Prof. Laurenz Wiskott

Bernstein Center for Computational Neuroscience Berlin
Institute for Theoretical Biology, Humboldt-University Berlin

"Slow feature analysis for invariant object recognition and its relationship to spike timing dependent plasticity."

Slow Feature Analysis (SFA) is an 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. Here I will report about our results on invariant object recognition.

If slowness is indeed an important learning principle in visual cortex and beyond, the question arises, how it could be implemented in a biologically plausible learning rule. In the second part of the talk I will show analytically that for linear Poisson units, SFA can be implemented with STDP with the standard learning window as measured by, e.g., Bi and Poo (1998).