Li Nuo, Daie Kayvon, Svoboda Karel, Druckmann Shaul
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA.
Nature. 2016 Apr 28;532(7600):459-64. doi: 10.1038/nature17643. Epub 2016 Apr 13.
Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.
神经活动维持着连接过去和未来事件的表征，通常会持续许多秒。网络模型可以产生持续的和逐渐增强的活动，但对于这些缓慢动态至关重要的正反馈会导致对扰动敏感。在这里，我们使用小鼠运动前皮层中的电生理学和光遗传学扰动来探究运动规划过程中持续神经表征的稳健性。我们表明，准备活动对大规模单侧沉默具有显著的稳健性：驱动特定未来运动的详细神经动力学被网络迅速且有选择地恢复。运动前皮层双侧沉默后选择性并未恢复。因此，对一个半球的扰动会被来自另一个半球的信息纠正。胼胝体切断术表明运动前皮层半球可以独立维持准备活动。正如我们在运动前皮层中观察到的那样，选择性耦合模块之间的冗余是稳健控制系统的一个标志。纳入这些原理的网络模型显示出与数据一致的稳健性。
