Closed-loop brain-machine interfaces (BMIs) hold significant promise for restoring autonomy to motor-disabled subjects, such as amputees, tetraplegic patients, and others with motor impairments. Current research in BMIs focuses on restoring proprioceptive feedback through direct cortical stimulations. Typically, these systems are tested using animal models, such as mice, to evaluate their real-world applicability. However, few studies have explored informing these systems with in-silico models to predict proprioceptive feedback. In this study, we take a first step in this direction by investigating whether a musculoskeletal forelimb model of the mouse can be controlled to achieve reaching movements. We demonstrate that this musculoskeletal forelimb model can reproduce reaching movements in good agreement with recorded experimental data from mice. In the future, this model is intended to serve as the basis for artificial cortical feedback by predicting proprioceptive signals from the computational muscles in the model. We hypothesize that using this proprioceptive feedback will help to develop enhanced closed-loop neural prostheses.