  The possibility of simulating in detail in-vivo experiments could be highly
beneficial to the neuroscientific community. It could easily allow for
preliminary testing of different experimental conditions without having to be
constrained by factors such as training of the subjects or resting times
between experimental trials. In order to achieve this, the simulation of the
environment, of the subject and of the neural system, should be as accurate as
possible. Unfortunately, it is not possible to completely simulate physical
systems, alongside their neural counterparts, without greatly increasing the
computational cost of the simulation. For this reason, it is crucial to limit
the simulation to all physical and neural areas that are involved in the
experiment. We propose that using a combination of data analysis and simulated
models is beneficial in determining the minimal subset of entities that have to
be included in the simulation to replicate the in-vivo experiment. In
particular, we focused on a pulling task performed by mice on a robotic
platform before and after lesion of the central nervous system. Here, we show
that, while it is possible to replicate the behaviour of the healthy mouse just
by including models of the mouse forelimb, spinal cord, and recording of the
rostral forelimb area (RFA), it is not possible to reproduce the behaviour of
the post-stroke mouse. This can give us insights on what other elements would
be needed to replicate the complete experiment.
