Researchers from UT Austin utilize deep learning and supercomputing to help identify brain tumors.
Using a combination of neuroimaging, genetic research, epigenetics and other biological data in conjunction with artificial intelligence may result in a biological classification of mental illness, rather than diagnosing people based on symptoms alone.
Researchers have created a convolutional neural network to better understand how the brain processes movies of natural scenes. This may be the first step in helping scientists decode how the brain makes sense of dynamic visual surroundings.
A new deep learning algorithm can interpret EEG data from preterm babies and estimate the child's brain functional maturity, researchers report.
A new brain wiring map reconstructs the entire shape and position of more than 300 neurons in the mouse brain.
Georgia Tech researchers are calling on machine learning specialists and cancer researchers to help discover potential new therapies for oncological diseases.
Machine learning study reveals that, much like genetics, brain connectivity patterns are passed down from parents to children.
The way in which fruit flies approach and compute smell similarities could help pave the way for computer search algorithms of the future.
Researchers call for regulations and ethical guidelines to help protect personal privacy and autonomy for those who use neurotechnologies.
Advances in machine learning could allow researchers to detect drug effects that may be missed through conventional statistical analysis, researchers report.
Writing magnetic patterns onto nanowires could help computers better mimic how the brain processes information, a new study reports.
Following just one season, children who have a history of concussion and high impact exposure as a result playing football undergo significant brain changes, researchers report.
