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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Functional Network Reorganization During Learning In A Brain-computer Interface Paradigm
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Functional Network Reorganization During Learning In A Brain-computer Interface Paradigm

机译：在脑机接口范式学习过程中的功能网络重组

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摘要

Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.
机译：研究学习的神经相关性的努力受到学习发生的网络规模的阻碍。为了了解神经元网络中与学习相关的变化的重要性，有必要了解神经网络如何整体上产生行为。在这里，我们介绍一种可以直接扰动皮层网络输出的范例，并详细研究补偿性变化的神经基础。使用脑计算机接口，在行为觉醒的猴子的运动皮层中同时记录了数十个神经元，这些猴子被用来控制三维虚拟现实环境中光标的移动。该设备在记录的神经元的放电和表达的行为（光标移动）之间创建精确，定义明确的映射。在一系列实验中，我们强迫动物重新学习神经元子集中的神经放电和光标移动之间的关联，并评估网络如何变化以进行补偿。我们发现，神经活动的变化不仅反映出行为策略的改变，而且还反映了单个神经元对种群误差信号的相对贡献。

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