Pinpointing connectivity despite hidden nodes within stimulus-driven networks.

The effects of hidden nodes can lead to erroneous identification of connections among measured nodes in a network. For example, common input from a hidden node may cause correlations among a pair of measured nodes that could be misinterpreted as arising from a direct connection between the measured nodes. We present an approach to control for effects of hidden nodes in networks driven by a repeated stimulus. We demonstrate the promise of this approach via simulations of small networks of neurons driven by a visual stimulus.