Flexible inference of learning rules from de novo learning data using neural networks

• URL: http://arxiv.org/abs/2509.04661v1
• Date: Thu, 04 Sep 2025 21:20:11 GMT
• Title: Flexible inference of learning rules from de novo learning data using neural networks
• Authors: Yuhan Helena Liu, Victor Geadah, Jonathan Pillow,
• Abstract summary: We introduce a flexible framework for inferring learning rules from behavioral data in de novo learning tasks.<n>These results provide insights to inform experimental training protocols and the development of behavioral digital twins.
• Score: 4.396860522241306
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding how animals learn is a central challenge in neuroscience, with growing relevance to the development of animal- or human-aligned artificial intelligence. However, most existing approaches assume specific parametric forms for the learning rule (e.g., Q-learning, policy gradient) or are limited to simplified settings like bandit tasks, which do not involve learning a new input-output mapping from scratch. In contrast, animals must often learn new behaviors de novo, which poses a rich challenge for learning-rule inference. We target this problem by inferring learning rules directly from animal decision-making data during de novo task learning, a setting that requires models flexible enough to capture suboptimality, history dependence, and rich external stimulus integration without strong structural priors. We first propose a nonparametric framework that parameterizes the per-trial update of policy weights with a deep neural network (DNN), and validate it by recovering ground-truth rules in simulation. We then extend to a recurrent variant (RNN) that captures non-Markovian dynamics by allowing updates to depend on trial history. Applied to a large behavioral dataset of mice learning a sensory decision-making task over multiple weeks, our models improved predictions on held-out data. The inferred rules revealed asymmetric updates after correct versus error trials and history dependence, consistent with non-Markovian learning. Overall, these results introduce a flexible framework for inferring biological learning rules from behavioral data in de novo learning tasks, providing insights to inform experimental training protocols and the development of behavioral digital twins.

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