@article{allegramascaro-Experimental-2020,
 abstract = {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.},
 author = {Allegra Mascaro, Anna Letizia and Falotico, Egidio and Petkoski, Spase and Pasquini, Maria and Vannucci, Lorenzo and Tort-Colet, Núria and Conti, Emilia and Resta, Francesco and Spalletti, Cristina and Ramalingasetty, Shravan Tata and von Arnim, Axel and Formento, Emanuele and Angelidis, Emmanouil and Blixhavn, Camilla H. and Leergaard, Trygve B. and Caleo, Matteo and Destexhe, Alain and Ijspeert, Auke and Micera, Silvestro and Laschi, Cecilia and Jirsa, Viktor and Gewaltig, Marc-Oliver and Pavone, Francesco S.},
 doi = {10.3389/fnsys.2020.00031},
 issn = {1662-5137},
 journal = {Frontiers in Systems Neuroscience},
 keywords = {brain modeling,closed-loop simulation,connectome,Functional     Connectivity,in silico,mean-field model,motor control,pulling,Rehabilitation,simulation,Spiking Neuronal Network,Stroke},
 langid = {english},
 publisher = {Frontiers},
 shorttitle = {Experimental and Computational Study on Motor Control and Recovery After Stroke},
 title = {Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience},
 url = {https://www.frontiersin.org/articles/10.3389/fnsys.2020.00031/full},
 urldate = {2020-12-14},
 volume = {14},
 year = {2020}
}