神经修复术后慢波睡眠中涌现任务相关集合的再激活。
Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning.
机构信息
1] Neurology and Rehabilitation Department, San Francisco VA Medical Center, San Francisco, California, USA. [2] Department of Neurology, University of California, San Francisco, California, USA.
1] Neurology and Rehabilitation Department, San Francisco VA Medical Center, San Francisco, California, USA. [2] Department of Psychiatry, San Francisco VA Medical Center, San Francisco, California, USA. [3] Department of Psychiatry, University of California, San Francisco, California, USA.
出版信息
Nat Neurosci. 2014 Aug;17(8):1107-13. doi: 10.1038/nn.3759. Epub 2014 Jul 6.
Brain-machine interfaces can allow neural control over assistive devices. They also provide an important platform for studying neural plasticity. Recent studies have suggested that optimal engagement of learning is essential for robust neuroprosthetic control. However, little is known about the neural processes that may consolidate a neuroprosthetic skill. On the basis of the growing body of evidence linking slow-wave activity (SWA) during sleep to consolidation, we examined whether there is 'offline' processing after neuroprosthetic learning. Using a rodent model, we found that, after successful learning, task-related units specifically experienced increased locking and coherency to SWA during sleep. Moreover, spike-spike coherence among these units was substantially enhanced. These changes were not present with poor skill acquisition or after control awake periods, demonstrating the specificity of our observations to learning. Notably, the time spent in SWA predicted the performance gains. Thus, SWA appears to be involved in offline processing after neuroprosthetic learning.
脑机接口可以实现神经对辅助设备的控制。它们也为研究神经可塑性提供了重要平台。最近的研究表明，学习的最佳参与对于稳健的神经假肢控制至关重要。然而，对于可能巩固神经假肢技能的神经过程知之甚少。基于越来越多的证据将睡眠期间的慢波活动 (SWA) 与巩固联系起来，我们研究了在神经假肢学习后是否存在“离线”处理。使用啮齿动物模型，我们发现，在成功学习后，与任务相关的单元在睡眠期间特异性地经历了与 SWA 的增加锁定和相干性。此外，这些单元之间的尖峰尖峰相干性得到了显著增强。这些变化在技能获取不佳或控制清醒期间不存在，表明我们的观察结果具有学习的特异性。值得注意的是，SWA 时间预测了性能增益。因此，SWA 似乎参与了神经假肢学习后的离线处理。
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