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Peer-reviewedCC BY-NC-NDNature Neuroscience

Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex

  • John C. Bowler1,2
  • Dua Azhar1,2
  • Cambria M. Jensen1,2
  • Hyun‐Woo Lee1,2
  • James G. Heys3,4
4 affiliations
  1. 1Department of Neurobiology, University of Utah, Salt Lake City, UT, USA
  2. 2Interdepartmental PhD Program in Neuroscience, Department of Neurobiology, University of Utah, Salt Lake City, UT, USA
  3. 3Department of Neurobiology, University of Utah, Salt Lake City, UT, USA. [email protected]
  4. 4Interdepartmental PhD Program in Neuroscience, Department of Neurobiology, University of Utah, Salt Lake City, UT, USA. [email protected]
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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.

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Cite

BibTeXRIS
BibTeX
@article{bowler2026structured,
  title = {{Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex}},
  author = {John C. Bowler and Dua Azhar and Cambria M. Jensen and Hyun‐Woo Lee and James G. Heys},
  journal = {Nature Neuroscience},
  year = {2026},
  month = sep,
  doi = {10.1038/s41593-026-02409-7},
  url = {https://doi.org/10.1038/s41593-026-02409-7},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex
AU  - John C. Bowler
AU  - Dua Azhar
AU  - Cambria M. Jensen
AU  - Hyun‐Woo Lee
AU  - James G. Heys
PY  - 2026
DA  - 2026/09/03
JO  - Nature Neuroscience
DO  - 10.1038/s41593-026-02409-7
UR  - https://doi.org/10.1038/s41593-026-02409-7
AB  - 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.
SN  - 1097-6256
SN  - 1546-1726
ER  -