In silico discovery of representational relationships across visual cortex

Abstract

Now published in Nature Human Behavior doi: https://doi.org/10.1038/s41562-025-02252-z Human vision is mediated by a complex interconnected network of cortical brain areas that jointly represent visual information. While these areas are increasingly understood in isolation, their representational relationships remain elusive. Here we developed relational neural control (RNC), and used it to investigate the representational relationships for univariate and multivariate fMRI responses of areas across visual cortex. Through RNC we generated and explored in silico fMRI responses for large amounts of images, discovering controlling images that align or disentangle responses across areas, thus indicating their shared or unique representational content. This revealed a typical network-level configuration of representational relationships in which shared or unique representational content varied based on cortical distance, categorical selectivity, and position within the visual hierarchy. Closing the empirical cycle, we validated the in silico discoveries on in vivo fMRI responses from independent subjects. Together, this reveals how visual areas jointly represent the world as an interconnected network.

Now published in Nature Human Behavior doi: https://doi.org/10.1038/s41562-025-02252-z

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway
• Gradient-free activation maximization for identifying effective stimuli
• The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes
• The Sensorium competition on predicting large-scale mouse primary visual cortex activity
• The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies