| Description | Aim:
The bottom-up strategy is one possible approach to studying the brain. It attempts to construct biologically realistic neuronal network models based on the detailed knowledge of the constituting components. The assumption is that with sufficient accuracy in structure and dynamics, the correct functions will emerge. Toward this aim, we want to simulate a control system integrated with a detailed model of the cerebellum able to perform an adaptive reaching task.
The candidate will first investigate how to encode motion-related signals as cerebellar inputs; then, they will analyse how these signals propagate into the cerebellar circuit and turn into the physiological cerebellar output signals. The model’s overall performance will be evaluated by embedding it in a comprehensive control system to drive a closed-loop reaching task simulation. Project phases: - Literature review
- Definition and analysis of somatotopic input for the cerebellar circuit
- Tuning and Refinement of the model
- Testing in an adaptive reaching task protocol
Requirements: - Knowledge of Python
- Knowledge of C++ (not mandatory, but preferable)
- Willingness to work in a collaborative environment
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