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

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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Christoph Kolodziejski, Bernd Porr, Minija Tamosiunaite, Florentin Wörgötter
• Keywords: Information Technology, Local Modulatory Signal, NIPS 2008, Reinforcement Learning Framework, Temporal Difference Learning