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Uncovering the mechanisms behind rapid motor adaptation in the brain using recurrent neural networks

CORDIS · observation · Publication date unknown

roscience are very precise for modelling neurons and synapses (i.e., the connections between neurons), but fall short when it comes to learning behaviourally relevant tasks. In contrast, methods from machine learning are becoming increasingly more powerful. My innovative idea is that instead of focusing on skill acquisition, which is slow, I propose to focus on rapid adaptation where recordings during its whole duration is feasible. I propose to use data-driven machine learning methods alongside newly acquired motor datasets to uncover where changes occur in the brain during motor adaptation, which brain regions are involved, which rules govern the changes, and how different motor skills interact. Unravelling the fundamental mechanisms underpinning rapid motor adaptation will equip me with the knowledge necessary to engineer tools that accelerate motor adaptation, particularly for medical applications. 100 million people in the EU alone suffer from a disability including movement disorders. I propose to leverage these tools to accelerate how quickly users can adapt to intracortical

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recordType

award

status

SIGNED

region

EU

value

1993474

unit

EUR