[Comp-neuro] new e-print available about SFA and STDP

Laurenz Wiskott l.wiskott at biologie.hu-berlin.de
Tue Dec 19 13:44:46 CET 2006


       by Henning Sprekeler, Christian Michaelis, and Laurenz Wiskott

Slow Feature Analysis (SFA) is an efficient algorithm for learning
input-output functions that extract the most slowly varying features from a
quickly varying signal.  It has been successfully applied to the
unsupervised learning of translation-, rotation-, and other invariances in
a model of the visual system, to the learning of complex cell receptive
fields, and, combined with a sparseness objective, to the self-organized
formation of place cells in a model of the hippocampus.

In order to arrive at a biologically more plausible implementation of this
learning rule, we consider analytically how SFA could be realized in simple
linear continuous and spiking model neurons. It turns out that for the
continuous model neuron SFA can be implemented by means of a modified
version of standard Hebbian learning. In this framework we provide a
connection to the trace learning rule for invariance learning. We then show
that for Poisson neurons spike-timing-dependent plasticity (STDP) with a
specific learning window can learn the same weight distribution as
SFA. Surprisingly, we find that the appropriate learning rule reproduces
the typical STDP learning window.  The shape as well as the timescale are
in good agreement with what has been measured experimentally.  This offers
a completely novel interpretation for the functional role of
spike-timing-dependent plasticity in physiological neurons.

Available from:

Sprekeler, H., Michaelis, C., and Wiskott, L. (12. December 2006).
Slowness: An Objective for Spike-Timing-Dependent Plasticity?
Cognitive Sciences EPrint Archive (CogPrints) 5281, http://cogprints.org/5281/.

Also available from:

A short project description is available at:

Prof. Laurenz Wiskott, Institute for Theoretical Biology, Berlin
l.wiskott at biologie.hu-berlin.de

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