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### C4. The comparison set contains the closest alternatives
-The main comparison must include exact BP, FA, DFA, direct/unamortized node perturbation, Dual Prop,
-and a burst/dendritic credit-assignment method such as BurstCCN. EP remains an informative
+The main comparison must include exact BP, FA, DFA, direct/unamortized node perturbation, learned
+direct node-perturbation feedback (Lansdell et al. 2020), Dual Prop, and BurstCCN. The Lansdell
+method is the exact no-traffic/P=0 backbone of the current implementation, not merely a related
+baseline. BurstCCN is already demonstrated on CIFAR-10 and ImageNet and already models the Harnett
+BCI signatures, so it is the strongest dendritic/scaling comparator. EP remains an informative
relaxation-based baseline. Forward-Forward and PEPITA are appendix context unless they become
competitive under the frozen protocol.
@@ -108,10 +111,11 @@ claim is explicitly computational rather than a fit to cortical data.
## Oral bar A: standard deep architectures
Prepare convolutional local-update primitives and ResNet-20/32/56 protocols early. Queue frozen
-runs opportunistically on authorized idle GPUs. The oral-level target is a memorable joint result:
-near-BP accuracy in a genuinely deep standard model, a win over the strongest local baseline, and
-a nondominated accuracy–hardware-independent-cost point. Scale alone is not enough if C1 does not
-show that somato-dendritic innovation is the load-bearing operation.
+runs opportunistically on authorized idle GPUs. Because BurstCCN already reports CIFAR-10 and
+ImageNet scaling, dataset scale alone is not novel. The oral-level target is a memorable joint
+result: near-BP accuracy in a standard architecture, a win over or materially simpler/lower-cost
+regime than BurstCCN, and a nondominated accuracy–hardware-independent-cost point. Scale alone is
+also insufficient if C1 does not show that somato-dendritic innovation is load-bearing.
## Stop conditions