[Re] An anatomically constrained neural network model of fear conditioning

The results contained within this archive correspond to the Python re-implementation of the computation model and replication of the classical conditioning experiment described in Armony et al. (1995). The data was generated by running the program using the 14th frequency as the Conditioned Stimulus (CS_IDX = 13) and setting the random seed for the Numpy library to 3 (NUMPY_SEED = 3).
During the pre- and post-conditioning testing phases, the activation values of all the neurons in the model have been recorded in different pandas.DataFrames. At the end of the experiment, those DataFrames have been written to disk using the HDF5 file format. It should be noted that although this might have been unnecessary given the size of the final dataset, the file has been further compressed to save on space.

The HDF5 file format works similarly to dictionaries in Python, or Maps in other programming languages. That is, the data is organized into tables/arrays each associated with a unique key. In the case of the current dataset, the keys are the name of the different layers in lowercase (i.e.: mgm, mgv, cortex, and amygdala). Then, the array corresponding to each of those key includes the layer's neural activities for all frequencies, and for both the pre- and post-conditioning phases.
The columns making up each table are:

The "Frequency" index with values in the range [1-15],

One column for storing the activity of each unit ("Unit 1", ..., "Unit N", where N = 3 or N = 8 depending on the layer),

The last column, entitled "Phase", contains string representations of the phase during which the activity was recorded (either "Pre-conditioning" or "Post-conditioning").

The data included in the archive can be retrieved and stored in a dictionary for further processing using the following Python script:

import pandas as pd

# DATA_PATH is the absolute path to the file containing the hdf5 formated data
data = {k: pd.read_hdf(DATA_PATH, key=k) for k in ['mgm', 'mgv', 'cortex', 'amygdala']}