Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

Remarkable progress can be observed in recent years in the controlled emission, guiding and detection of coherent, free electrons. Those methods were applied in matter wave interferometers leading to high phase sensitivities and novel sensor technologies for dephasing influences such as mechanical vibrations or electromagnetic frequencies. However, the previous devices have been large laboratory setups. For future sensor applications or tests of the coherence properties of an electron source, small, portable interferometers are required. Here, we demonstrate a compact biprism electron interferometer that can be used for mobile applications. The design was optimized for small dimensions by beam path simulations. The interferometer has a length between the tip and the superposition plane before magnification of only 47 mm and provides electron interference pattern with a contrast up to 42.7 %. The detection of two dephasing frequencies at 50 and 150 Hz was demonstrated applying second order correlation and Fourier analysis of the interference data.

Cortical connectivity mostly stems from local axonal arborizations, suggesting coordination is strongest between nearby neurons in the range of a few hundred micrometers. Yet multi-electrode recordings of resting-state activity in macaque motor cortex show strong positive and negative spike-count covariances between neurons that are millimeters apart. Here we show that such covariance patterns naturally arise in balanced network models operating close to an instability where neurons interact via indirect connections, giving rise to long-range correlations despite short-range connections. A quantitative theory explains the observed shallow exponential decay of the width of the covariance distribution at long distances. Long-range cooperation via this mechanism is not imprinted in specific connectivity structures but rather results dynamically from the network state. As a consequence, neuronal coordination patterns are not static but can change in a state-dependent manner, which we demonstrate by comparing different behavioral epochs of a reach-to-grasp experiment.