Circuit-Based Understanding of Fine Spatial Scale Clustering of Orientation Tuning in Mouse Visual Cortex Article Swipe
Peijia Yu
,
Yuhan Yang
,
Olivia Gozel
,
Ian Antón Oldenburg
,
Mario Dipoppa
,
Federico Rossi
,
Kenneth D. Miller
,
Hillel Adesnik
,
Na Ji
,
Brent Doiron
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.11.637768
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.11.637768
In sensory cortex of brain it is often the case that neurons are spatially organized by their functional properties. A hallmark of primary visual cortex (V1) in higher mammals is a columnar functional map, where neurons tuned to different stimuli features are regularly organized in space. However, rodent visual cortex is at odds with this rule and lacks any spatially ordered functional architecture, and rather neuron feature preference is haphazardly organized in patterns termed "salt-and-pepper". This sharp contrast in feature organization between the visual cortices of rodents and higher mammals has been a persistent mystery, fueled in part by abundant evidence of conserved cortical physiology between species. In this work, we applied a novel GCaMP indicator that are localized in the nucleus of neurons during two photon imaging in mouse V1, which enabled us to overcome most spurious spatially correlated activity due to fluorescence contamination, and to ensure a faithful observation of functional organization over space. We found that the orientation tuning properties of distant neuron pairs (> 20 um) are irregularly and randomly organized, while neuron pairs that are extremely close (< 20 um) have strongly correlated orientation tuning, indicating a narrow yet strong spatially clustered organization of orientation preference, which we term "micro-clustered" organization. Exploring a circuit based model of recurrently coupled mouse V1 we derived two key predictions for the micro cluster: spatially localized recurrent connections over a comparable narrow spatial scale, and common relative spatial spreads of balanced excitation and inhibition in the network over broad spatial scales. These predictions are validated by both anatomical and optogenetic-based physiological circuit mapping experiments. Altogether, our work takes an important step in building a circuit-based theory of visual processing in mouse V1 over spatial scales that are often ignored, yet contain powerful synaptic interactions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.02.11.637768
- OA Status
- green
- Cited By
- 3
- References
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407472820
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407472820Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.02.11.637768Digital Object Identifier
- Title
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Circuit-Based Understanding of Fine Spatial Scale Clustering of Orientation Tuning in Mouse Visual CortexWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-02-13Full publication date if available
- Authors
-
Peijia Yu, Yuhan Yang, Olivia Gozel, Ian Antón Oldenburg, Mario Dipoppa, Federico Rossi, Kenneth D. Miller, Hillel Adesnik, Na Ji, Brent DoironList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.02.11.637768Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11844487Direct OA link when available
- Concepts
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Visual cortex, Orientation (vector space), Cluster analysis, Scale (ratio), Computer science, Orientation column, Neuroscience, Pattern recognition (psychology), Artificial intelligence, Psychology, Cartography, Geography, Mathematics, Striate cortex, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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1Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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