Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex
TL;DR
RNNs are trained on an odor timing task used to study complex timing behavior in mice and then tested the model predictions with mouse behavior and medial entorhinal cortex recordings, demonstrating that the structure of prior experience governs how flexible, generalizable knowledge emerges in biological systems and computational models.
Abstract
Animals solve new, complex tasks by reusing and adapting prior knowledge. This flexibility depends not only on the content of experience but also on its structure. Early training curricula are especially important: poorly structured experiences can hinder abstraction and limit generalization. However, the neural mechanisms through which experience shapes future learning remain unclear. Here, we trained recurrent neural networks (RNNs) on an odor timing task used to study complex timing behavior in mice and then tested the model predictions with mouse behavior and medial entorhinal cortex recordings. Without structured early experience, both RNNs and mice developed rigid, error-prone strategies, whereas structured training promoted neural activity reflecting the task’s temporal structure. Using dynamical systems analysis, we examined how different training curricula shaped network dynamics and whether these dynamics supported abstraction and generalization as task complexity increased. These findings demonstrate that the structure of prior experience governs how flexible, generalizable knowledge emerges in biological systems and computational models. The authors use recurrent neural networks to predict how structured prior experience shapes neural dynamics as mice learn flexible timing strategies during complex context-dependent behavior.