Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex

Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into data-driven, biologically realistic models of the mouse primary visual cortex. The models were constructed at two levels of granularity, using either biophysically-detailed or point-neuron models, with identical network connectivity. Both models were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. Three specific predictions emerge from model construction and simulations: about connectivity between excitatory and parvalbumin-negative inhibitory neurons, functional specialization of connections between excitatory neurons, and the impact of the cortical retinotopic map on structure-function relationships. Finally, despite their vastly different neuronal levels of granularity, both models perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.