Auto-adaptativité et topologie dans les cartes de Kohonen
|Advisor:||Kröger, Helmut; Parizeau, Marc|
|Abstract:||Using biological understanding we have modified the unsupervised Kohonen algo- rithm, with two aims : to improve the performance of modelisation and to make this theoretical model of neural self-organisation more realistic. At various stages during our research into the auto-adaptivity and topology of Kohonen maps, we implemented our findings into practical algorithms creating normalised, multirhythmic and self-instructed versions. Two new functions are introduced : local attractivity AintL , inspired from Growing Neural Gas networks (GNG), and knowledge Cint. Using these, modelisation error is reduced by up to 80% of the standard error. Guided by recent work that shows small-world topologies exist in a large number of networks, we have extended this classic approach to information theory. This has highlighted the temporal link between structure (topology) and function (learning and knowledge) in the neural system.|
|Document Type:||Mémoire de maîtrise|
|Open Access Date:||12 April 2018|
|Collection:||Thèses et mémoires|
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