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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.
Highlights
In nature, the activity of the brain of an individual interacting with the environment is conditioned by the response of the environment itself, in that the output of the brain is relevant only if it has the ability to impact the future and the input the brain receives
We show how the simulation of brain activity by neural mass models allows replicating the evolution of functional connectivity in mouse brain after a stroke and under rehabilitation
Spiking Network Model for Simulation of Slow Wave Activity in Peri-Infarct Cortex Besides the phenomenological neural mass model for the oscillatory activity that we have used in the brain network models (BNM), we show an alternative spiking neural model to reproduce local brain activity in the acute phase after stroke
Summary
The activity of the brain of an individual interacting with the environment is conditioned by the response of the environment itself, in that the output of the brain is relevant only if it has the ability to impact the future and the input the brain receives This “closed-loop” can be simulated in a virtual world, where simulated experiments reproduce actions (output from the brain) that have consequences (future input to the brain) (Zrenner et al, 2016). From a comparison of the real and model-based data, the features that are most important to replicate the real experiment are identified, and novel insights are generated (Figure 1) To realize such a complex virtual system, many choices can be made, for instance on the brain model or spinal cord model that best represent the salient features of experimental measures to be replicated. By briefly introducing the state of the art in brain and spinal cord modeling, we will discuss few classes of models to pick from an ideal library
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