som_stability

This repository contains all the scripts for reproducing the experiments from "Stability analysis of a neural field self-organizing map" ([1]).

Contents

• som.py Main simulation script. This script implements the neural field model and the self-organization algorithm.
• distortion.py Implements distortion (using Numba) and MSE functions for measuring the performance of the SOM.
• som_dxdy.py Implements the Dx-Dy representation function. It qualifies the topographic map after learning.
• condition.py Computes the condition (14) from [1] assuring stability of the SOM model.
• plot_functions.py Implemts all the necessary functions for plotting the figures in [1].
• figureX.py All scripts named figure plot the X figure from [1]. The user need to run first the som.py and distortion scripts to get the necessary data files. All the data should be stored in a folder named data/.

Requirements

• Python 3
• Numpy
• Numba
• Sklearn
• Matlotlib

Run experiments

In order to run the experiments you can use the script som.py as follows:

$ python som.py 135 0.47 0.2 04702

In this case you run the SOM experiment with (K_e, K_i) = (0.47, 0.2) and the PRNG seed set to 135.

Running platform information

The current source code has been tested on the following system configuration:

• CPU Intel i7 10th Generation with 32 GB physical memory
• Linux 5.3.0-29-generic #31-Ubuntu SMP x86_64 x86_64 x86_64 GNU/Linux
• GCC (Ubuntu 9.2.1-9ubuntu2) 9.2.1 20191008
• Python 3.7.5
  • Numpy 1.18.1
  • Numba 0.48.0
  • Sklearn 0.22.1
  • Matlotlib 3.1.3

License

The current repository is freely available under the GPL 3.0.

References

[1] G.Is. Detorakis, A. Chaillet, and N.P. Rougier, "Stability analysis of a neural field self-organizing map", 2020.