Data and analysis code

Data and analysis code for the paper 'Neuronal tuning to threat exposure remains stable in the mouse prefrontal cortex over multiple days' (PLOS Biology).

This repository contains:

The full dataset: - dataset.py

 Main analysis code: fig1-fig4.py and supplemental1-supplemental7.py

Function code: glm_to_predict_speed_from_neuronal_activity.py, glm_tst_50_neurons_no_iterations.py, Graphs.py, manifold.py, prediction.py, rate_coding.py, statistics_test.py, time_functions.py, time_locked_functions.py, transient.py

Source data for figures: - source_data.xls

The function code is normally stored in a folder called function.

Structure of the dataset:

The dataset is a python dictionary with 3 levels. 

Level 1: Animal ID

'animal294', dict

'animal295', dict

'animal329', dict

'animal337', dict

'animal339', dict

'animal341', dict

Level 2: Recording day

'day1', dict

'day3', dict

'day9', dict

Level 3: Variables

'A', spatial coordinate of extracted neurons, csc_matrix

'assignments':  assignments id of multisession registration, Numpy array

'assignments_filts': assignments id of multisession registration containing only detected neurons, NumPy array

'background_time': boolean array of timepoints when the behavior could not be clearly assigned to either struggle or immobility behavior, NumPy array.

'baseline_speed_bodyparts': dict with multiple NumPy arrays of the animal's speed of various bodyparts during baseline, dict

'baseline_speed_dlc': speed of the animal during baseline averaged for all bodyparts, NumPy array

'fn_baseline': calcium activity during baseline, NumPy array

'fn_tst': calcium activity during tail suspesion, NumPy array

'hemis': The hemisphere of the recording was from, str

'immobility_time': boolean array of timepoints the animal is immobile, NumPy array

'sex': the sex of the animal, str 

'struggle_time': boolean array of timepoints the animal is struggling, Numpy array

'tst_speed_bodyparts': dict with multiple NumPy arrays of the animal's speed of various bodyparts during tail suspension, dict

'tst_speed_dlc': speed of the animal during tail suspension averaged for all bodyparts, NumPy array