On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Christoph Kolodziejski, Bernd Porr, Minija Tamosiunaite, Florentin Wörgötter


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 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.