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Found 4 papers, 3 papers with code
Credit Assignment in Neural Networks through Deep Feedback Control
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output.
Minimizing Control for Credit Assignment with Strong Feedback
Building upon deep feedback control (DFC), a recently proposed credit assignment method, we combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization.
Equilibrium Propagation for Complete Directed Neural Networks
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts.
Intrinsic Biologically Plausible Adversarial Training
Artificial Neural Networks (ANNs) trained with Backpropagation (BP) excel in different daily tasks but have a dangerous vulnerability: inputs with small targeted perturbations, also known as adversarial samples, can drastically disrupt their performance.
