Bioengineering Graduate Program, University of California San Francisco, San Francisco, CA, USA.
Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA.
Nat Commun. 2022 May 4;13(1):2450. doi: 10.1038/s41467-022-30069-1.
Animals can capitalize on invariance in the environment by learning and automating highly consistent actions; however, they must also remain flexible and adapt to environmental changes. It remains unclear how primary motor cortex (M1) can drive precise movements, yet also support behavioral exploration when faced with consistent errors. Using a reach-to-grasp task in rats, along with simultaneous electrophysiological monitoring in M1 and dorsolateral striatum (DLS), we find that behavioral exploration to overcome consistent task errors is closely associated with tandem increases in M1 and DLS neural variability; subsequently, consistent ensemble patterning returns with convergence to a new successful strategy. We also show that compared to reliably patterned intracranial microstimulation in M1, variable stimulation patterns result in significantly greater movement variability. Our results thus indicate that motor and striatal areas can flexibly transition between two modes, reliable neural pattern generation for automatic and precise movements versus variable neural patterning for behavioral exploration.
动物可以通过学习和自动化高度一致的动作来利用环境中的不变性;然而,它们也必须保持灵活性并适应环境变化。目前尚不清楚初级运动皮层 (M1) 如何既能驱动精确的运动,又能在面对一致的错误时支持行为探索。我们在大鼠中使用了伸手抓握任务,并在 M1 和背外侧纹状体 (DLS) 中进行同步电生理监测,发现克服一致任务错误的行为探索与 M1 和 DLS 神经变异性的串联增加密切相关;随后,一致的集合模式随着向新的成功策略的收敛而恢复。我们还表明,与 M1 中可靠的颅内微刺激模式相比,可变的刺激模式会导致运动变异性显著增加。因此,我们的研究结果表明,运动和纹状体区域可以在两种模式之间灵活转换,可靠的神经模式生成用于自动和精确的运动,而可变的神经模式生成用于行为探索。