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Functional bipartite invariance in mouse primary visual cortex receptive fields.

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Title: Functional bipartite invariance in mouse primary visual cortex receptive fields.
Authors: Ding, Zhiwei (AUTHOR), Tran, Dat (AUTHOR), Ponder, Kayla (AUTHOR), Ding, Zhuokun (AUTHOR), Froebe, Rachel (AUTHOR), Ntanavara, Lydia (AUTHOR), Fahey, Paul G. (AUTHOR), Cobos, Erick (AUTHOR), Baroni, Luca (AUTHOR), Diamantaki, Maria (AUTHOR), Wang, Eric Y. (AUTHOR), Chang, Andersen (AUTHOR), Papadopoulos, Stelios (AUTHOR), Fu, Jiakun (AUTHOR), Muhammad, Taliah (AUTHOR), Papadopoulos, Christos (AUTHOR), Cadena, Santiago A. (AUTHOR), Evangelou, Alexandros (AUTHOR), Willeke, Konstantin (AUTHOR), Anselmi, Fabio (AUTHOR)
Source: Nature Neuroscience. Apr2026, Vol. 29 Issue 4, p851-863. 13p.
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. This study uses the inception loop framework to map neuronal invariances in mouse V1, revealing a bipartite receptive-field organization linked to segmentation and a synaptic-level hierarchy of increasing invariance supported by the MICrONS dataset. [ABSTRACT FROM AUTHOR]
Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Functional bipartite invariance in mouse primary visual cortex receptive fields.
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  Data: 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. This study uses the inception loop framework to map neuronal invariances in mouse V1, revealing a bipartite receptive-field organization linked to segmentation and a synaptic-level hierarchy of increasing invariance supported by the MICrONS dataset. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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