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2008

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

11 years 1 months ago
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.
Christoph Kolodziejski, Bernd Porr, Minija Tamosiu
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Christoph Kolodziejski, Bernd Porr, Minija Tamosiunaite, Florentin Wörgötter
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