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Updated: Jun 27, 2026

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
Published on: January 27, 2018
On the asymptotic equivalence between differential Hebbian and temporal difference learning
Christoph Kolodziejski1, Bernd Porr, Florentin Wörgötter
1Bernstein Center for Computational Neuroscience, University of Göttingen, 37073 Göttingen, Germany. kolo@bccn-goettingen.de
Abstract:
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.
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