Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Such models facilitate identifying candidate mechanisms underlying experimentally observed spatiotemporal activity patterns in the cortex. Multi-layer spiking neuron network models of local cortical circuits covering about 1 mm$^2$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials (LFPs) can be computed from the simulated spiking activity. We here extend a local network and LFP model to an area of 4$\times$4 mm$^2$. The upscaling preserves the densities of neurons while capturing a larger proportion of the local synapses within the model. The procedure further introduces distance-dependent connection probabilities and conduction delays. Based on model predictions of spiking activity and LFPs, we find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental data recorded in the sensory cortex. In contrast with the weak spike-train correlations, the correlation of LFP signals is strong and decays over a distance of several hundred micrometers, compatible with experimental observations. Enhanced spatial coherence in the low-gamma band around 50 Hz may explain the recent experimental report of an apparent band-pass filter effect in the spatial reach of the LFP