Deriving connectivity from spiking activity in biophysical cortical microcircuits

Inferring detailed 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 between pairs of neurons, but previous studies did not address confounds of physiological large-scale networks such as correlated firing and inactive neurons. We tested connectivity derivation methods on ground-truth spiking data from detailed models of human cortical microcircuits in different layers and between key neuron types. We showed that physiological oscillations in the large-scale microcircuits hindered derivation accuracy, which was improved using a shorter cross-correlogram analysis window. We then showed that connection derivation was poor in cortical layer 2/3 microcircuits compared to layer 5, due to low firing rates and inactive neurons. General stimulation strategies for layer 2/3 microcircuits led to only a moderate improvement in derivation performance, due to a trade-off between the proportions of inactive neurons and overactive neurons, indicating the need for more refined strategies. Lastly, we showed that derivation of inhibitory connections from somatostatin interneurons targeting distal dendrites required a longer timescale of cross-correlation lags. Our results identify key physiological challenges and methods to improve accuracy in deriving connections from spiking activity in large-scale neuronal microcircuits.