Artificial Development of Biologically Plausible Neural-Symbolic Networks

作者：Townsend Joe^*; Keedwell Ed; Galton Antony

来源：Cognitive Computation, 2014, 6(1): 18-34.

DOI：10.1007/s12559-013-9217-0

• Artificial development
• Neural-symbolic integration
• Indirect encoding
• Neural networks
• Evolutionary computing

Neural-symbolic networks are neural networks designed for the purpose of representing logic programs. One of the motivations behind this is to work towards a biologically plausible model of knowledge representation in the brain. This paper reviews work in this direction and suggests that a new direction to take would be to evolve neural-symbolic networks using artificial development, which also has some biological plausibility. This idea is supported by a review of artificial development, followed by some initial results in using artificial development to evolve a neural-symbolic SHRUTI network in order to demonstrate how the fields of neural-symbolic integration and artificial development may be integrated. The experiments were successful in evolving genomes which could develop connections between neurons in working SHRUTI networks.

• 出版日期2014-3