We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...
— Inspired by the universal laws governing different kinds of complex networks, we propose a scale-free highlyclustered echo state network (SHESN). Different from echo state netw...
– We have developed and tested a novel artificial neural network for the processing of temporal signals. The working of the units (TempUnit) is based on the mechanism of temporal...
We describe a method for processing large amounts of volumetric data collected from a Knife Edge Scanning Microscope (KESM). The neuronal data that we acquire consists of thin, br...
Bruce H. McCormick, David Mayerich, John Keyser, P...
"KnowledgeMiner" was designed to support the knowledge extraction process on a highly automated level. Implemented are 3 different GMDH-type self-organizing modeling algo...