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Representational geometry and connectivity-based studies offer complementary insights into neural information processing, but it is unclear how representations and networks interact to generate neural information. Using a multi-task fMRI dataset, we investigate the role of intrinsic connectivity in shaping diverse representational geometries across the human cortex. Activity flow modeling, which generates neural activity based on connectivity-weighted propagation from other regions, successfully recreated similarity structure and a compression-then-expansion pattern of task representation dimensionality. We introduce a novel measure, convergence, quantifying the degree to which connectivity converges onto target regions. As hypothesized, convergence corresponded with compression of representations and helped explain the observed compression-then-expansion pattern of task representation dimensionality along the cortical hierarchy. These results underscore the generative role of intrinsic connectivity in sculpting representational geometries and suggest that structured connectivity properties, such as convergence, contribute to representational transformations. By bridging representational geometry and connectivity-based frameworks, this work offers a more unified understanding of neural information processing and the computational relevance of brain architecture.
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Relating Representational Geometry to Cortical Geometry in the Visual Cortex
A fundamental principle of neural representation is to minimize wiring length by spatially organizing neurons according to the frequency of their communication [Sterling and Laughlin, 2015]. A consequence is that nearby regions of the brain tend to represent similar content. This has been explored in the context of the visual cortex in recent works [Doshi and Konkle, 2023, Tong et al., 2023]. Here, we use the notion of cortical distance as a baseline to ground, evaluate, and interpret measures of representational distance. We compare several popular methods—both second-order methods (Representational Similarity Analysis, Centered Kernel Alignment) and first-order methods (Shape Metrics)—and calculate how well the representational distance reflects 2D anatomical distance along the visual cortex (the anatomical stress score). We evaluate these metrics on a large-scale fMRI dataset of human ventral visual cortex [Allen et al., 2022b], and observe that the 3 types of Shape Metrics produce representational-anatomical stress scores with the smallest variance across subjects, (Z score = -1.5), which suggests that first-order representational scores quantify the relationship between representational and cortical geometry in a way that is more invariant across different subjects. Our work establishes a criterion with which to compare methods for quantifying representational similarity with implications for studying the anatomical organization of high-level ventral visual cortex.
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- Award ID(s):
- 2313150
- PAR ID:
- 10529187
- Publisher / Repository:
- NeurIPS Workshop on Unifying Representations
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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