Using MRI, engineers have found a way to detect light deep in the brain
The new technique could enable detailed studies of how brain cells develop and communicate with each other.
The new technique could enable detailed studies of how brain cells develop and communicate with each other.
Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
A new framework describes how thought arises from the coordination of neural activity driven by oscillating electric fields — a.k.a. brain “waves” or “rhythms.”
MIT professors Roger Levy, Tracy Slatyer, and Martin Wainwright appointed to the 2024 class of “trail-blazing fellows.”
For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.
The honor recognizes professors for their outstanding mentorship of graduate students.
An MIT Values event showcased three women's career journeys and how they are paving the way for the next generation.
Mark Harnett investigates how electrical activity in mammalian cortical cells helps to produce neural computations that give rise to behavior.
For 10th consecutive year, the Institute ranks No. 2 among all colleges and No. 1 among colleges with one main campus, underlying the impact of innovation and critical role of technology transfer.
The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.
An MRI method purported to detect neurons’ rapid impulses produces its own misleading signals instead, an MIT study finds.
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
Single-cell gene expression patterns in the brain, and evidence from follow-up experiments, reveal many shared cellular and molecular similarities that could be targeted for potential treatment.
An MIT study finds the brains of polyglots expend comparatively little effort when processing their native language.
By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.