Grace Ko

This project develops an intuitive graphical user interface (GUI) to facilitate the alignment and comparison of representational structures across diverse AI models and biological systems. Utilizing Representational Similarity Analysis (RSA), the tool constructs and analyzes Representational Dissimilarity Matrices (RDMs) to identify correspondences between different systems. It incorporates multiple alignment metrics, including Spearman correlation, Centered Kernel Alignment (CKA), Procrustes analysis, and optimization techniques such as ridge regression and Bayesian methods. By enabling researchers to easily compare and visualize representations, the GUI helps uncover shared and unique patterns of information encoding across models and biological data. This approach significantly simplifies complex representational comparisons, bridging machine learning and cognitive neuroscience. Ultimately, the RSA GUI serves as a versatile platform for exploring how information structures align across artificial and biological representations.

We leverage activation spaces from diverse neural network architectures to identify optimal combinations of network processing that functionally emulate brain activity. By systematically exploring and aligning these internal representations with neural responses to sensory stimuli, this approach aims to discover novel, hybrid architectures capable of modeling the brain's complex computational principles