TY  - JOUR
A1  - Savin, Cristina
A1  - Joshi, Prashant
A1  - Triesch, Jochen
T1  - Independent component analysis in spiking neurons
T2  - PLoS Computational Biology
N2  - 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.
Y1  - 2010
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20111
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-83851
SN  - 1553-7358
N1  - 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.
VL  - 6
IS  - (4): e1000757
SP  - 1
EP  - 10
PB  - Public Library of Science
CY  - Lawrence, Kan.
ER  -