TY - JOUR
T1 - Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex
AU - Sakellaridi, Sofia
AU - Christopoulos, Vassilios N.
AU - Aflalo, Tyson
AU - Pejsa, Kelsie W.
AU - Rosario, Emily R.
AU - Ouellette, Debra
AU - Pouratian, Nader
AU - Andersen, Richard A.
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/5/8
Y1 - 2019/5/8
N2 - 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. Sakellaridi, Christopoulos, et al. studied the learning mechanism in human AIP using brain-machine interface paradigms. They found that changes in neural activity during learning reflect cognitive re-adaptation mechanisms. When cognitive strategies were not adequate for compensation, AIP failed to learn.
AB - 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. Sakellaridi, Christopoulos, et al. studied the learning mechanism in human AIP using brain-machine interface paradigms. They found that changes in neural activity during learning reflect cognitive re-adaptation mechanisms. When cognitive strategies were not adequate for compensation, AIP failed to learn.
KW - anterior intraparietal cortex
KW - brain-machine interface
KW - individual-neuron learning
KW - intrinsic-variable learning
KW - posterior parietal cortex
KW - spinal cord injury
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U2 - 10.1016/j.neuron.2019.02.012
DO - 10.1016/j.neuron.2019.02.012
M3 - Article
C2 - 30853300
AN - SCOPUS:85065025494
SN - 0896-6273
VL - 102
SP - 694-705.e3
JO - Neuron
JF - Neuron
IS - 3
ER -