6-MAR-2025
This repo accompanies our report:
Jake Carr, Michael Condon, Bora Gunel, Samuel Hardy
Much of the code in this repository originates from the Github from the original paper. The files listed below are our own work. Anything excluded is from the original paper below. Rosenberg, M., Zhang, T., Perona, P., and Meister, M. (2021). Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration. BioRxiv 2021.01.14.426746. (https://doi.org/10.1101/2021.01.14.426746)
This repo contains all the data and code needed to reproduce the analysis in the submitted preprint and the revised version.
change_point_bct_analysis.ipynb: Analysis using Bayesian Context Trees and change point detection.
tree_animation.py: Code for visualizing trajectory over a binary tree.
tree.py: Code for constructing the BCT model in python, parameterising and inferencing it.
bayes.py Code for calculating the posterior probability of changepoint location
Thigmotaxis_analysis.ipynb: Analysis of wall-following behavior in binary tree maze
plot_model_findings.py
Q_learning_1.py
Q_learning_2.py