Brain and cognitive sciences
Brain and cognitive sciences

Mapping the brain pathways of visual memorability

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.

A crossroads for computing at MIT

The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.

A new computational technique could make it easier to engineer useful proteins

MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.

Researchers enhance peripheral vision in AI models

By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.

Complex, unfamiliar sentences make the brain’s language network work harder

A new study finds that language regions in the left hemisphere light up when reading uncommon sentences, while straightforward sentences elicit little response.

Deep neural networks show promise as models of human hearing

Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.

Search algorithm reveals nearly 200 new kinds of CRISPR systems

By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.

Using AI to optimize for rapid neural imaging

MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.

The brain may learn about the world the same way some computational models do

Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.

New technique helps robots pack objects into a tight space

Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.