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This paper shows that predictive coding networks compute the same gradients as backpropagation in the limit of width much larger than depth, bridging biological learning and standard neural network training.
Proposes Adaptive Multi-Scale Goodness Aggregation (AMSGA), an extension of the Forward-Forward algorithm that improves stability, robustness, and generalization via multi-scale goodness aggregation, adaptive hard negative mining, and layer-dependent thresholds, achieving modest accuracy gains on MNIST and Fashion-MNIST.