To study the evolution of complex nervous systems through artificial development, an encoding scheme for modeling networks is needed that reflects intrinsic properties similiar to natural encodings. Like the ge- netic code, a description language for simulations should indirectly encode networks, be stable but adaptable through evolution and should encode functions of neural networks through architectural design as well as sin- gle neuron configurations. We propose an indirect encoding scheme based on Compositional Pattern Produc- ing Networks (CPPNs) to fulfill these needs. The encoding scheme uses CPPNs to generate multidimensional patterns that represent the analog to protein distributions in the development of organisms. These patterns form the template for three-dimensional neural networks, in which dendrite- and axon cones are placed in space to determine the actual connections in a spiking neural network simulation.
['Martin Pyka', 'Tilo Kircher', 'Sascha Hauke', 'Dominik Heider']
The Brain in a Box An Encoding Scheme for Natural Neural Networks