Analysis of hippocampal ensembles during Contextual Feeding and cNOR tasks

This dataset contains two jupyter notebooks and python scripts to run some exemplar analysis from the article 'Organising the coactivity structure of the hippocampus from robust to flexible memory'.

The jupyter notebooks provided are:
- 'cNOR_task.ipynb' which computes the object/location coding analyses shown in Figures 1 and 4
- 'coactivity_cond+cnor.ipynb' reproduces some of the coactivity analyses shown in Figures 1 and 4.

In the 'results' folder are stored processed data that are loaded throughout the notebooks.

The python script 'makeGraphbatch.py' computes coactivity graphs during active exploration times (theta-informed) from the spiking data, available here: https://data.mrc.ox.ac.uk/data-set/hippocampal-ensemble-recordings After downloading, ensure the spiking data folder is named 'data' and placed inside the root folder 'orgCoactHippo'. See script for more info.

Inside the 'recordings' folder, there are text files that list the recording days belonging to each task. That is: 'cond_ll145' and 'cond_ll149' lists the food-context conditioning days for each animal, while `cnor_x` and `cnor_y` list the cnor days in the two contexts, regardless of the animal's identity.

The python library 'util_func.py' data loading and processing functions used by the python script and notebooks. See script for more info.
'difference_estimation_plot.py' is a python library to produce estimation plots as in the article.

For the analysis in the original paper, this code was run in Python version 3.10. Execution of the code requires the following libraries:

matplotlib 3.7.1
matplotlib-inline 0.1.6
networkx 2.8.4
numpy 1.24.3
pandas 1.5.3
pandas-ods-reader 0.1.4
scikit-learn 1.2.2
scipy 1.10.1
seaborn 0.12.2