Title: Noradrenergic-inspired gain modulation attenuates the stability gap in joint training
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: stability gap, continual learning, neuromodulation, stability-plasticity dilemma

Abstract: Recent work in continual learning has identified the *stability gap* -- a temporary decline in performance on previously learned tasks when new tasks are introduced. This gap reflects an imbalance between rapid adaptation and robust retention at task boundaries, underscoring the need for mechanisms that reconcile plasticity and stability. In biological systems, this balance is achieved through multi-timescale dynamics regulated by neuromodulatory processes. By contrast, artificial networks rarely incorporate comparable mechanisms. Although optimizers such as momentum-SGD and Adam introduce implicit forms of regularization, they too exhibit stability gaps. Notably, such gaps arise even under ideal joint training, making it crucial to study them in this setting to disentangle their causes from other sources of forgetting. Drawing inspiration from noradrenergic bursts, which enhance neuronal gain under uncertainty, we introduce *uncertainty-modulated gain dynamics* as a two-timescale approach that balances adaptation and retention by adjusting learning rates and flattening the energy landscape. Evaluating on domain- and class-incremental MNIST, CIFAR, and mini-ImageNet benchmarks under joint training, we demonstrate that neuromodulated gain dynamics effectively attenuate the stability gap. Furthermore, our analysis highlights parallels with noradrenergic function in cortical circuits, providing mechanistic insights into how noradrenaline supports perceptual integration with minimal interference.

TL;DR: We mitigate the stability gap in continual learning with a noradrenaline-inspired gain mechanism that adapts learning rates and flattens the loss landscape, reducing interference across tasks.