<ul><li>Equilibrium Propagation (EP) is a supervised learning algorithm that trains network parameters using local neuronal activity.</li><li>EP avoids data movement, making it suitable for energy-efficient training on neuromorphic systems.</li><li>EP can learn on hardware with physical uncertainties, providing implications for self-learning systems.</li><li>Research shows successful training of deep neural networks using EP in the presence of uncertainties.</li></ul>

Harnessing uncertainty when learning through Equilibrium Propagation in neural networks

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