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It is still unclear how information is actually stored in biological neural networks. We propose here that information could be first orthogonalized and then stored. This could happen in a manner similar to how a set of vectors is transformed into a set of orthogonalized (i.e. mutually perpendicular) vectors. Orthogonalization may overcome the limits of conventional artificial networks, particularly...
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