NEUROFIELD

Analysis and numerical simulation of deterministic and stochastic neural field equations with applications to robotics

Short Description

The present project is concerned with Dynamic Neural Fields (DNF), which is a rapidly growing branch of Mathematical Neuroscience. We will analyze a novel mathematical model in DNF and carry out numerical simulations in direct and inverse problems for Neural Field Equations. Since the effects of random disturbance play a crucial role in DNF, we will pay special attention to stochastic models.

The research will be a joint work of a team in the Instituto Superior Tecnico, Lisbon (which will be focused on the numerical solution of equations and parameter estimation) and a team at the University of Minho (which will concern with the analysis of the mathematical model and applications to Robotics). Each of the teams will hire a post-doc researcher who will be entirely dedicated to this project.

One of the main objectives of the research is to integrate novel mathematical results, both theoretical and numerical, in existing DNF-based control architecture for cognitive robots.

Project Idendification

This project has the ID POCI-01-0145-FEDER-031393 or PTDC/MAT-APL/31393/2017 .

Timeline

The project started in 15 October 2018 and has a duration of 36 months (3 years)


Funding

The total budget of the project is 239742 E, which are divided in two equal parts by the two partners (IST and Uminho). From this amount about 80% are used to hire two researchers who will work full time in this project. The remaining is spent in missions and scientific advisors.

Tasks

  1. Analysis of pattern formation in a novel DNF model supporting a continuum of bump amplitudes.

  2. Development of new efficient numerical tools for 1D and 2D neural field equations.

  3. Data assimilation and parameter estimation in stochastic neural field models .

  4. Testing analytical and numerical results in a robotics application .


Research Team