Title: HebbGate: Local Reward‑Modulated Gating for Continual Learning
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Continual Learning, Incremental Learning, Hebbian Plasticity, Local Learning Rules

Abstract: Neural networks that learn continually should acquire new tasks without revisiting old data and with small per-task overhead.  In parameter-isolation CL, existing approaches typically learn dense task masks via backpropagation, which couples mask learning to the backbone optimiser, adds training compute, and inflates
memory with extra mask parameters.
We introduce HebbGate, a parameter-isolation method for continual learning that uses local, reward-modulated gates in place of backpropagated masks. Crucially, each task adds just one scalar per channel (not per weight), keeping memory growth tiny and the masks interpretable. A simple utilisation penalty discourages reuse of over-popular channels, and a $\kappa$-decay capacity warm-up lets new tasks explore larger masks before annealing to the target sparsity, mitigating order bias and improving forward transfer.
For class-incremental evaluation, we replicate each sample across all task-masks and perform one fused forward pass.
HebbGate is equally effective when applied to a two‑layer MLP, a shallow CNN, or a gated ResNet‑18. On Permuted‑MNIST, Split‑CIFAR‑10, and CIFAR-100, it matches the accuracy of gradient‑based masking methods while keeping the parameter footprint essentially unchanged. HebbGate, therefore, offers a lightweight, transparent alternative for exemplar-free, single-head continual learning.

TL;DR: HebbGate applies a local, reward‑modulated Hebbian rule to create sparse task‑specific subnetworks, delivering fast continual learning with minimal extra memory.