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