Neural Algorithms of Human Language

Large speech and language models offer testable algorithmic hypotheses about how the brain understands language. Laura Gwilliams examines neural recordings at several spatial scales, from population activity measured with MEG and intracranial EEG to individual neurons sampled across cortical depth with human Neuropixels probes. The studies ask which representations and operations connect speech sounds to meaning, and how information at different timescales is nested across time and cortical space. Comparing biological recordings with artificial models reveals candidate mechanisms for exchanging information across hierarchical structures and for implementing language understanding at the level of whole neural systems.