Neurocomputing for spatio-/spectro temporal pattern recognition and early event prediction: methods, systems, applications
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The talk presents a brief overview of contemporary methods for neurocomputation, including: evolving connections systems (ECOS) and evolving neuro-fuzzy systems ; evolving spiking neural networks (eSNN) [2-5]; evolutionary and neurogenetic systems ; quantum inspired evolutionary computation [7,8]; rule extraction from eSNN . These methods are suitable for incremental adaptive, on-line learning from spatio-temporal data and for data mining. But the main focus of the talk is how they can learn to predict early the outcome of an input spatio-temporal pattern, before the whole pattern is entered in a system. This is demonstrated on several applications in bioinformatics, such as stroke occurrence prediction, and brain data modeling for brain-computer interfaces , on ecological and environmental modeling . eSNN have proved superior for spatio-and spectro-temporal data analysis, modeling, pattern recognition and early event prediction as outcome of recognized patterns when partially presented.