标题： 基于去甲肾上腺素的增益调制减少联合训练中的稳定性差距 显示英文标题

标题： Noradrenergic-inspired gain modulation attenuates the stability gap in joint training

摘要： 最近在持续学习领域的研究发现，当吸收新任务时，对已掌握任务的性能会出现暂时下降，这被称为稳定性差距。 这种动态与持续学习的目标相矛盾，揭示了在减轻遗忘方面缺乏鲁棒性，并且值得注意的是，在理想联合损失制度下仍然存在。 在理想化的联合训练背景下研究这一差距对于将其与其他遗忘来源区分开来至关重要。 我们认为，这反映了任务边界处快速适应与稳健保持之间的不平衡，强调了需要研究能够在持续学习框架内协调可塑性和稳定性的机制。 生物大脑通过在多个时间尺度上同时运作来解决类似的困境，利用神经调节信号来调节突触可塑性。 然而，人工网络缺乏原生的多时间尺度动态，尽管像动量-SGD和Adam这样的优化器引入了隐式的時間尺度正则化，它们仍然表现出稳定性差距。 受蓝斑介导的去甲肾上腺素爆发的启发，在不确定性下暂时增强神经元增益以促进感官整合，我们提出了不确定性调制的增益动态——一种近似双时间尺度优化器的自适应机制，能够动态平衡知识整合与对先前巩固信息的最小干扰。 我们在联合训练下的MNIST和CIFAR基准的不同领域增量和类别增量变体上评估了我们的机制，证明不确定性调制的增益动态能够有效减弱稳定性差距。 最后，我们的分析阐明了增益调制如何复制皮层电路中的去甲肾上腺素功能，为减少稳定性差距和提高持续学习任务的性能提供了机制上的见解。 显示英文摘要

摘要： Recent studies in continual learning have identified a transient drop in performance on mastered tasks when assimilating new ones, known as the stability gap. Such dynamics contradict the objectives of continual learning, revealing a lack of robustness in mitigating forgetting, and notably, persisting even under an ideal joint-loss regime. Examining this gap within this idealized joint training context is critical to isolate it from other sources of forgetting. We argue that it reflects an imbalance between rapid adaptation and robust retention at task boundaries, underscoring the need to investigate mechanisms that reconcile plasticity and stability within continual learning frameworks. Biological brains navigate a similar dilemma by operating concurrently on multiple timescales, leveraging neuromodulatory signals to modulate synaptic plasticity. However, artificial networks lack native multitimescale dynamics, and although optimizers like momentum-SGD and Adam introduce implicit timescale regularization, they still exhibit stability gaps. Inspired by locus coeruleus mediated noradrenergic bursts, which transiently enhance neuronal gain under uncertainty to facilitate sensory assimilation, we propose uncertainty-modulated gain dynamics - an adaptive mechanism that approximates a two-timescale optimizer and dynamically balances integration of knowledge with minimal interference on previously consolidated information. We evaluate our mechanism on domain-incremental and class-incremental variants of the MNIST and CIFAR benchmarks under joint training, demonstrating that uncertainty-modulated gain dynamics effectively attenuate the stability gap. Finally, our analysis elucidates how gain modulation replicates noradrenergic functions in cortical circuits, offering mechanistic insights into reducing stability gaps and enhance performance in continual learning tasks.