Faithful representations with topographic maps
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Fast self-organizing feature map algorithm
IEEE Transactions on Neural Networks
A SOM-Based validation approach to a neural circuit theory of autism
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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Cognitive modeling methodologies form the groundwork of significant studies in cognitive science. In this work a prototype computational model, called IPSOM, is introduced, which charts the spatial cognitive human behaviour in completing interlocking puzzles. IPSOM is a neural network of the class of self-organizing maps, and has been implemented using an artificial data set that consists of synthesized patterns of puzzle completion. The results show that the model is particularly successful in depicting valid cognitive behavioural patterns with a very high degree of confidence. Based on IPSOM's performance and structure, it is argued that a scaled-up version of this model could readily be used in representing real-life puzzle-completion patterns.