Identifying critical components in the structure of multi-scale neural networks

One of the major challenges of computational neuroscience is the integration of available multi-scale experimental data into coherent models of the brain. Building a link between structure and activity, such a model would serve as a tool to identify the mechanisms giving rise to experimental observations. However, even if a model integrates experimental data on brain anatomy to the best of our knowledge, it will be underconstrained. Therefore, physiological observations should guide the exploration of the model's parameter ranges within the uncertainty of the anatomical data. To this end, we here use a mean-field reduction of spiking network dynamics to investigate the mechanisms that give rise to stable and physiologically realistic activity, which often remain unclear from simulation results alone. This reduction allows us to include activity constraints to shape the phase space of large-scale network models. In particular, we apply the theory to a spiking multi-area model of macaque visual cortex (Schmidt et al., 2013) and increase its fixed-point firing rates to a realistic level while preserving the basin of attraction of this fixed point. To achieve this, we control the location of the separatrix dividing the phase space into realistic low-activity behavior and unrealistic high-activity states (Fig. 1). We identify components on the macroscopic level of cortical areas and on the layer-specific population level that are critical for the stability properties of the network. In particular, we find a subcircuit of two frontal areas, 46 and FEF, which crucially influence the network stability. Moreover, we identify connections to the excitatory population of layer 5 to be critical for the network dynamics, in line with experimental findings (Sanchez-Vives et al., 2000; Beltramo et al., 2013). By systematically refining these connections to a small degree, we obtain realistic layer- and area-specific firing rates in the visual cortex model