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Heterochrony and adaptation in developing neural networks

Cangelosi, Angelo (1999) Heterochrony and adaptation in developing neural networks. [Conference Paper]

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Abstract

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.

Item Type:Conference Paper
Keywords:Hetereochrony, genetic algorithm, genotype-phenotype mapping, neural network, neural development
Subjects:Biology > Evolution
Computer Science > Artificial Intelligence
Computer Science > Neural Nets
Psychology > Evolutionary Psychology
Neuroscience > Neural Modelling
ID Code:2021
Deposited By:Cangelosi, Professor Angelo
Deposited On:16 Jan 2002
Last Modified:11 Mar 2011 08:54

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