Spiking cerebellar model with damaged cortical Neural population reproduces human ataxic behaviors in perturbed upper limb reaching

The fundamental role of the cerebellum in motor learning explains the deficits of cerebellar patients in adaptation to a changing environment. For example, lesions to the cerebellar cortex compromise performance during tasks like reaching a target under a force field perturbation. However, the exact relationship between neural damages and misbehaviors still needs to be clarified. To this aim, it could become a turning point to exploit a bio-inspired cerebellar model able to simulate multiple tasks in closed-loop, under physiological and different pathological conditions. In the present study, we used a well-established Spiking Neural Network representing a cerebellar microcomplex to reproduce alterations in a perturbed reaching task, after lesions to the neural population in the cerebellar cortex. Following a multiscale approach, we explored different amounts of damage and analyzed the modified behavior, matching the results of a literature reference study. Then, we could make predictions about the underlying altered neural activity, showing the neural causes of high-level impairments. The results demonstrate the generalization capabilities of the model, extending previous studies on different lesions and tasks. We showed the strong potentialities of computational neuroscience in investigating cerebellar diseases through a non-invasive approach, allowing to isolate damages, test multiple configurations, and suggest treatments thanks to a deeper understanding of pathologies.