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Computational Neuroscience Journal Club

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If you have a question about this talk, please contact Guillaume Hennequin .

Yan Wu will cover:

Population code in mouse V1 facilitates readout of natural scenes through increased sparseness

Emmanouil Froudarakis, Philipp Berens, Alexander S Ecker, R James Cotton, Fabian H Sinz, Dimitri Yatsenko, Peter Saggau, Matthias Bethge & Andreas S Tolias Nature, 2014

http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.3707.html

ABSTRACT :

Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.

This talk is part of the Computational Neuroscience series.

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