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Activity of large neural populations can be represented by few independent patterns called “synergies”. Synergies constrain neural dynamics in specific manifolds. Standard dimensionality-reduction techniques have been used to find sets of synergies. Neural spaces may contain distinct manifolds, each associated with specific tasks: is each manifold spanned by its own unique synergies, or do tasks in a class share some synergies that represent common features? We use sparse dictionary-learning approaches to tackle these open issues.
This presentation is part of Minisymposium “MS53 - Dimensionality Reduction Algorithms for Large-Scale Images (2 parts)”
organized by: Salvatore Cuomo (Dept. Mathematics and Applications "Renato Caccioppoli", University of Naples) , Costantinos Siettos (National Technical University of Athens ) , Lucia Russo (Consiglio Nazionale delle Ricerche, Istituto di Ricerche sulla Combustione) .