EPSRC Network on Evolvability in Biology & Software Systems

Evolvability, Genetics & Development in Natural and Constructed Systems: Abstracts of the EPSRC Evolvability Network Symposium

Tewin Bury Farm Hotel, Hertfordshire, England, UK
26-28 August 2003

University of Hertfordshire Computer Science Technical Report 389
C. L. Nehaniv, P. J. Bentley & S. Kumar (Editors)

Modularity and Language in Evolutionary

Modularity and Language in Evolutionary
Neural Networks


School of Computing, Communication and Electronics
University of Plymouth
Centre for Neural and Adaptive Systems
University of Plymouth
Plymouth PL4 8AA, UK


Evolutionary neural networks, i.e. neural networks combined with genetic algorithms, are one of the main modelling tools in the research area of language evolution. They permit the simultaneous consideration of the three fundamental mechanisms of language evolution and change: ontogenetic learning, cultural transmission, and historical changes (Parisi & Cangelosi, 2002). In addition, evolutionary neural networks can be used to study the interaction between language and other sensorimotor, cognitive and neural factors. For example, a recent computer model of the origins of syntax has focused on the neural and sensorimotor basis of the linguistics categories of nouns and verbs (Cangelosi & Parisi, in press). Detailed analyses of the model have shown a functional specialization of the neural networks modules (layers) that resembles some essential properties of the modular language speaking brain (Pulvermuller 2002; Cappa & Perani 2003). Evolutionary neural networks have also been proposed to study the evolution of modularity (Calabretta & Parisi, in press). The use of genetic regulatory networks (GRNs) for the development of the neural network architectures facilitates the emergence of functional neural modules (Cangelosi, Nolfi & Parisi, 2003). GRNs also allow evolution to employ heterochronic mechanisms for the emergence of adaptive neural networks (Cangelosi, 1999). The combination of GRN with evolutionary neural networks for future research on the emergence of modularity and language will be discussed.

Calabretta, R. & Parisi, D. (in press). Evolutionary Connectionism and Mind/Brain Modularity. In W. Callabaut & D. Rasskin-Gutman (Eds.), Modularity. Understanding the Development and Evolution of Complex Natural Systems, Cambridge, MA: MIT Press.

Cangelosi, A. (1999). Heterochrony and adaptation in developing neural networks. In W. Banzhaf et al. (Eds), Proceedings of GECCO99 Genetic and Evolutionary Computation Conference. San Francisco, CA: Morgan Kaufmann, 1241-1248.

Cangelosi, A., Nolfi S., & Parisi D. (in press), Artificial life models of neural development. In S. Kumar & P. Bentley (eds.) On Growth, Form, and Computers, Academic Press, London UK.

Cangelosi, A. & Parisi D. (in press). The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging. Brain & Language.

Cappa, S.F. & Perani, D. (2003).The neural correlates of noun and verb processing. Journal of Neurolinguistics, 16 (2-3), 183-189.

Parisi, D. & Cangelosi, A. (2002). A unified simulation scenario for language development, evolution, and historical change. In A. Cangelosi & D. Parisi, Simulating the Evolution of Language, London: Springer, pp. 255-276.

Pulvermuller, F. (2002). The Neuroscience of Language. Cambridge: Cambridge University Press.