Universal scale-free representations in human visual cortex

Raj Magesh Gauthaman, Brice Ménard and Michael F Bonner

PLOS Computational Biology, 2025, vol. 21, issue 11, 1-26

Abstract: How does the human brain encode complex visual information? While previous research has characterized individual dimensions of visual representation in cortex, we still lack a comprehensive understanding of how visual information is organized across the full range of neural population activity. Here, analyzing fMRI responses to natural scenes across multiple individuals, we discover that neural representations in human visual cortex follow a remarkably consistent scale-free organization—their variance decay is consistent with a power-law distribution, detected across four orders of magnitude of latent dimensions. This scale-free structure appears consistently across multiple visual regions and across individuals, suggesting it reflects a fundamental organizing principle of visual processing. Critically, when we align neural responses across individuals using hyperalignment, we find that these representational dimensions are largely shared between people, revealing a universal high-dimensional spectrum of visual information that emerges despite individual differences in brain anatomy and visual experience. Traditional analysis approaches in cognitive neuroscience have focused primarily on a small number of high-variance dimensions, potentially missing crucial aspects of visual representation. Our results demonstrate that visual information is distributed across the full dimensionality of cortical activity in a systematic way, thus revealing a key property of neural coding in visual cortex. These findings suggest that we need to move beyond low-dimensional characterizations to fully understand how the brain represents the visual world.Author summary: The human cerebral cortex is thought to encode sensory information in population activity patterns, but the statistical structure of these population codes has yet to be characterized. By examining large-scale neuroimaging recordings of human vision using a spectral approach more common in physics than neuroscience, we reveal the universal scale-free structure of population codes in visual cortex, which is found in all subjects and at multiple stages of visual processing. Moreover, the underlying dimensions of these scale-free representations are strongly shared across individuals, indicating a remarkable convergence toward a common high-dimensional code, despite differences in visual experience or brain anatomy. These findings reveal high-dimensional aspects of cortical representation that are undetectable with conventional methods, such as representational similarity analysis, and they contradict previous theories suggesting that high-level visual cortex representations are low-dimensional. Together, this work identifies a vast space of uncharted dimensions in the human brain that have been largely overlooked in previous work but may be critical for understanding human vision.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013714

DOI: 10.1371/journal.pcbi.1013714

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