Heterochrony and Adaptation in Developing Neural Networks

This paper discusses the simulation results of a model of biological development for neural networks based on a regulatory genome. The model’s results are analyzed using the framework of Heterochrony theory (McKinney and McNamara, 1991). The network development is controlled by genes that produce elements regulating the activation, inhibition, and delay of neurogenetic events. The genome can also regulate the gene expression mechanisms. An ecological task of foraging behavior is used to test the model with an evolving population of artificial organisms. Organisms evolve an optimal foraging behavior and the ability to adapt to changing environments. The adaptive strategy consists in changes of network architecture that are determined by the regulatory rearrangment of neurogenetic events. Results show how heterochronic changes play an adaptive role in the evolution of neural networks. 1 HETEROCHRONY AND DEVELOPMENT IN NEURAL NETWORKS In living organisms the existence of a variable and...

• Type: Conference
• Year: 1999
• Where: GECCO
• Authors: Angelo Cangelosi
• Keywords: GECCO 1999, Neural Networks, Optimization, Regulatory, Regulatory Genome