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The human brain is a physically embedded complex system that exhibits exceptional computational capacity despite its limited resources. These limited resources, which include constraints on space, material, and metabolism, systematically shape inter-regional connectivity, which in turn circumscribes the brain's computational dynamics. A key question in neuroscience is how the brain navigates these resource constraints to enable diverse human behavior and cognition. Here, we address this question using recurrent neural networks (RNNs). RNNs are artificial neural networks that can be trained to perform a wide variety of behavioral and cognitive tasks with high performance, making them a natural candidate for studying brain computation. However, RNNs are typically trained in the absence of any resource constraints, which allows them to develop connectivity that---while tailored for high task performance---is …