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

机译：脑机接口控制人类前脑内皮层的内在可变学习

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摘要

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.
机译：虽然动物研究提供了了解理解学习和适应的神经基础的重要见解，但由于缺乏口头沟通，它们往往不会在不同的学习机制之间解散。为了克服这一限制，我们研究了人类脑机接口（BMI）范式的学习机制及其限制。四轮参与者通过调节前部内部区域（AIP）中的单神经元活性来控制2D计算机光标。通过扰动神经元到运动映射，参与者学会通过采用目标重新瞄准策略来调节记录的神经元的活动来解决扰动。但是，当没有认知策略足以产生正确的反应时，AIP未能适应扰动。这些发现表明，学习受到预先存在的神经元结构的限制，尽管AIP可能需要更多的训练时间来学习在认知重新适应无法解决扰动时产生新颖的活动模式。

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