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Dopamine encodes deep network teaching signals for individual learning trajectories.

Samuel Liebana¹, Aeron Laffere¹, Chiara Toschi¹

¹Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford OX1 3PT, UK.

|June 12, 2025

Summary

Individual learning paths vary, but striatal dopamine signals systematically guide strategy transitions in mice. This research uncovers biological and mathematical principles behind diverse, long-term learning trajectories.

Keywords:

basal ganglia dopamine gradient descent individual variability long-term learning neural network reinforcement learning reward prediction error saddle point striatum

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Area of Science:

Neuroscience
Computational Neuroscience
Behavioral Neuroscience

Background:

Striatal dopamine is crucial for decision-making and learning.
Individual learning trajectories show significant diversity, posing a challenge to understanding dopaminergic mechanisms.

Purpose of the Study:

To longitudinally investigate the role of dorsal striatal dopamine in mice learning a decision task.
To elucidate the mechanisms underlying diverse and systematic individual learning trajectories.

Main Methods:

Longitudinal measurement and optogenetic manipulation of dorsal striatal dopamine in mice.
Analysis of strategy transitions and stimulus-choice associations.
Development and analysis of a deep neural network model with heterogeneous teaching signals.

Main Results:

Mouse learning trajectories exhibited diverse strategy sequences but systematic transitions.
Dopamine signals encoded stimulus-choice associations, reflecting strategy transitions.
Optogenetic manipulation of these associations produced distinct learning effects.

Conclusions:

Individual learning diversity and systematicity can be explained by heterogeneous teaching signals influencing specific association weights.
This study reveals biological and mathematical principles governing long-term individual learning trajectories.