INB-Lunch-Seminar

Slow Feature Analysis: Learning with the Slowness Principle

Laurenz Wiskott, Institute for Neuroinformatics at the Ruhr-University Bochum

Slow feature analysis (SFA) is a biologically motivated algorithm for extracting slowly varying features from a quickly varying signal. We have applied SFA to the learning of complex cell receptive fields, visual
invariances for whole objects, and place cells in the hippocampus. On the technical side SFA can be used to extract slowly varying driving forces of dynamical systems and to perform nonlinear blind source separation. Here I will give an overview over these different applications of SFA.