Izquierdo-Torres, Eduardo and Harvey, Inman (2007) Hebbian Learning using Fixed Weight Evolved Dynamical 'Neural' Networks. In: Proceedings of the First IEEE Symposium on Artificial Life. (IEEE-ALife'07), Hawaii.

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Abstract

We evolve small continuous-time recurrent neural networks with fixed weights that perform Hebbian learning behavior. We describe the performance of the best and smallest successful system, providing an in-depth analysis of its evolved mechanisms. Learning is shown to arise from the interaction between the multiple timescale dynamics. In particular, we show how the fast-time dynamics alter the slow-time dynamics, which in turn shapes the local behavior around the equilibrium points of the fast components by acting as a parameter to them.

Item Type:	Conference or Workshop Item (Paper)
Schools and Departments:	School of Engineering and Informatics > Informatics
Depositing User:	Eduardo Jose Izquierdo-Torres
Date Deposited:	06 Feb 2012 18:12
Last Modified:	04 Apr 2012 12:23
URI:	http://sro.sussex.ac.uk/id/eprint/15235

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