Functional network reorganization during learning in a brain-computer interface paradigm
Functional network reorganization during learning in a brain-computer interface paradigm
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DOI：
10.1073/pnas.0808113105
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发表时间：
2008-12-09
影响因子：
11.1
通讯作者：
Schwartz, Andrew B.
中科院分区：
文献类型：
--
作者：
Jarosiewicz, Beata;Chase, Steven M.;Schwartz, Andrew B.
研究学习的神经关联的努力受到发生学习的网络规模的阻碍。为了理解神经元网络中与学习相关的变化的重要性，有必要了解网络作为一个整体是如何产生行为的。在这里，我们介绍了一种可以直接扰动大脑皮层网络输出的范例，并详细研究了补偿性变化的神经基础。利用脑机接口，在清醒、行为正常的猴子的运动皮质中同时记录的数十个神经元被用来控制三维虚拟现实环境中光标的移动。这一设备在记录的神经元的放电和表达的行为(光标移动)之间建立了精确、明确的映射。在一系列实验中，我们迫使动物重新学习神经元子集中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了行为策略的变化，还反映了单个神经元对群体误差信号的相对贡献。
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.