Slowness: An Objective for Spike-Timing-Dependent Plasticity?

Sprekeler, Henning and Michaelis, Christian and Wiskott, Laurenz (2006) Slowness: An Objective for Spike-Timing-Dependent Plasticity? [Preprint]

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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.

Item Type:Preprint
Keywords:spike-timing dependent plasticity STDP slowness Slow Feature Analysis SFA invariance learning computational neuroscience modeling trace rule
Subjects:Neuroscience > Neural Modelling
Neuroscience > Computational Neuroscience
Biology > Theoretical Biology
ID Code:5281
Deposited By: Sprekeler, Henning
Deposited On:12 Dec 2006
Last Modified:11 Mar 2011 08:56

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