Learning sensory representations with intrinsic plasticity

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Learning sensory representations with intrinsic plasticity
Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting its intrinsic excitability. Previously, we showed that model neurons combining a model of IP based on information theory with Hebbian synaptic plasticity can adapt their weight vector to discover heavy-tailed directions in the input space. In this paper we show how a network of such units can solve a standard non-linear independent component analysis (ICA) problem. We also present a model for the formation of maps of oriented receptive fields in primary visual cortex and compare our results with those from ICA. Together, our results indicate that intrinsic plasticity that tries to locally maximize information transmission at the level of individual neurons may play an important role for the learning of efficient sensory representations in the cortex. r 2006 Elsevier B.V. All rights reserved.
Nicholas Butko, Jochen Triesch
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where IJON
Authors Nicholas Butko, Jochen Triesch
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