Highway error propagation augments predictive coding with feedback matrices V to deliver depth-independent error corrections, allowing effective training of 128-layer MLPs while preserving local synaptic updates.
Brain-inspired machine intelligence: A survey of neurobiologically-plausible credit assignment
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
HCL-FF augments the Forward-Forward algorithm with hierarchical learning and contrastive objectives to reach new state-of-the-art accuracies among FF methods on CIFAR-10 (+5.46%), CIFAR-100 (+17.00%), and Tiny-ImageNet (+12.51%).
GHL combines local Oja's rule with competitive learning and a global sign signal to outperform prior Hebbian methods and narrow the performance gap with backpropagation on large-scale tasks.
citing papers explorer
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Error Highways: Scaling Predictive Coding to Very Deep Networks
Highway error propagation augments predictive coding with feedback matrices V to deliver depth-independent error corrections, allowing effective training of 128-layer MLPs while preserving local synaptic updates.
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HCL-FF: Hierarchical and Contrastive Learning for Forward-Forward Algorithm
HCL-FF augments the Forward-Forward algorithm with hierarchical learning and contrastive objectives to reach new state-of-the-art accuracies among FF methods on CIFAR-10 (+5.46%), CIFAR-100 (+17.00%), and Tiny-ImageNet (+12.51%).
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Hebbian Learning with Global Direction
GHL combines local Oja's rule with competitive learning and a global sign signal to outperform prior Hebbian methods and narrow the performance gap with backpropagation on large-scale tasks.