In vitro validation of in silico identified inhibitory interactions

• Published in: Journal of neuroscience methods 2019-06, Vol.321, p.39-48
• Main Authors: Liu, Honglei, Bridges, Daniel, Randall, Connor, Solla, Sara A., Wu, Bian, Hansma, Paul, Yan, Xifeng, Kosik, Kenneth S., Bouchard, Kristofer
• Format: Article
• Language: English
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2019.04.002

•General linear models are used to extract functional networks.•Inhibitory connections are predicted from in silico perturbations of networks.•Neural Circuit Probe developed to deliver drugs to single cells.•Putative inhibitory connections from in silico procedure validated in vitro. Understanding how neuronal signals propagate in local network is an important step in understanding information processing. As a result, spike trains recorded with multi-electrode arrays (MEAs) have been widely used to study the function of neural networks. Studying the dynamics of neuronal networks requires the identification of both excitatory and inhibitory connections. The detection of excitatory relationships can robustly be inferred by characterizing the statistical relationships of neural spike trains. However, the identification of inhibitory relationships is more difficult: distinguishing endogenous low firing rates from active inhibition is not obvious. In this paper, we propose an in silico interventional procedure that makes predictions about the effect of stimulating or inhibiting single neurons on other neurons, and thereby gives the ability to accurately identify inhibitory effects. To experimentally test these predictions, we have developed a Neural Circuit Probe (NCP) that delivers drugs transiently and reversibly on individually identified neurons to assess their contributions to the neural circuit behavior. Using the NCP, putative inhibitory connections identified by the in silico procedure were validated through in vitro interventional experiments. Together, these results demonstrate how detailed microcircuitry can be inferred from statistical models derived from neurophysiology data.