• This is a collection of python scripts to test learning strategies to efficiently code natural image patches. This is here restricted to the framework of the SparseNet algorithm from Bruno Olshausen (http://redwood.berkeley.edu/bruno/sparsenet/).

• this has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ):

  @article{Perrinet10shl,
       Author = {Perrinet, Laurent U.},
       Title = {Role of homeostasis in learning sparse representations},
       Year = {2010}
       Url = {http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl},
       Doi = {10.1162/neco.2010.05-08-795},
       Journal = {Neural Computation},
       Volume = {22},
       Number = {7},
       Keywords = {Neural population coding, Unsupervised learning, Statistics of natural images, Simple cell receptive fields, Sparse Hebbian Learning, Adaptive Matching Pursuit, Cooperative Homeostasis, Competition-Optimized Matching Pursuit},
       Month = {July},
       }

• all comments and bug corrections should be submitted to Laurent Perrinet at Laurent.Perrinet@univ-amu.fr
• find out updates on http://invibe.net/LaurentPerrinet/SparseHebbianLearning

• Be sure to have dependencies installed:

  pip3 install -U git+https://github.com/NeuralEnsemble/NeuroTools.git
  pip3 install -U git+https://github.com/bicv/SLIP.git

• Then, either install the code directly:

  pip3 install git+https://github.com/bicv/SHL_scripts.git

• or if you wish to tinker with the code, download the code @ https://github.com/bicv/shl_scripts/archive/master.zip. You may also grab it directly using the command-line:

  wget https://github.com/bicv/shl_scripts/archive/master.zip
  unzip master.zip -d shl_scripts
  cd shl_scripts/
  ipython setup.py clean build install
  jupyter notebook

• developpers may use all the power of git with:

  git clone https://github.com/bicv/SHL_scripts.git

This piece of code is distributed under the terms of the GNU General Public License (GPL), check http://www.gnu.org/copyleft/gpl.html if you have not red the term of the license yet.

• README.rst : this file
• index.ipynb : an introduction as a notebook
• src/shl_*.py : the class files
• probe*.ipynb : the individual experiments as notebooks
• database : the image files.

• 3.0 - 2017-06-06: refactored the code for http://invibe.net/LaurentPerrinet/Publications/BoutinRuffierPerrinet17spars

* 2.1 - 2015-10-20:

• finalizing the code to reproduce the sparsenet algorithm

* 2.0 - 2015-05-07:

• transform to a class to just do the Sparse Hebbian Learning (high-level) experiments (getting data from an image folder, learning, coding, analyszing)
• use sklearn to do all the hard low-level work, in particular sklearn.decomposition.SparseCoder see http://scikit-learn.org/stable/auto_examples/decomposition/plot_image_denoising.html and http://www.cs.utexas.edu/~leif/pubs/20130626-scipy-johnson.pdf
• The dictionary learning is tested in http://blog.invibe.net/posts/2015-05-05-reproducing-olshausens-classical-sparsenet.html and the corresponding PR is tested in http://blog.invibe.net/posts/2015-05-06-reproducing-olshausens-classical-sparsenet-part-2.html

* 1.1 - 2014-06-18:

• documentation
• dropped Matlab support

• 1.0 - 2011-10-27 : initial release