Continuous, adaptive learning, the ability to adapt to the environment and keep improving performance, is a hallmark of natural intelligence. Biological organisms excel in acquiring, transferring, and retaining knowledge while adapting to volatile environments, making them a source of inspiration for artificial neural networks (ANNs). This study explores how neuromodulation, a building block of learning in biological systems, can help address catastrophic forgetting and enhance the robustness of ANNs in continual learning. Driven by neuromodulators including dopamine (DA), acetylcholine (ACh), serotonin (5-HT) and noradrenaline (NA), neuromodulatory processes in the brain operate at multiple scales, facilitating dynamic responses to environmental changes through mechanisms ranging from local synaptic plasticity to global network-wide adaptability. Importantly, the relationship between neuromodulators and their interplay in modulating sensory and cognitive processes is more complex than previously expected, demonstrating a "many-to-one" neuromodulator-to-task mapping. To inspire neuromodulation-aware learning rules, we highlight (i) how multi-neuromodulatory interactions enrich single-neuromodulator-driven learning, (ii) the impact of neuromodulators across multiple spatio-temporal scales, and correspondingly, (iii) strategies for approximating and integrating neuromodulated learning processes in ANNs. To illustrate these principles, we present a conceptual study to showcase how neuromodulation-inspired mechanisms, such as DA-driven reward processing and NA-based cognitive flexibility, can enhance ANN performance in a Go/No-Go task. Though multi-scale neuromodulation, we aim to bridge the gap between biological and artificial learning, paving the way for ANNs with greater flexibility, robustness, and adaptability.

翻译：持续自适应学习，即适应环境并不断提升性能的能力，是自然智能的显著特征。生物有机体在获取、迁移和保留知识的同时能够适应多变环境，这使其成为人工神经网络（ANNs）的重要灵感来源。本研究探讨了神经调节——生物系统学习的基本构建模块——如何帮助缓解灾难性遗忘问题，并增强人工神经网络在持续学习中的鲁棒性。由多巴胺（DA）、乙酰胆碱（ACh）、血清素（5-HT）和去甲肾上腺素（NA）等神经调质驱动的神经调节过程在大脑中跨多尺度运作，通过从局部突触可塑性到全局网络适应性的多种机制，促进对环境变化的动态响应。值得注意的是，神经调质在调节感觉与认知过程中的相互作用关系比以往认知更为复杂，呈现出“多对一”的神经调质-任务映射模式。为启发具有神经调节意识的学习规则，我们重点阐述：（i）多神经调节交互如何丰富单神经调质驱动的学习过程，（ii）神经调质在多重时空尺度上的影响，以及相应的（iii）在人工神经网络中近似与整合神经调节学习过程的策略。为阐明这些原理，我们通过一项概念性研究展示神经调节启发的机制（如DA驱动的奖赏处理与NA基础的认知灵活性）如何提升人工神经网络在Go/No-Go任务中的表现。通过多尺度神经调节的研究，我们致力于弥合生物学习与人工学习之间的鸿沟，为开发更具灵活性、鲁棒性与适应性的人工神经网络开辟道路。