Department of Biology, Humboldt-University Berlin, Germany
Wednesday 5 July 2006
Seminar Room B10 (Basement)
Alexandra House, 17 Queen Square, London, WC1N 3AR
The Discovery of Slowness
Slow Feature Analysis (SFA) is an algorithm that we have developed based on the slowness learning principle. SFA 'learns' functions that extract slowly varying features from quickly varying high-dimensional input data. In this talk I will review a number of applications of SFA in neurobiological modeling, (i) learning of invariances in the visual system, (ii) learning of complex-cell receptive fields in V1, (iii) learning of place cells in hippocampus, (iv) STDP learning. Most of the results can be largely understood analytically.