03Jun 2021 — Dec 2023Lead researcher · IIT Madras
Hierarchical Motor Learning Network
A hierarchical motor network built to understand how skill transfers from a trained arm to the naive arm under visuomotor-rotation adaptation — and what the left and right parietal areas each compute along the way.
PyTorchMotor learningGPU
[ network architecture / adaptation curves ]
// approach
→Build a hierarchical motor network that adapts to a visuomotor rotation.
→Model the transfer of skill from the trained arm to the naive arm.
→Separate the computational roles of the left and right parietal areas in unimanual and bimanual movement.
→Replicate experimentally observed adaptation behaviour with the network.
// outcomes
●Reproduces the lateralized pattern of interlimb transfer seen experimentally.
●Gives a computational account of hemispheric specialization in motor control.
// related publications
A lateralized motor network in order to understand adaptation to visuomotor rotation
J. Neural Engineering · 2024