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Classification and Recognition from High Dimensional Network Dynamics


We develop methods at the interface of dynamical system theory and data analysis to classify the dynamics that networks produce. In neuronal networks these are collections of experimentally observed time-series recorded from multiple neurons as they respond to stimuli. We also develop optimal strategies for network sampling to obtain efficient classification. Examples of classifications from our research include olfactory decision space in insects from supervised recordings, and functional connectome for the C. elegans worm.