INM-6 Seminar: Talk by Stojan Jovanovic

Bernstein Center Freiburg and KTH, Stockholm

Statistical Inference in Networks with HIdden Units

With recent advances in high-throughput recordings, researchers are turning to statistical models to interpret the data. These methods, however, are limited by the extent to which the population is covered. The effect of what remains hidden on what can be inferred is currently not understood and poses a significant challenge. Here, we have sought for ways to understand and correct the effects of subsampling in inference for the cases of kinetic Ising and Generalized Linear Models. In this work, we derive a second order method to account for these errors by explicitly including hidden nodes and then, using approximation techniques, marginalize out their effect. Through application of this framework on Ising networks of varying relative population size and coupling strength, we asses how these unknown variables can influence inference and to what degree they can be accounted for.

Host:

Prof. Dr. Sonja Grün
Institute of Neuroscience and Medicine (INM-6)
Computational and Systems Neuroscience
Institute for Advanced Simulation (IAS-6)
Theoretical Neuroscience