title: On the asymptotic equivalence between differential Hebbian and temporal difference learning.
abstract: In this theoretical contribution, we provide mathematical proof that two of the most important classes of network learning-correlation-based differential Hebbian learning and reward-based temporal difference learning-are asymptotically equivalent when timing the learning with a modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation-based perspective more closely related to the biophysics of neurons.
authors.affiliation: Bernstein Center for Computational Neuroscience, University of Göttingen, 37073 Göttingen, Germany. kolo@bccn-goettingen.de
authors.forename: Christoph
authors.initials: C
authors.lastname: Kolodziejski
authors.forename: Bernd
authors.initials: B
authors.lastname: Porr
authors.forename: Florentin
authors.initials: F
authors.lastname: Wörgötter
mesh_terms: D000818:Animals; D001703:Biophysics; D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D012054:Reinforcement, Psychology; D013997:Time Factors