Higher order recurrent networks and grammatical inference
Advances in neural information processing systems 2
Neural Networks
Fundamentals of speech recognition
Fundamentals of speech recognition
Neural Networks
Neural Networks - Special issue: models of neurodynamics and behavior
Self-organizing maps
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Temporal self-organization for neural networks
Temporal self-organization for neural networks
Dynamic subgrouping in RTRL provides a faster O(N/sup 2/) algorithm
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
Self-organizing maps with refractory period
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Residual activity in the neurons allows SOMs to learn temporal order
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Self-Organizing neural networks for signal recognition
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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In this paper, we develop a spatio-temporal memory that blends properties from long and short-term memory and is motivated by reaction diffusion mechanisms. The winning processing element of a self-organizing network creates traveling waves on the output space that gradually attenuate over time and space to diffuse temporal information and create localized spatio-temporal neighborhoods for clustering. The novelty of the model is in the creation of time varying Voronoi tessellations anticipating the learned input signal dynamics even when the cluster centers are fixed. We test the method in a robot navigation task and in vector quantization of speech. This method performs better than conventional static vector quantizers based on the same data set and similar training conditions.