Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Anna Letizia Allegra Mascaro, Egidio Falotico, Spase Petkoski, Maria Pasquini, Lorenzo Vannucci, Nuria Tort-Colet, Emilia Conti, Francesco Resta, Cristina Spalletti, Shravan Tata Ramalingasetty, Axel von Arnim, Emanuele Formento, Emmanouil Angelidis, Camilla Blixhavn, Trygve Leergaard, Matteo Caleo, Alain Destexhe, Auke Ijspeert, Silvestro Micera, Cecilia Laschi, Viktor Jirsa, Marc-Oliver Gewaltig und Francesco Pavone

Frontiers in Systems Neuroscience, 14

Juli 2020 · DOI: https://doi.org/10.3389/fnsys.2020.00031


Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

Stichworte: Neuromorphic Computing, HBP, Human Brain Project, Neurorobotics, Neurorobotics Platform, Neuroscience, Artificial Intelligence, KI, Spiking Neural Networks neurorobotics, neuromorphic, neuromorphic computing, HBP, NRP, virtual, robotics

Url: https://www.frontiersin.org/articles/10.3389/fnsys.2020.00031/full