Molecular self-organisation in a developmental model for the evolution of large-scale artificial neural networks

We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial Neural Networks using this principle is presented. We also present simulation results demonstrating the evolution of an edge detecting retina using the proposed methodology

Rotation, Translation, and Scaling Tolerant Recognition of Complex Shapes Using a Hierarchical Self-Organising Neural Network

A hierarchical neural network model for the identification of arbitrary contour shapes is presented. Tolerance towards translation, rotation and scaling is achieved far more costeffectively than for a fully connected multi-layer perceptron