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The continuous integration of experimental data into coherent models of thebrain is an increasing challenge of modern neuroscience. Such models provide abridge between structure and activity, and identify the mechanisms giving riseto experimental observations. Nevertheless, structurally realistic networkmodels of spiking neurons are necessarily underconstrained even if experimentaldata on brain connectivity are incorporated to the best of our knowledge.Guided by physiological observations, any model must therefore explore theparameter ranges within the uncertainty of the data. Based on simulationresults alone, however, the mechanisms underlying stable and physiologicallyrealistic activity often remain obscure. We here employ a mean-field reductionof the dynamics, which allows us to include activity constraints into theprocess of model construction. We shape the phase space of a multi-scalenetwork model of the vision-related areas of macaque cortex by systematicallyrefining its connectivity. Fundamental constraints on the activity, i.e.,prohibiting quiescence and requiring global stability, prove sufficient toobtain realistic layer- and area-specific activity. Only small adaptations ofthe structure are required, showing that the network operates close to aninstability. The procedure identifies components of the network critical to itscollective dynamics and creates hypotheses for structural data and futureexperiments. The method can be applied to networks involving any neuron modelwith a known gain function.

Fundamental activity constraints lead to specific interpretations of the connectome