Slow feature analysis for invariant object recognition and its relationship to spike timing dependent plasticity
Speaker: Laurenz Wiskott , Bernstein Center for Computational Neuroscience, BerlinContact:
Date: October 23 2007
Time: 4:00PM to 5:00AM
Location: Bldg 46-3310
Host: Tomaso Poggio, CSAIL, BCS
Thomas Serre, firstname.lastname@example.orgRelevant URL:
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).
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