Data from: Fundamental activity constraints lead to specific interpretations of the connectome

The continuous integration of experimental data into coherent models of
the brain is an increasing challenge of modern neuroscience. Such models
provide a bridge between structure and activity, and identify the
mechanisms giving rise to experimental observations. Nevertheless,
structurally realistic network models of spiking neurons are necessarily
underconstrained even if experimental data on brain connectivity are
incorporated to the best of our knowledge. Guided by physiological
observations, any model must therefore explore the parameter ranges within
the uncertainty of the data. Based on simulation results alone, however,
the mechanisms underlying stable and physiologically realistic activity
often remain obscure. We here employ a mean-field reduction of the
dynamics, which allows us to include activity constraints into the process
of model construction. We shape the phase space of a multi-scale network
model of the vision-related areas of macaque cortex by systematically
refining its connectivity. Fundamental constraints on the activity, i.e.,
prohibiting quiescence and requiring global stability, prove sufficient to
obtain realistic layer- and area-specific activity. Only small adaptations
of the structure are required, showing that the network operates close to
an instability. The procedure identifies components of the network
critical to its collective dynamics and creates hypotheses for structural
data and future experiments. The method can be applied to networks
involving any neuron model with a known gain function.