Language
English
Publication Date
4-1-2026
Journal
Nature Neuroscience
DOI
10.1038/s41593-026-02213-3
PMID
41741659
PMCID
PMC13061618
PubMedCentral® Posted Date
2-25-2026
PubMedCentral® Full Text Version
Post-print
Abstract
Sensory systems support generalization by representing features that persist under input variation; however, identifying the neuronal basis of these invariances remains difficult due to high-dimensional and nonlinear neural computations. Here we leverage the inception loop paradigm, iterating between large-scale recordings, predictive models and in silico experiments with in vivo verification, to characterize neuronal invariances in mouse primary visual cortex (V1). We synthesize varied exciting inputs (VEIs), dissimilar images that drive target neurons. These VEIs revealed a new bipartite invariance: one subfield encodes a shift-tolerant high-frequency texture and the other encodes a fixed low-frequency pattern. This division aligns with object boundaries defined by spatial frequency differences in highly activating images, suggesting a contribution to segmentation. Analysis of the MICrONS dataset revealed a hierarchy of excitatory neurons in mouse V1 layers 2/3: postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. Together, these results provide insights and scalable methodology for mapping neuronal invariances.
Keywords
Animals, Primary Visual Cortex, Mice, Neurons, Models, Neurological, Photic Stimulation, Visual Fields, Visual Cortex, Visual Pathways, Mice, Inbred C57BL, Neural encoding, Object vision, Pattern vision
Published Open-Access
yes
Recommended Citation
Ding, Zhiwei; Tran, Dat; Ponder, Kayla; et al., "Functional Bipartite Invariance in Mouse Primary Visual Cortex Receptive Fields" (2026). Faculty, Staff and Students Publications. 7257.
https://digitalcommons.library.tmc.edu/baylor_docs/7257