The advent of artificial neural networks, which has benefited from the remarkable success of the brain’s ability to decide and learn, is attempting to transform human society through machine-based representations that mimic patterns of biological neural activity. For example, biologically inspired convolutional neural networks (CNNs) have shown promising performance in a variety of tasks including image recognition, classification and analysis.
Recent studies have adopted a more biologically-realistic compartmental structure in the design of deep learning algorithms. Here, we review subcortical structures and neuromodulatory systems that regulate contextual decision making and learning in the brain, and outline proposals towards more efficient machine-based representations for neuromodulation-aware models of deep-learning. Taken together, a comprehensive review of existing findings on the role of neuromodulators in decision making and learning processes will be essential for evidence-driven, biologically-inspired deep learning models.