Information visualization for knowledge extraction in neural networks

L. Stuart, D. Marocco, A. Cangelosi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationInternational Conference on Artificial Neural Networks
Subtitle of host publicationFormal Models and Their Applications – ICANN 2005
PublisherSpringer Nature
Pages515-520
Number of pages6
ISBN (Electronic)9783540287568
ISBN (Print)9783540287551
Publication statusPublished - 2005

Publication series

NameLecture Notes in Computer Science
Volume3697

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