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The author describes implementing a biologically plausible neural network training algorithm proposed by Geoffrey Hinton.
This paper investigates how correlated noise, inspired by neural variability in the brain, can enhance the robustness of artificial neural networks against adversarial attacks and naturalistic image modifications.
Introduces Score Broadcast and Decorrelation (SBD), a principled framework for broadcast-based credit assignment that generalizes to differentiable loss families including cross-entropy, Bregman divergences, and proper scoring rules. The work provides theoretical grounding for the three-factor learning rule and demonstrates improved performance over existing broadcast approaches on CIFAR-10 and Tiny ImageNet.