Co-hosted by the McGovern Institute, MIT Open Learning, and others, the symposium stressed emerging technologies in advancing understanding of mental health and neurological conditions.
A new study finds that language regions in the left hemisphere light up when reading uncommon sentences, while straightforward sentences elicit little response.
Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.
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.
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.
Training artificial neural networks with data from real brains can make computer vision more robust.
With further development, the programmable system could be used in a range of applications including gene and cancer therapies.
MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks.
