Department of Bioengineering, Imperial College London, London, UK.
Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada.
Nat Commun. 2024 May 14;15(1):4084. doi: 10.1038/s41467-024-48008-7.
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
动物可以快速将习得的动作适应外部干扰，并且它们现有的运动技能库可能会影响它们适应的容易程度。长期学习会导致神经连接的持久变化，从而塑造适应过程中可以产生的活动模式。在这里，我们通过使用递归神经网络对运动皮层神经群体动力学进行建模，研究了通过从头学习获得的神经群体的现有活动模式如何影响后续的适应。我们根据不同的学习经验，在不同的运动技能库上对网络进行训练，这些运动技能库包含不同数量的运动。具有多个运动的网络具有更受约束和更稳健的动力学，这与可用的群体活动模式中更明确的神经“结构”组织相关联。这种结构促进了适应，但仅当外部干扰引起的变化与输入的组织以及从头学习中获得的神经活动中的结构一致时才会如此。这些结果突出了技能获取中的权衡，并展示了不同的学习经验如何塑造神经群体活动和随后的适应的几何性质。
