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• W2125444740 abstract "In this theoretical contribution we provide mathematical proof that two of the most important classes of network learning - correlation-based differential Heb-bian learning and reward-based temporal difference learning - are asymptotically equivalent when timing the learning with a local modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation based perspective that is more closely related to the biophysics of neurons." @default.
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• W2125444740 date "2008-12-08" @default.
• W2125444740 modified "2023-09-24" @default.
• W2125444740 title "On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor" @default.
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• W2125444740 hasPublicationYear "2008" @default.
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