A spiking network model explains multi-scale properties of cortical dynamics

Neural networks in visual cortex are structured into areas, layers, and neuronal populations with specific connectivity at each level. Cortical dynamics can similarly be characterized on different scales, from single-cell spiking statistics to the structured patterns of interactions between areas. A challenge of computational neuroscience is to investigate the relation of the structure of cortex to its dynamics. Network models are promising tools, but for technical and methodological reasons, they have been restricted to detailed models of one or two areas or large-scale models that reduce the internal structure of areas to a small number of differential equations.We here present a multi-scale spiking network model of all vision-related areas of macaque cortex that represents each area by a full-scale microcircuit with area-specific architecture based on a model of early sensory cortex [1]. The layer- and population-resolved network connectivity integrates axonal tracing data from the CoCoMac database with recent quantitative tracing data, and is systematically refined using dynamical constraints [2]. Gaps in the data are bridged by exploiting regularities of cortical structure such as the exponential decay of connection densities with inter-areal distance and a fit of laminar patterns versus logarithmized ratios of neuron densities. Simulations reveal a stable asynchronous irregular ground state with heterogeneous activity across areas, layers, and populations. In the presence of large-scale interactions, the model reproduces longer intrinsic time scales in higher compared to early visual areas, similar to experimental findings [3]. Activity propagates preferentially in the feedback direction, mimicking experimental results associated with visual imagery [4]. Cortico-cortical interaction patterns agree well with fMRI resting-state functional connectivity [5]. The model bridges the gap between local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales