Independent component analysis in spiking neurons

Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.

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Metadaten
Author:Cristina Savin, Prashant Joshi, Jochen Triesch
URN:urn:nbn:de:hebis:30-83851
DOI:http://dx.doi.org/10.1371/journal.pcbi.1000757
ISSN:1553-7358
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=20421937
Parent Title (English):PLoS Computational Biology
Publisher:Public Library of Science
Place of publication:Lawrence, Kan.
Document Type:Article
Language:English
Date of Publication (online):26.10.2010
Year of first Publication:2010
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Volume:6
Edition:(4): e1000757
Source:PLoS Computational Biology 6(4): e1000757 ; doi:10.1371/journal.pcbi.1000757
HeBIS PPN:229857698
Institutes:Frankfurt Institute for Advanced Studies
Dewey Decimal Classification:150 Psychologie
Sammlungen:Universitätspublikationen
Note:
Copyright: © 2010 Savin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Licence (German):License LogoCreative Commons - Namensnennung 3.0

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