• artificial intelligence
• cerebellar ataxia
• cerebellum
• robotics
• spiking neural network

The thesis represents an interdisciplinary exploration that bridges the fields of computational neuroscience and robotics to delve into the intricate connections between neural alterations within the cerebellum and adaptive motor behaviors, with a specific focus on prism adaptation. By employing a biologically inspired spiking neural network and integrating it with a simulated robotic arm, the research investigates motor behavior in patients affected by cerebellar ataxia, shedding light on the underlying neural mechanisms that influence motor adaptation. Through this approach, the thesis aims to advance our understanding of the neural basis of motor control and adaptation, offering valuable insights into the implications of cerebellar dysfunction on motor behaviors. By studying these phenomena within a non-invasive controlled experimental set-up involving robotics and computational models, the thesis aims to contribute to the development of novel therapeutic interventions and rehabilitation strategies aimed at improving motor function in clinical settings.