PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
Stefan Klampfl and Wolfgang Maass
In: NIPS 2009, 7-10 Dec 2009, Vancouver, Canada.

Abstract

It is open how neurons in the brain are able to learn without supervision to discriminate between spatio-temporal firing patterns of presynaptic neurons. We show that a known unsupervised learning algorithm, Slow Feature Analysis (SFA), is able to acquire the classification capability of Fisher’s Linear Discriminant (FLD), a powerful algorithm for supervised learning, if temporally adjacent samples are likely to be from the same class. We also demonstrate that it enables linear readout neurons of cortical microcircuits to learn the detection of repeating firing patterns within a stream of spike trains with the same firing statistics, as well as discrimination of spoken digits, in an unsupervised manner.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Natural Language Processing
Theory & Algorithms
ID Code:6087
Deposited By:Michael Pfeiffer
Deposited On:08 March 2010