By David Cai, NYU and NYU Shanghai

Erroneous inferences can arise in data processing due to sampling hazards. Their resolutions often require one to go deeper to understand underlying dynamical mechanisms. Two illustrative examples will be presented. The first example is potential inference hazards in the application of Grange causality (GC). An effective strategy of overcoming GC sampling issues will be described. In particular, the detailed underlying mechanism for its successful application in the reconstruction of the network topology of nonlinear neuronal networks will be discussed. The second example will illustrate in detail how a large scale computational modeling of the primary visual cortex (V1) has helped to resolve uncertainties about cortical mechanisms inferred from optical imaging of the spatiotemporal dynamics of V1.