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Population Code in Mouse V1 Facilities Read-out of Natural Scenes through Increased Sparseness

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Ecker,  AS
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bethge,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Froudarakis, A., Berens, P., Ecker, A., Cotton, R., Sinz, F., Yatsenko, D., et al. (2014). Population Code in Mouse V1 Facilities Read-out of Natural Scenes through Increased Sparseness. Poster presented at AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece.


Cite as: http://hdl.handle.net/21.11116/0000-0001-32BD-2
Abstract
The neural code is 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 induce 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.