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Transformation of population code from dLGN to V1 facilitates linear decoding

View ORCID ProfileN. Alex Cayco Gajic, Séverine Durand, Michael Buice, Ramakrishnan Iyer, Clay Reid, Joel Zylberberg, Eric Shea-Brown
doi: https://doi.org/10.1101/826750
N. Alex Cayco Gajic
1Laboratoire de Neurosciences Cognitives et Computationnelles, Ecole Normale Supérieure, Paris, France
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  • ORCID record for N. Alex Cayco Gajic
  • For correspondence: natasha.cayco.gajic@ens.fr
Séverine Durand
2Allen Institute for Brain Science, Seattle, USA
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Michael Buice
2Allen Institute for Brain Science, Seattle, USA
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Ramakrishnan Iyer
2Allen Institute for Brain Science, Seattle, USA
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Clay Reid
2Allen Institute for Brain Science, Seattle, USA
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Joel Zylberberg
3Department of Physics and Astronomy and Center for Vision Research, York University, Toronto, Canada
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Eric Shea-Brown
4Department of Applied Mathematics, University of Washington, Seattle, USA
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Summary

How neural populations represent sensory information, and how that representation is transformed from one brain area to another, are fundamental questions of neuroscience. The dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) represent two distinct stages of early visual processing. Classic sparse coding theories propose that V1 neurons represent local features of images. More recent theories have argued that the visual pathway transforms visual representations to become increasingly linearly separable. To test these ideas, we simultaneously recorded the spiking activity of mouse dLGN and V1 in vivo. We find strong evidence for both sparse coding and linear separability theories. Surprisingly, the correlations between neurons in V1 (but not dLGN) were shaped as to be irrelevant for stimulus decoding, a feature which we show enables linear separability. Therefore, our results suggest that the dLGN-V1 transformation reshapes correlated variability in a manner that facilitates linear decoding while producing a sparse code.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted November 04, 2019.
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Transformation of population code from dLGN to V1 facilitates linear decoding
N. Alex Cayco Gajic, Séverine Durand, Michael Buice, Ramakrishnan Iyer, Clay Reid, Joel Zylberberg, Eric Shea-Brown
bioRxiv 826750; doi: https://doi.org/10.1101/826750
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Transformation of population code from dLGN to V1 facilitates linear decoding
N. Alex Cayco Gajic, Séverine Durand, Michael Buice, Ramakrishnan Iyer, Clay Reid, Joel Zylberberg, Eric Shea-Brown
bioRxiv 826750; doi: https://doi.org/10.1101/826750

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