On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

ABSTRACT: A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computation

SUBMITTER: Tonelli P 

PROVIDER: S-EPMC3827315 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature