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Christoph Kolodziejski
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Publisher: Journals Gateway
Neural Computation (2009) 21 (4): 1173–1202.
Published: 01 April 2009
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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.