@misc{10481/107283,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/107283},
abstract = {The striatum plays a central role in action selection and reinforcement learning, integrating cortical
inputs with dopaminergic signals encoding reward prediction errors. While dopamine modulates
synaptic plasticity underlying value learning, the mechanisms that enable selective reinforcement of
behaviorally relevant stimulus-action associations–the structural credit assignment problem–remain
poorly understood, especially in environments with multiple competing stimuli and actions. Here,
we present a computational model in which acetylcholine (ACh), released by striatal cholinergic
interneurons, acts as a channel-specific gating signal that restricts plasticity to brief temporal windows
following action execution. The model implements a biologically plausible three-factor learning rule
requiring presynaptic activity, postsynaptic depolarization, and phasic dopamine, with plasticity
gated by cholinergic pauses that temporally align with behaviorally relevant events. This mechanism
ensures that only synapses involved in the selected behavior are eligible for modification. Through
systematic evaluation across tasks with distractors and contingency reversals, we show that AChgated learning promotes synaptic specificity, suppresses cross-channel interference, and yields
increasingly competitive performance relative to Q-learning in complex tasks, reflecting the scalability
of the proposed learning mechanism. Moreover, the model reveals distinct roles for striatal pathways:
direct pathway (D1) neurons maintain stimulus-specific responses, while indirect pathway (D2)
neurons are progressively recruited to suppress outdated associations during policy adaptation. These
findings provide a mechanistic account of how coordinated cholinergic and dopaminergic signaling can
support scalable and efficient reinforcement learning in the striatum, consistent with experimental
observations of pathway-specific plasticity.},
organization = {MICIU/AEI/10.13039/501100011033 - FEDER, UE (PID2022-140095NB-I00, SENSCOMP)},
organization = {Vetenskapsrådet (VR-M-2020-01652)},
organization = {Horizon 2020 Framework Programme (945539, HBP SGA3)},
organization = {EU Horizon Europe Programme (101147319, EBRAINS 2.0 Project)},
organization = {European Union - Horizon Europe (101137289)},
organization = {Universidad de Granada / CBUA (Open access)},
publisher = {Nature Publishing Group},
keywords = {Dopamine},
keywords = {Acetylcholine},
keywords = {Spiking neural network},
title = {Cholinergic modulation enables scalable action selection learning in a computational model of the striatum},
doi = {10.1038/s41598-025-18776-3},
author = {González-Redondo, Álvaro and Garrido Alcázar, Jesús Alberto and Hellgren Kotaleski, Jeanette and Grillner, Sten and Ros, Eduardo},
}