Speaker: Duane Nykamp (University of Minnesota)
Title: Estimating connectivity in neuronal and other networks
Abstract: Determining the connectivity structure of complex networks is hindered by one's inability to simultaneously measure the activity of all nodes. In experiments probing networks such as gene regulatory networks, computer networks, or neural networks, many hidden nodes could be interacting with the small set of measured nodes and corrupting estimates of connectivity in unknown ways. For example, if a hidden node had connections onto two measured nodes, this common input could introduce correlations among the measured nodes, which might lead one to erroneously infer a connection between the measured nodes. We present a model-based approach to control for such effects of hidden nodes in networks. We demonstrate the promise of this approach via simulations of small networks of neurons driven by a visual stimulus.