Functional organization and natural scene responses across mouse visual cortical areas revealed with encoding manifolds
4 affiliations
- 1School of Science and Technology, IE University, Madrid, Spain
- 2Jules Stein Eye Institute, Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
- 3Department of Physiology, University of California, San Francisco, California, United States of America
- 4Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, California, United States of America
Abstract
A challenge in sensory neuroscience is understanding how populations of neurons operate in concert to represent diverse stimuli. To meet this challenge, we have created “encoding manifolds” that reveal the overall responses of brain areas to diverse stimuli and organize individual neurons in stimulus-response coordinates according to their selectivity and response dynamics. Here we use encoding manifolds to compare the population-level encoding of primary visual cortex (VISp) with that of five higher visual areas (VISam, VISal, VISpm, VISlm, and VISrl), using data from the Allen Institute Visual Coding–Neuropixels dataset from the mouse. We show that the topology of the encoding manifold for VISp and for higher visual areas is continuous, with smooth coordinates along which stimulus selectivity and response dynamics are organized with layer and cell-type specificity. Surprisingly, the manifolds revealed novel relationships between how natural scenes are encoded relative to static gratings—a relationship conserved across visual areas. Namely, neurons preferring natural scenes preferred either low or high spatial frequency gratings, but not intermediate ones. Analyzing responses by cortical layer reveals a preference for gratings concentrated in layer 6, whereas preferences for natural scenes tended to be higher in layers 2/3 and 4. The results demonstrate how machine learning approaches can be used to organize and visualize the structure of sensory coding, thereby revealing novel relationships within and across brain areas and sensory stimuli.
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BibTeX
@article{dyballa2026functional,
title = {{Functional organization and natural scene responses across mouse visual cortical areas revealed with encoding manifolds}},
author = {Luciano Dyballa and Greg D. Field and Michael P. Stryker and Steven W. Zucker},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0356243},
url = {https://doi.org/10.1371/journal.pone.0356243},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Functional organization and natural scene responses across mouse visual cortical areas revealed with encoding manifolds
AU - Luciano Dyballa
AU - Greg D. Field
AU - Michael P. Stryker
AU - Steven W. Zucker
PY - 2026
DA - 2026/09/17
JO - PLOS One
DO - 10.1371/journal.pone.0356243
UR - https://doi.org/10.1371/journal.pone.0356243
AB - A challenge in sensory neuroscience is understanding how populations of neurons operate in concert to represent diverse stimuli. To meet this challenge, we have created “encoding manifolds” that reveal the overall responses of brain areas to diverse stimuli and organize individual neurons in stimulus-response coordinates according to their selectivity and response dynamics. Here we use encoding manifolds to compare the population-level encoding of primary visual cortex (VISp) with that of five higher visual areas (VISam, VISal, VISpm, VISlm, and VISrl), using data from the Allen Institute Visual Coding–Neuropixels dataset from the mouse. We show that the topology of the encoding manifold for VISp and for higher visual areas is continuous, with smooth coordinates along which stimulus selectivity and response dynamics are organized with layer and cell-type specificity. Surprisingly, the manifolds revealed novel relationships between how natural scenes are encoded relative to static gratings—a relationship conserved across visual areas. Namely, neurons preferring natural scenes preferred either low or high spatial frequency gratings, but not intermediate ones. Analyzing responses by cortical layer reveals a preference for gratings concentrated in layer 6, whereas preferences for natural scenes tended to be higher in layers 2/3 and 4. The results demonstrate how machine learning approaches can be used to organize and visualize the structure of sensory coding, thereby revealing novel relationships within and across brain areas and sensory stimuli.
SN - 1932-6203
ER -