Self-Organizing Maps
Detailed learning in narrow fields: towards a neural network model of autism
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Self-organizing topological tree for online vector quantization and data clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Self-organizing nets for optimization
IEEE Transactions on Neural Networks
A multimodal self-organizing network for sensory integration of letters and phonemes
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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We introduce a novel system of interconnected Self- Organizing Maps that can be used to build feedforward and recurrent networks of maps. Prime application of interconnected maps is in modelling systems that operate with multimodal data as for example in visual and auditory cortices and multimodal association areas in cortex. A detailed example of animal categorization in which the feedworward network of self-organizing maps is employed is presented. In the example we operate with 18-dimensional data projected up on the 19-dimensional hyper-sphere so that the “dot-product” learning law can be used. One potential benefit of the multimodal map is that it allows a rich structure of parallel unimodal processing with many maps involved, followed by convergence into multimodal maps. More complex stimuli can therefore be processed without a growing map size.