Over the past decade our community has made substantial progress in the construction of anatomically detailed network models of the cortical tissue. Thanks to advances in computer hardware and simulation technology, researchers can now routinely work with these models at the natural density of neurons and synapses. Moreover, the availability of cloud services means that such investigations can be carried out without having to install either the model or the simulation software. A recent workshop analyzed the impact of a specific model of the cortical microcircuit, published ten years ago . The model has been reused in multiple contexts: for reproduction studies, validation of mean-field approaches, exploration of methods of model sharing, and as a building block for larger models. Although the model was less successful in inspiring further neuroscientific studies than the authors of the original work had hoped, it became a de facto benchmark for neuromorphic computing systems. It sparked a constructive race for ever shorter simulation times and lower energy consumption. The quantitative comparison of different platforms reveals qualitative differences between conventional and neuromorphic hardware and limits of speed-up.
The structure of the model is based on light microscopy because these were the data available at the time. Guided by simulation results and physiological evidence, the original publication hypothesized a preference of excitatory neurons for inhibitory targets. Modern electron microscopy data of cortical volumes combined with AI based reconstruction techniques is capable of resolving individual synaptic connections. This advances  the concept of digital twins of the cortical network to a new level of precision, and has already enabled us to confirm the assumption of target type specifity underlying earlier models. Maybe with the progress sketched here, our community is at a transition point where it becomes easier to cooperatively and incrementally work on models with a larger explanatory scope.