Information visualization for knowledge extraction in neural networks

Authors:
Liz Stuart;Davide Marocco;Angelo Cangelosi
Affiliations:
School of Computing Communication and Electronics, University of Plymouth, Plymouth, UK;Institute of Cognitive Science and Technologies, National Research Council, Rome, Italy;School of Computing Communication and Electronics, University of Plymouth, Plymouth, UK
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

In this paper, a user-centred innovative method of knowledge extraction in neural networks is described. This is based on information visualization techniques and tools for artificial and natural neural systems. Two case studies are presented. The first demonstrates the use of various information visualization methods for the identification of neuronal structure (e.g. groups of neurons that fire synchronously) in spiking neural networks. The second study applies similar techniques to the study of embodied cognitive robots in order to identify the complex organization of behaviour in the robot's neural controller.