Acosta, F., Conwell, C., Sanborn, S., Klindt, D., Miolane, N.
Abstract
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 mea- sures 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.
Citation
Acosta, F., Conwell, C., Sanborn, S., Klindt, D., & Miolane, N. (2023, December). Relating Representational Geometry to Cortical Geometry in the Visual Cortex. NeurIPS Workshop on Unifying Representations.
BibTeX
@article{osti_10529187, place = {Country unknown/Code not available}, title = {Relating Representational Geometry to Cortical Geometry in the Visual Cortex}, url = {https://par.nsf.gov/biblio/10529187}, journal = {}, publisher = {NeurIPS Workshop on Unifying Representations}, author = {Acosta, Francisco and Conwell, Colin and Sanborn, Sophia and Klindt, David and Miolane, Nina}, }
