Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning

This repository contains the code used for the paper "Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning" (arXiv).

Content:

• SO_base.py - Main file. Uses the custom containers in containers.py, the parameters in paramfiles/modularprob.py, and calls for SO_base_FUNC.py to run the simulations. Calls for SO_base_plots.py to generate figures 2-9 in the paper.
• SO_base_plots.py - Calls for SO_for_SAT.py and borders.py, and contains various plot functions to generate figures 2-9.
• SO_base_FUNC.py - Calls for hebbclean.F90 to run the simulation in FORTRAN. Contains functions from SO-scaled-up to generate random weight matrices.
• hebbclean.F90 - Contains the FORTRAN routine.
• containers.py - Contains custom containers.
• paramfiles/modularprob.py - Folder that contains all the parameters.

To replicate the figures in the paper, modify the following parameters in the paramfiles/modularprob.py file:

• For Figures 2-4, and 8-9:
  • Set CO.alphaGrid = True and CO.num_alphas = 1 (for Fig. 3 also set plot6 = True in SO_base.py).
• For Figures 5-6:
  • Set CO.alphaGrid = False and CO.num_alphas = 64.
• For Figure 7:
  • Set CO.alphaGrid = False, CO.num_alphas = 1 and CO.resets_arr = [5000] * len(CO.alpha_arr) in line 33 of the code.

To run the code from Python with FORTRAN:

Make sure your system has gfortran and f2py. Run the following commands before the execution of the python code to compile the FORTRAN file:

f2py3 --f90flags="-g -fdefault-integer-8 -O3" -m hebbF -c hebbclean.F90

To run the simulations from Python without FORTRAN

Current code is written to run the explicitly with FORTRAN (plots are generated in Python). To run the simulation in Python only see SO-scaled-up.

If you have any questions, feel free to open an issue or send me an email: natalya.weber (at) oist.jp.