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There is a concerted effort to build domain-general artificial intelligencein the form of universal neural network models with sufficient computationalflexibility to solve a wide variety of cognitive tasks but without requiringfine-tuning on individual problem spaces and domains. To do this, models needappropriate priors and inductive biases, such that trained models cangeneralise to out-of-distribution examples and new problem sets. Here weprovide an overview of the hallmarks endowing biological neural networks withthe functionality needed for flexible cognition, in order to establish whichfeatures might also be important to achieve similar functionality in artificialsystems. We specifically discuss the role of system-level distribution ofnetwork communication and recurrence, in addition to the role of short-termtopological changes for efficient local computation. As machine learning modelsbecome more complex, these principles may provide valuable directions in anotherwise vast space of possible architectures. In addition, testing theseinductive biases within artificial systems may help us to understand thebiological principles underlying domain-general cognition.

Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture