A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and pre-motor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission, and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.

中文翻译：

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学习相关神经重组过程中运动皮层网络状态的稳定性。

运动皮层回路中神经活动和神经元与运动关系的实质性重组伴随着运动学习过程中可重复运动模式的出现。关于这种神经活动重组的风暴如何影响循环皮层回路中网络状态的稳定性，我们知之甚少。为了研究这个问题，我们重新分析了数据，在这些数据中，我们通过群体钙成像记录了 14 天小鼠前肢运动和前运动皮层第 2/3 层和第 5 层锥体神经元相同神经群体的活动。学习杠杆按压任务。我们发现，在神经群体活动及其与整个学习过程中杠杆运动的关系的广泛重组期间，运动皮层网络状态相对于关键网络状态保持稳定。具体而言，第 2/3 层皮层电路不断显示出在临界网络状态下运行的有力证据，该状态可最大化信息容量和传输，并在网络鲁棒性和灵活性之间取得平衡。相比之下，第 5 层电路在所有 14 天的记录和学习过程中都远离关键网络状态。总之，该结果表明，学习过程中突触、神经元和神经连接的广泛延展性在关于动态临界性的稳定和特定层网络状态的约束下运行，