Online neural learning requires models that update after each incoming example, remain calibrated under distributional change, and avoid brittle gradient transmission. The original version of this work used a small static benchmark, a shallow model, few random seeds, and no significance tests. This revision reformulates the problem as genuine prequential online learning and introduces Smooth Online Activation Routing (SOAR), a Hedge-style routing algorithm over smooth activation experts trained by singleinstance adaptive updates. SOAR links activation smoothness with online regret through a prediction-with-expert-advice bound: the mixture incurs only logarithmic-exponential-weight overhead relative to the best activation expert in hindsight, while each expert uses diagonal adaptive online gradients. We evaluate two-hidden-layer online neural networks on eight streams, fourteen methods, thirteen comparators, and eight random seeds, yielding 896 fully reproducible runs. The benchmark includes four real scikit-learn data sets and four synthetic streaming tasks with stationary, nonlinear, gradual-drift, and sudden-drift regimes. Across all dataset-seed blocks, SOAR obtains the best mean final accuracy ( ), best mean negative log-likelihood ( 0.199 ), and best prequential accuracy ( ). Paired Wilcoxon and Holm-corrected tests show significant improvements over ReLU, LeakyReLU, ELU, Softplus, Adam, SGD, and the compact ablation; improvements over the strongest fixed TeLU activation are positive but not statistically significant. The results support a nuanced conclusion: adaptive smooth activation routing is a robust online-learning mechanism and the best overall method in this controlled benchmark suite, but fixed smooth activations remain highly competitive on small static data.