Machine learning study reveals that, much like genetics, brain connectivity patterns are passed down from parents to children.
Georgia Tech researchers are calling on machine learning specialists and cancer researchers to help discover potential new therapies for oncological diseases.
A new brain wiring map reconstructs the entire shape and position of more than 300 neurons in the mouse brain.
A new deep learning algorithm can interpret EEG data from preterm babies and estimate the child's brain functional maturity, researchers report.
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.
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 from UT Austin utilize deep learning and supercomputing to help identify brain tumors.
NYU researchers report stress can lead to a diminished ability to predict new dangers.
Researchers have created computer models of the neural connections in both health brains and brains of people suffering from Parkinson's disease.
McGill University researchers have developed a deep learning algorithm capable of detecting signatures of dementia in patients two years before the onset of symptoms by reviewing a single PET brain scan.
Researchers have developed a new algorithm that uses deep learning techniques to automatically detect and recognizes soccer formations.
Researchers report a convolutional neural network has been used to decode brain signals from EEG data. Scientists believe deep learning systems could be important tools for neuroscience analysis and could help revolutionize brain research.
