Biologically plausible models of cognitive flexibility: merging recurrent neural networks with full-brain dynamics

Cognitive flexibility, a cornerstone of human cognition, enables us to adapt to shifting environmental demands. This brain function has been widely explored using computational modeling, although oftentimes these models focus on the operational dimension of cognitive flexibility and do not retain a sufficient level of neurobiological detail to lead to electrophysiological or neuroimaging insights. In this review, we explore recent advances and future directions on neurobiologically plausible computational models of cognitive flexibility. We first cover progress in recurrent neural network models trained to perform flexible cognitive tasks, followed by a discussion on how whole-brain or large-scale brain network models have approached the distributed nature of flexible cognitive functions. Ultimately, we propose here a hybrid framework in which both modeling philosophies converge, advocating for a balanced approach that merges computational power with realistic spatiotemporal dynamics of brain activity, and explore early examples in this direction.