  We present a model of decentralized growth for Artificial Neural Networks
(ANNs) inspired by the development and the physiology of real nervous systems.
In this model, each individual artificial neuron is an autonomous unit whose
behavior is determined only by the genetic information it harbors and local
concentrations of substrates modeled by a simple artificial chemistry. Gene
expression is manifested as axon and dendrite growth, cell division and
differentiation, substrate production and cell stimulation. We demonstrate the
model's power with a hand-written genome that leads to the growth of a simple
network which performs classical conditioning. To evolve more complex
structures, we implemented a platform-independent, asynchronous, distributed
Genetic Algorithm (GA) that allows users to participate in evolutionary
experiments via the World Wide Web.
