The scripts in this directory show how to evolve networks that implement the 2-input XOR function.  These XOR examples
are intended to be "Hello World" style samples, so that you can see the minimal amount of code you need to write in order
to make use of *neat-python*. 

These simple examples are sometimes also useful as a debugging tool for *neat-python*, because you can step through the 
NEAT-specific code and watch what happens without getting swamped by the complexity of the networks and/or application code.

Note that there is a significant amount of duplication between these scripts, and this is intentional.  The goal is to 
make it easier to see what the example is doing, without making the user dig through a bunch of code that is not 
directly related to the NEAT library usage.

## The examples ##

* `evolve-minimal.py` shows how to evolve a feed-forward neural network with sigmoidal neurons; this example has all 
unnecessary complications removed. 

* `evolve-feedforward.py` shows how to evolve a feed-forward neural network with sigmoidal neurons. This is
essentially the evolve-minimal example with prettier output and better coding practices.  

* `evolve-feedforward-parallel.py` evolves the same type of network as `evolve-feedforward.py`, but this example shows
    how you can make use of multiple processors to evaluate networks in parallel.  

* `evolve-spiking.py` evolves a network of spiking neurons, using Izhikevich's neuron model from ["Simple model of spiking 
neurons"](http://www.dis.uniroma1.it/~gori/Sito_GG/Modellistica_files/2003%20Net.pdf) in 2003.