Neural Networks

Artificial Neural Networks are an attempt to mimic the human brain, they are usually used in an attempt to create a program that can do some of the thing that people do effortlessly. But most Neural Nets are fully connected networks of similar neurones, very regular in structure and fully explained by the programmer.

On the other hand the natural network connections found in any brain is much more random, multi-layered, containing different types of neurones and largely beyond our comprehension. I would like to investigate a method of creating a neural network that is closer to the structure of the human brain.

Stanley and Miikkulainen (2002) have presented a method of evolving both Network Architecture and Initial weights in a process they call Neuro Evolution of Augmenting Topologies (NEAT). Network Architecture (or Topology) is arrangement of neurones and their interconnections. This process has produced some interesting results.

NEAT starts with a small simple network, and over successive generation it develops a more complex and involved structure. The networks that develop are very unlike traditional artificial neural networks and more like those of nature.

The researchers have made various versions of NEAT code available under GPL, and given their success and the limitations of this project this seemed like a really good place to start development.

Stanley, K. O., and Miikkulainen, R. (2002). Evolving Neural Networks through Augmenting Topologies. In Evolutionary Computing. 10(2), pp 99-127.