Research map / Architectures and efficiency / RNNs and normalization
Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Z-IL instead of IL for weight updates | predictive coding network · ImageNet | runtime per weight update, GPU (ms): 56.3 -> 4.1 | Helped |
| add the phi function for full autonomy (Fa-Z-IL vs Z-IL) | Z-IL · ImageNet | runtime per weight update, GPU (ms): 4.1 -> 4.2 | No clear effect |
| increase detection window td from 1 to 4 (ablation) | Fa-Z-IL phi · Inference-moment detection | success rate: 93.4% -> 100.0% | Helped |
| add C1 (initialize value nodes at prediction equilibrium, sigma=0) (ablation) | Z-IL · MNIST | divergence from BP (test error, final weights) | Helped |
| add C2 (update weights at t=l instead of fixed tc) (ablation) | Z-IL · MNIST | divergence from BP (test error, final weights) | Helped |
| add C3 (integration step gamma=1) (ablation) | Z-IL · MNIST | divergence from BP (test error, final weights) | Helped |
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