Independent component analysis in spiking neurons

PLoS Computational Biology
Cristina SavinJochen Triesch

Abstract

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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Citations

Jul 31, 2013·Neural Computation·Toby LightheartTien-Fu Lu
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Jun 12, 2013·PLoS Computational Biology·Jörg BornscheinJörg Lücke
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Jul 7, 2017·Neuron·Heather K TitleyChristian Hansel

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

InfoMax

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