Gradients of local circuit architecture shape stability and information integration across the cortical hierarchy

The mammalian neocortex is a complex structure with many interacting regions and cell types. Multiple efforts have recently generated large datasets from multiple cortical regions to determine generalizing principles of cortical organization and activity. One such generalizing principle is the concept of a cortical hierarchy - low-dimensional macroscopic gradients of biological properties across cortical areas and a hierarchy of timescales for information processing. Such gradients in properties along a cortical hierarchy may explain how different cortical regions serve different computational roles (i.e. sensory processing versus working memory). We set out to investigate such principles using biologically realistic simulations. Starting from our previously published model of the mouse primary visual cortex (VISp) with updated connectivity reflecting recent large-scale connectomics and synaptic physiology datasets, we generated multiple versions of the cortical circuit model with increased connectivity as measured along the cortical hierarchy. We then measure the resulting neural activity in response to a simulated single-cell photostimulation paradigm and thalamocortical inputs. We found that overall circuit stability increased across the cortical hierarchy, due to both increases in connection density and increases in the relative proportion of SST cells. The results of this study link cortical cell types to population-level activity, generating insight into the function of local circuits across cortex.