Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Highlights

• AIP neurons learn to modulate their activity to compensate for errors in BMI tasks

• Changes in the neural activity reflect a cognitive re-adaptation mechanism

• AIP fails to compensate for errors when novel neural activity patterns are required

• Learning in AIP is constrained by the pre-existing neuronal structure

Summary

Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.