Neuroconstructivism proposes that, as cognitive processes develop, the functional roles of neuronal populations are shaped by their complex interactions with one another across development. However, methodological constraints have limited the study of this developmental complexity.Recent methodological advances in computational modelling offer a new frontier for answering developmental systems neuroscience questions, without losing complexity. We term this frontier ‘computational neuroconstructivism’. The central challenge for this new frontier is the integration of two hitherto disparate literatures: the first is computational models of cognitive development, the second is computational models that implement biophysical constraints.We review the broad literature of computational models as applied to cognitive development and developmental neuroscience. Specifically, we highlight recent advances in generative network modelling that strongly resonate with this computational neuroconstructivist framework. We also highlight ways in which this modelling currently falls short of a full neuroconstructivist account and suggest ways that it could further bridge the gap between models of neural and cognitive development.We highlight the key hallmarks of a good developmental systems neuroscience model and outline future areas of promise for implementing these within computational frameworks. Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.