A Thalamus–Brainstem Attractor Network Drives History-Biased Decisions

Figure Reproduction Code This repository contains the experimental data and MATLAB code used to reproduce the main results and figure panels in the manuscript “A Thalamus–Brainstem Attractor Network Drives History-Biased Decisions”. Repository structure One folder per figure (main figures and extended data figures). Data and code are co-located: all files needed to generate a given figure are contained within that figure’s folder. Folder naming matches the figure identifier (e.g., Fig1, Fig2, EDFig1, etc.; exact names follow the manuscript figure labels). Entry script in each figure folder: run the script ending with `_Main.m` (e.g., `Fig1_Main.m`, `Ext1_Main.m`). Within each _Main.m file: Each MATLAB section corresponds to one panel (Panel a, b, c, …). Helper functions are defined at the end of the script. Model code (separate repository) The spiking neural network model code is hosted separately: https://github.com/Mulab2020/HAN Recommended workflow (per panel per section) Open the corresponding'_Main.m'file and run the code section-by-section using MATLAB Run Section. Example Navigate to the corresponding figure folder (set MATLAB Current Folder to the figure directory). Note: Set MATLAB’s Current Folder to the corresponding figure directory. Alternatively, in each section, there is a function that automatically switches the working directory to the correct folder, 'cd_to_active_file'. Open the corresponding _Main.m file (e.g., Fig1_Main.m). Use Run Section to execute each panel block (e.g., Panel a, b, c, …). This section-based workflow is designed to make the figure-to-code mapping explicit.