A sparse generative model of V1 simple cells with intrinsic plasticity

Neural Computation
Cornelius Weber, Jochen Triesch

Abstract

Current models for learning feature detectors work on two timescales: on a fast timescale, the internal neurons' activations adapt to the current stimulus; on a slow timescale, the weights adapt to the statistics of the set of stimuli. Here we explore the adaptation of a neuron's intrinsic excitability, termed intrinsic plasticity, which occurs on a separate timescale. Here, a neuron maintains homeostasis of an exponentially distributed firing rate in a dynamic environment. We exploit this in the context of a generative model to impose sparse coding. With natural image input, localized edge detectors emerge as models of V1 simple cells. An intermediate timescale for the intrinsic plasticity parameters allows modeling aftereffects. In the tilt aftereffect, after a viewer adapts to a grid of a certain orientation, grids of a nearby orientation will be perceived as tilted away from the adapted orientation. Our results show that adapting the neurons' gain-parameter but not the threshold-parameter accounts for this effect. It occurs because neurons coding for the adapting stimulus attenuate their gain, while others increase it. Despite its simplicity and low maintenance, the intrinsic plasticity model accounts for more experimental ...Continue Reading

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Citations

Apr 24, 2013·Psychopharmacology·Claire WhiteMark Edwards
Mar 20, 2010·Neural Computation·Laurent U Perrinet
Mar 20, 2010·Neural Computation·R Rossi Pool, G Mato
Apr 28, 2010·PLoS Computational Biology·Cristina SavinJochen Triesch
Apr 5, 2013·PLoS Computational Biology·Viola PriesemannMichel Le Van Quyen
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Jan 21, 2012·Journal of Neurophysiology·Rahul Kumar Rathour, Rishikesh Narayanan

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