Jupyter Notebook Python
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
database
probe
shl_scripts
.gitignore
LICENCE.txt
MANIFEST.in
README.rst
index.ipynb
setup.py

README.rst

Reproducible research : Python implementation of SparseHebbianLearning

Set of RFs after aSSC learning.

Object

  • 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

Installation

  • Be sure to have dependencies installed:

    pip3 install -U NeuroTools
    pip3 install -U SLIP
    
  • 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
    

Licence

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.

Contents

  • 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.

Changelog

  • finalizing the code to reproduce the sparsenet algorithm
  • 2.0 - 2015-05-07:
  • 1.1 - 2014-06-18:
  • documentation
  • dropped Matlab support
  • 1.0 - 2011-10-27 : initial release