Abstract
A primary goal of sensory systems is to extract robust and meaningful features that are invariant to variations in the sensory input. Characterizing these invariances at the neuronal level is crucial for understanding how the visual system supports generalization, but the high-dimensional nature of ecological stimuli poses major challenges. Consequently, our understanding of how the brain represents invariances has historically depended on a few examples, such as phase invariance to grating stimuli in V1 complex cells. Here, we leverage the inception loop paradigm —iterating between large-scale recordings, deep learning neuronal predictive models, and in silico experiments with in vivo verification—to characterize neuronal invariances in mouse V1. Using a neuronal predictive model, we synthesized Diverse Exciting Inputs (DEIs) that strongly drive target neurons while differing substantially in image space. These DEIs revealed a novel bipartite invariance: one portion of the receptive field encodes shift-invariant, high-frequency textures, while the other encodes a fixed, low-frequency spatial pattern. This subfield division aligned with object boundaries defined by spatial frequency differences in highly activating stimuli, suggesting bi-partite invariance contributes to segmentation. Our analysis of computational models and anatomical data from the MICrONS dataset revealed a hierarchical organization of excitatory neurons in mouse V1 Layers 2/3: We found that postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. These findings suggest a synaptic-level hierarchy that progressively increases neural invariance within the primary visual cortex. Intriguingly, similar high-low frequency bipartite patterns strongly activate certain units in artificial neural networks, suggesting that universal visual representations govern both biological and artificial systems, potentially aiding in the extraction of visual features from complex backgrounds.
Competing Interest Statement
XP is a co-founder of Upload AI, LLC, a company in which he has financial interests. AST is a co-founder of Vathes Inc., and UploadAI LLC companies in which he has financial interests. JR is a co-founder of Vathes Inc., and UploadAI LLC companies in which he has financial interests.
Footnotes
Performed additional closed-loop experiments and analyses to verify in vivo activation of individual DEIs (Fig. 2); Demonstrated the robustness of our DEI synthesis methodology (Fig. 3); Introduced a quantification metric of the bipartite phase invariance (Fig. 4); Included more rigorous analyses on the RF subfield spatial frequency bias (Fig. 5); Added new analyses of the functional-connectomics MICrONS data (Fig. 6); Added new analyses from electrophysiological data to strengthen our findings from the two-photon imaging data; Conducted a series of additional control experiments to rule out alternative explanations for the bipartite RF structure; Updated supplementary material
