Learning latent features from time series data is an important problem in both machine learning and brain function.

"We're not quite sure what features our algorithm is looking for," says Professor Laurenz Wiskott from the Institute for Neural Computation. This is because the system has learned to assess faces. The successful algorithm developed by the Bochum-based researchers is a hierarchical neural network with eleven levels. As input data, the researchers fed it with several thousand photos of faces of different ages. The age was known in each case.

The processes underlying artificial intelligence today are in fact quite dumb. Researchers from Bochum are attempting to make them smarter. Radical change, revolution, megatrend, maybe even a risk: artificial intelligence has penetrated all industrial segments and keeps the media busy. Researchers at the RUB Institute for Neural Computation have been studying it for 25 years. Their guiding principle is: in order for machines to be truly intelligent, new approaches must first render machine learning more efficient and flexible.

The processes underlying artificial intelligence today are in fact quite dumb. Researchers from Bochum are attempting to make them smarter. Radical change, revolution, megatrend, maybe even a risk: artificial intelligence has penetrated all industrial segments and keeps the media busy. Researchers at the RUB Institute for Neural Computation have been studying it for 25 years. Their guiding principle is: in order for machines to be truly intelligent, new approaches must first render machine learning more efficient and flexible.

Slow feature analysis (SFA) is an information processing method proposed by Wiskott and Sejnowski (WS02) which allows to extract slowly varying signals from complex multidimensional time series. Wiskott (Wis98) formulated a similar idea already before as a model of unsupervised learning of invariances in the visual system of vertebrates. SFA has been applied successfully to numerous different tasks: to reproduce a wide range of properties of complex cells in primary visual cortex (BW05), to model the self-organized formation of place cells in the hippocampus (FSW07), to classify handwritten digits (Ber05) and to extract driving forces from nonstationary time series (Wis03). The analysis of nonstationary time series plays an important role in the data understanding of various phenomena such as temperature drift in experimental setup, global warming in climate data or varying heart rate in cardiology. Such nonstationarities can be modeled by underlying parameters, referred to as driving forces, that change the dynamics of the system smoothly on a slow time scale or abruptly but rarely, e.g. if the dynamics switches between different discrete states.