Dataset for: Directional intermodular coupling enriches functional complexity in biological neuronal networks.

This dataset contains simulation data from spiking neural network (SNN) models. The weight matrices and spike trains are available in .pkl format.

A detailed description of the materials and methods will be published in the journal Neural Networks.DOI: https://doi.org/10.1016/j.neunet.2024.106967

File Loading

The .pkl files contain various data, including the weight matrices and spike trains, organized as structured objects in Python. The following script can be used to load these .pkl files:

import pickle

target_file = r"p_inter_0.1\p_inter_0.1_D_0.0.pkl"

with open(target_file, "rb") as f:    loaded_data = pickle.load(f)

for key,value in loaded_data.items():    print(f"{key} : {value}")

Data Structure

The table below provides detailed information about the data structure.

Raster
Raster data. First column: spike timing [sec], Second column: the index of fired neuron.

w
Connection matrix. Row and column represents input and output, respectively

Inf

 The index to which a specific neuron belongs.

D
Directionality of intermodular connection.

p_inter
Connection density between modules.

Note: Only representative samples are included; the dataset does not encompass all simulation data.