Informing deep neural networks by multiscale principles of neuromodulatory systems

Our brains have evolved the ability to configure and adapt their processing states to match the unique challenges of acting and learning in diverse environments and behavioral contexts. In biological nervous systems, such state specification and adaptation arise in part from neuromodulators, including acetylcholine, noradrenaline, serotonin, and dopamine, whose diffuse release fine-tunes neuronal and synaptic dynamics and plasticity to complement the behavioral context in real-time. Despite the demonstrated effectiveness of deep neural networks for specific tasks, they remain relatively inflexible at generalizing across tasks or adapting to ever-changing behavioral demands. In this article, we provide an overview of neuromodulatory systems and their relationship to emerging pertinent principles in deep neural networks. We further outline opportunities for the integration of neuromodulatory principles into deep neural networks, towards endowing artificial intelligence with a key ingredient underlying the flexibility and learning capability of biological systems.