This code repository is tailored for the dataset "Behaviour and dorsal striatum photometry recordings of mice during choice task with non-stationary probabilistic reward outcomes", but can be adapted for use on datasets from other trial-based choice tasks, especially those generated with pyControl and pyPhotometry. This repository is also hosted and maintained at https://github.com/Bogacz-Group/DLS_APE, the DOI of this dataset relates to the 1.0.0 release.

“qualitative_analysis.ipynb” contains code for loading the behavioural data and performing qualitative (model-free) analysis. “behaviour_model_fitting.ipynb” performs maximum-likelihood parameter optimisation with a stochastic algorithm and Bayesian model selection to compare the goodness of fit of different generative models on the same behavioural data. This notebook also generates latent variables from the best fitting model for use in analysis of photometry data.

“pre_processing.py”, “make_dataframe.py” and “simplify_data.py” are code for pre-processing and filtering the raw photometry data, aligning photometry signals with behavioural events on each trial, and formatting the processed data for efficient regression analysis. “mixed_effects.py” performs regression analysis with mixed-effects models on the processed photometry data to find correlation between neural activity and behavioural variables.

The “README.md” file within the repository provides further details on the functionality of the code, and more details on analysis methods will be published in a future research article.