Modeling and Understanding Human Brain Computation at Scale

Recent advances in deep learning and improvements in the quantity and quality of available human brain-activity data (including functional MRI, EEG, MEG, and intracranial recordings) have made it possible to build accurate encoding models of the human brain that can predict neural activity for new visual and auditory stimuli in individual people, even with generalization to new individuals. In parallel, recent decoding models leverage prior information from generative multimodal models to extract rich perceptual and semantic content from brain activity with increasing fidelity. It remains unclear, however, how these technical advances can best be translated into theoretical advances (a better scientific understanding of human brain computation) and impactful applications for the benefit of humanity.

One important goal is to build human brain foundation models that are constrained simultaneously by rich stimulus data, large-scale diverse brain-activity data, and task performance requirements, so as to capture the computations performed by the human brain. This satellite event "Modeling and Understanding Human Brain Computation at Scale" brings together researchers who build neural network models that capture shared structure in neural responses across the human population at scale and use the models to drive theoretical progress on the computations underlying human cognition and perception. A central theme is methodology: What mapping functions, architectures, and training regimes achieve strong generalization and enable interpretation? The event aims to foster dialogue between those collecting and modeling large-scale human brain data and those asking what such models can tell us about how the brain works and how human brain foundation models might be applied for human benefit.