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New Research, Sensory and Motor Systems

Organization of Neural Population Code in Mouse Visual System

Kathleen Esfahany, Isabel Siergiej, Yuan Zhao and Il Memming Park
eNeuro 6 July 2018, ENEURO.0414-17.2018; DOI: https://doi.org/10.1523/ENEURO.0414-17.2018
Kathleen Esfahany
1Ward Melville High School, East Setauket, NY 11733, USA
3Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA
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Isabel Siergiej
2Department of Computer Science, Cornell University, Ithaca, NY 14850, USA
3Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA
4Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11794, USA
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Yuan Zhao
3Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA
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Il Memming Park
3Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA
4Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11794, USA
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Abstract

The mammalian visual system consists of several anatomically distinct areas, layers, and cell types. To understand the role of these subpopulations in visual information processing, we analyzed neural signals recorded from excitatory neurons from various anatomical and functional structures. For each of 186 mice, one of six genetically tagged cell-types and one of six visual areas were targeted while the mouse was passively viewing various visual stimuli. We trained linear classifiers to decode one of six visual stimulus categories with distinct spatiotemporal structures from the population neural activity. We found that neurons in both the primary visual cortex and secondary visual areas show varying degrees of stimulus-specific decodability, and neurons in superficial layers tend to be more informative about the stimulus categories. Additional decoding analyses of directional motion were consistent with these findings. We observed synergy in the population code of direction in several visual areas suggesting area-specific organization of information representation across neurons. These differences in decoding capacities shed light on the specialized organization of neural information processing across anatomically distinct subpopulations, and further establish the mouse as a model for understanding visual perception.

Significance Statement This analysis is one of the first of the Allen Brain Observatory’s visual cortex dataset. The mouse has recently emerged as a powerful alternative to primates and carnivorous species as a model for studying visual perception. Mice offer the benefit of large-scale, high-throughput experiments and sophisticated genetic tools useful to investigating highly specific components of visual perception. Preliminary work in identifying the functional organization of mouse extrastriate areas has focused on single neurons and lacks analysis at the population level. Our population decoding analysis contributes novel evidence about the role of many distinct areas and layers of the mouse visual cortex in visual information processing to further establish the mouse as a viable model for future visual system research.

  • decoding
  • population
  • visual cortex

Footnotes

  • Authors report no conflict of interest.

  • This work was partially supported by the Simons Summer Research Program hosted by Stony Brook University. Simons Foundation [funding-id 100000893].

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

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Organization of Neural Population Code in Mouse Visual System
Kathleen Esfahany, Isabel Siergiej, Yuan Zhao, Il Memming Park
eNeuro 6 July 2018, ENEURO.0414-17.2018; DOI: 10.1523/ENEURO.0414-17.2018

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Organization of Neural Population Code in Mouse Visual System
Kathleen Esfahany, Isabel Siergiej, Yuan Zhao, Il Memming Park
eNeuro 6 July 2018, ENEURO.0414-17.2018; DOI: 10.1523/ENEURO.0414-17.2018
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  • decoding
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