Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity.
Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity.
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DOI：
10.7554/elife.67620
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发表时间：
2022-05-27
期刊：
影响因子：
7.7
通讯作者：
Churchland, Mark M.
中科院分区：
文献类型：
--
作者：
Saxena, Shreya;Russo, Abigail A.;Cunningham, John;Churchland, Mark M.
可以以不同的速度熟练地执行学到的动作。什么样的神经策略可以产生这种灵活性？它们可以通过网络建模来预测和理解吗？我们训练猴子以不同的速度执行骑车任务，并训练人工循环网络来生成经验肌肉活动模式。网络解决方案反映了平滑良好的动力学需要低轨迹缠结的原则。网络解决方案具有一致的形式，可以产生定量和定性的预测。为了评估预测，我们分析了同一任务期间记录的运动皮层活动。反应支持这样的假设：主要神经信号反映的不是肌肉活动，而是产生肌肉活动的网络级策略。网络活动比肌肉活动更好地解释了单神经元反应。同样，神经群体轨迹不是与肌肉轨迹共享其组织，而是与网络解决方案共享组织。因此，皮层活动可以根据通过动力学产生肌肉活动的需要来理解，从而允许对运动速度进行平滑、稳健的控制。
Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.