domain
Over the last years, the Compiegne team developed a multiscale, multiphysics model of skeletal muscle, mainly applied to the biceps brachii.
General objectives and main activities
The goal of the thesis work will be to take into account the very recent literature on the subject to build a new neural drive model, generating a realistic Motor Unit recruitment scheme, to be later merged into the muscle model.
This model should produce the neural input of all the motor units of the
muscle model, tailored for each specific anatomy simulated. It should be able to simulated both isometric and anisometric contraction of the muscle handling various physiological contexts as fatigue and aging.
The expected end product is a stand-alone model, implemented in Python, that can be later interfaced with our existing models, or even included into them.
This model should produce the neural input of all the motor units of the
muscle model, tailored for each specific anatomy simulated. It should be able to simulated both isometric and anisometric contraction of the muscle handling various physiological contexts as fatigue and aging.
The expected end product is a stand-alone model, implemented in Python, that can be later interfaced with our existing models, or even included into them.
Training Objectives (technical/analytical tools, experimental methodologies)
Physiological modeling
Python programming
Python programming
Place(s) where the thesis work will be carried out:
Additional information
Pre-requisite abilities/skills: BMBI Laboratory, Université de Technologie de Compiègne, France
Maximum number of students: 1
Financial support/scholarship: Erasmus