Deriving connectivity from spiking activity in detailed models of large-scale cortical microcircuits

Inferring large-scale cortical microcircuit connectivity is essential for uncovering how information is processed in the brain. A common method in vivo uses short-lag spike cross-correlations to derive putative monosynaptic connections, but key confounds of deriving connections of physiological large-scale networks, including inactive neurons and correlated firing, can hinder derivation accuracy. We tested connectivity derivation using simulated ground-truth spiking from detailed models of human cortical microcircuits in different layers. While derivation accuracy was high for cortical layer 5 microcircuits, low-firing and inactive neurons in layer 2/3 microcircuits required activation. General activation paradigms yielded only a moderate improvement in derivation performance, due to an increased proportion of correlated overactive neurons. A more refined activation paradigm that led to jittered moderate spiking avoided unwanted correlations and thus improved derivation accuracy. Our study addresses key physiological challenges and provides methods to improve performance in deriving connections from spiking activity in large-scale neuronal microcircuits.