SORN

This code contains the necessary files to simulate the SORN model and perform the avalanche analysis as described in Del Papa et al. (2017). It is based on the previous implementation by Hartmann et al. (2015), which can be found here.

This model implementation runs in python 2.7 and was my first repository, so it is not very organized, efficient, or easy to clone and run. It also lacks requirements files and installation guides, so version conflicts for some packages are to be expected. Instead of updating and maintaining this old repository, I have decide to create a new (and hopefully better) one. The new implementation in python 3, SORN_V2 is currently under development can be found here.

Experiments

To run simple SORN experiments, proceed in the same way as the original SORN code: navigate to the common folder and run python test_single.py <PATH_TO_PARAM_FILE>. The simulations store all relevant data in a backup folder, which you may change according to your needs.

The avalanche scripts should be run independently of the SORN experiments. They read the simulations results, perform the relevant analysis and plot the avalanche events distributions. Remember to update the simulations folder accordingly (usually named exper_path in the scripts).

Files and data for Del Papa et al. 2017

The experiments and parameter files described in the paper can be found in the delpapa folder. The subfolder avalanches contains the exact code to reproduce each of the figures, as instructed below. Note that a few parameters must be changed for each of the experiments (for example, in order to reproduce all the simulations for different network sizes from Fig. 2, you should run simulations for each size independently by changing c.N_e in the param_Zheng2013.py file).

• Fig. 1: Parameters and Experiments: param_Zheng2013.py, experiment_Zheng2013.py. Figure: avalanches/Fig1.py.

• Fig. 2: Parameters and Experiments: param_Zheng2013.py, experiment_Zheng2013.py. Figure: avalanches/Fig2.py.

• Fig. 3: Parameters and Experiments: param_Zheng2013.py, experiment_Zheng2013.py. Figure: avalanches/Fig3.py.

• Fig. 4: Parameters and Experiments: param_FrozenPlasticity.py, experiment_FrozenPlasticity.py. Figure: avalanches/Fig4.py.

• Fig. 5: Parameters and Experiments: param_Zheng2013.py, experiment_Zheng2013.py. Figure: avalanches/Fig5.py.

• Fig. 6: Parameters and Experiments: param_ExtraInput.py, experiment_ExtraInput.py. Figure: avalanches/Fig6.py.

• Fig. 7: Parameters and Experiments: param_CountingTask.py, experiment_CountingTask.py and param_RandomTask.py, experiment_RandomTask.py. Figure: avalanches/Fig7.py.

Dependencies

This code relies on the powerlaw python package to fit the power-law distributions of avalanche sizes and durations.