Bowler John C, Azhar Dua, Jensen Cambria M, Lee Hyun-Woo, Heys James G
Department of Neurobiology.
University of Utah, Salt Lake City, UT, USA.
bioRxiv. 2025 May 13:2025.05.13.653873. doi: 10.1101/2025.05.13.653873.
Animals can solve new, complex tasks by reusing and adapting what they've learned before. This kind of flexibility depends not just on having prior experience, but on how that experience was structured in the first place. The design of early training curriculum is especially important: poorly structured experiences can hinder abstraction and limit generalization, while carefully structured training promotes more flexible and adaptive behavior. Yet, the neural mechanisms supporting this process remain unclear. To investigate how early training shapes learning we first trained recurrent neural networks (RNNs) on variants of an odor-timing task previously used to study complex timing behavior in mice. We then tested the RNN predictions on how previous experience affects generalization using behavioral and electrophysiological recordings in mice trained on the same task using staged training sequences. RNNs and mice trained without well-structured early experience developed rigid strategies and made repeated errors. In contrast, those given more balanced early training were better able to generalize and showed similar neural activity patterns that reflected the task's underlying temporal structure. Using dynamical systems approaches, we reveal a mechanism for this effect: networks trained with appropriately structured curricula developed distinct dynamical motifs that support the correct abstractions when complexity was increased. Networks that lacked early training or received remedial curricula developed single fixed-point solutions that failed to generalize beyond the training stimuli. Together, these findings demonstrate that it is not just the presence of prior experience, but its structure, that governs how flexible and generalizable knowledge emerges in both biological systems and computational models.
动物可以通过复用和调整它们之前学到的东西来解决新的复杂任务。这种灵活性不仅取决于有先前的经验，还取决于该经验最初是如何构建的。早期训练课程的设计尤为重要：结构不佳的经验会阻碍抽象化并限制泛化能力，而精心构建的训练则会促进更灵活和适应性更强的行为。然而，支持这一过程的神经机制仍不清楚。为了研究早期训练如何塑造学习，我们首先在一种气味定时任务的变体上训练循环神经网络（RNN），该任务先前用于研究小鼠的复杂定时行为。然后，我们使用在相同任务上接受分阶段训练序列训练的小鼠的行为和电生理记录，测试了RNN关于先前经验如何影响泛化的预测。没有经过结构良好的早期经验训练的RNN和小鼠形成了僵化的策略并反复出错。相比之下，那些接受了更平衡的早期训练的RNN和小鼠能够更好地进行泛化，并表现出类似的神经活动模式，这些模式反映了任务的潜在时间结构。使用动态系统方法，我们揭示了这种效应的一种机制：用适当结构的课程训练的网络形成了不同的动态模式，当复杂性增加时，这些模式支持正确的抽象。缺乏早期训练或接受补救课程的网络形成了单一的定点解决方案，无法在训练刺激之外进行泛化。总之，这些发现表明，不仅先前经验的存在，而且其结构，都决定了生物系统和计算模型中灵活且可泛化的知识是如何出现的。
