freely_moving_photometry_data: raw photometry data

Dopaminergic action prediction errors serve as a value-free teaching signalAnimals’ choice behavior is characterized by two main tendencies: taking actions that led to rewards and repeating past actions. Theory suggests these strategies may be reinforced by different types of dopaminergic teaching signals: reward prediction error to reinforce value-based associations and movement-based action prediction errors to reinforce value-free repetitive associations. Here we use an auditory-discrimination task in mice to show that movement-related dopamine activity in the tail of the striatum encodes the hypothesized action prediction error signal. Causal manipulations reveal that this prediction error serves as a value-free teaching signal that supports learning by reinforcing repeated associations. Computational modelling and experiments demonstrate that action prediction errors alone cannot support reward-guided learning but when paired with the reward prediction error circuitry they serve to consolidate stable sound-action associations in a value-free manner. Together we show that there are two types of dopaminergic prediction errors that work in tandem to support learning, each reinforcing different types of association in different striatal areas.This is the dataset with the raw fiber photometry - not demodulated or smoothed. If you want the demodualted data this is in the processed_data upload 'processed_data including aligned traces, demodulated photometry and restructured behavioral events'. Look for files with structure mousename_date_smoothed_signal.npy in the processed data folder.To demodulate the raw data and align with behavioral events (raw bpod data) see code (https://github.com/SainsburyWellcomeCentre/SJLab_APE_paper/tree/main/APE_paper_photometry_code_francesca) SJLab_APE_paper/APE_paper_photometry_code_francesca/data_preprocessing/pre_processing.py. This script requires the raw bpod files (raw behavioral data) found in upload in this figshare project called 'bpod_data raw behavioral files for all sessions, not just for photometry'.

多巴胺能动作预测误差（dopaminergic action prediction errors）作为无价值教学信号发挥作用。动物的选择行为具有两大核心特征：选择曾带来奖赏的行动，以及重复过往实施过的行为。现有理论提出，这两类行为策略或许由不同类型的多巴胺能教学信号强化：奖赏预测误差（reward prediction error）用于强化基于价值的关联，而基于运动的动作预测误差（movement-based action prediction errors）则用于强化无价值的重复关联。本研究利用小鼠听觉分辨任务，证实纹状体尾部与运动相关的多巴胺活动编码了前述假说中的动作预测误差信号。因果操纵实验表明，该预测误差作为无价值教学信号，通过强化重复关联来支持学习过程。计算建模与实验结果共同证实，仅依靠动作预测误差无法支撑奖赏导向的学习，但当其与奖赏预测误差环路协同作用时，便能以无价值的方式巩固稳定的声音-行动关联。综上，本研究揭示存在两类协同工作的多巴胺能预测误差，它们分别在不同纹状体区域强化不同类型的行为关联。

本数据集包含未解调、未平滑处理的原始纤维光度学（fiber photometry）数据。若需获取解调后的数据，可查看上传的processed_data（已处理数据）文件夹，其中包含对齐后的轨迹、解调后的光度学数据及重构的行为事件。该文件夹内的文件命名格式为`mousename_date_smoothed_signal.npy`。

如需对原始数据进行解调并与行为事件（原始bpod数据）对齐，请参考代码仓库（https://github.com/SainsburyWellcomeCentre/SJLab_APE_paper/tree/main/APE_paper_photometry_code_francesca）中的`SJLab_APE_paper/APE_paper_photometry_code_francesca/data_preprocessing/pre_processing.py`脚本。该脚本依赖于本figshare项目上传的原始bpod文件——即所有实验会话的原始行为数据（而非仅光度学相关数据），其对应的上传文件夹名为"bpod_data raw behavioral files for all sessions, not just for photometry"。