#
# 
#   Dejan Pecevski, dejan@igi.tugraz.at, 
#   November, 2008
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This model contains the scripts in Python and other necessary files to
reproduce the results reported in:

    Legenstein R, Pecevski D, Maass W 2008 A Learning Theory
    for Reward-Modulated Spike-Timing-Dependent Plasticity with 
    Application to Biofeedback. PLoS Computational Biology 4(10): e1000180, Oct, 2008 
    doi:10.1371/journal.pcbi.1000180
    
To perform the simulations and produce the figures you need to:

1. Install the Parallel Circuit SIMulator - PCSIM:
    See the instructions on http://www.igi.tugraz.at/pcsim on how to do that.
    Checkout the newest revision from the repository. 

2. Set the RMSTDP_HOME environment variable to the directory where
   this README file resides.

3. Install additional python packages for scientific computing:
    numpy 1.1.1 
    scipy 0.6.0
    matplotlib 0.98.3      
    pygsl 1.20
    mpi4py 0.6.0
    pytables 2.0.4
    ipython 0.9.1

    and all dependent packages from these.

4. You need to compile a pcsim extension module used in the
   simulations.
   To do this:

   - Goto the subdirectory "packages/reward_gen".

   - Edit the line 5 in module_recipe.cmake

     SET( PCSIM_SOURCE_DIR "$ENV{HOME}/pcsim" )

     so that PCSIM_SOURCE_DIR variable is set to the location of your
     installation of PCSIM.
     The default already set value is ${HOME}/pcsim.

   - Execute:

   	    python pcsim_extension.py build

5. Now you are ready to go. Each directory contains the files for one
   simulation

   from 1 to 5, as they are enumerated in the paper, and also
   additional simulations
   reported in the supplementary figures. 

   In each directory there is a README file explaining how to run the
   scripts in the directory and which figures are produced from the
   scripts.
