Researchers explain how deep neural networks are able to learn complex physics.
Artificial neural networks based on human brain dynamics can outperform current deep learning models in learning capabilities.
A new software framework incorporates dendritic properties and mechanisms into large-scale neural network models.
Combining new wearable technology and artificial intelligence, researchers are better able to track motion and monitor the progression of movement disorders.
A newly developed machine learning model can predict the words a person is about to speak based on their neural activity recorded by a minimally invasive neuroprosthetic device.
Feed-forward neural networks improve speed and provide more accurate control of brain-controlled prosthetic hands and fingers.
Combining machine learning technology with neuroimaging data, clinicians will be better able to fully analyze a patient's glioblastoma brain tumor and predict cancer progression.
A newly developed, open-source app that utilizes AI technology allows researchers to precisely map the structure of the hippocampus.
Machine learning algorithms can effectively recognize patterns in a patient's neuroimaging data that are specific to rare forms of dementia, allowing for early diagnosis and monitoring of disease progression.
Deep learning technology can accurately reflect a person's risk of cognitive decline and Alzheimer's disease based on brain age.
New AI technology can instantly determine whether a person is above the legal alcohol limit by analyzing a 12-second clip of their voice.
Using machine learning technology, researchers provide new insight into the neural mechanisms that govern anger and aggression.
