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.