Tracking hippocampal neurons in mice as they watched a movie revealed novel ways to improve artificial intelligence and track neurological disorders associated with memory and learning deficits.
Study sheds light on why some people with autism experience aversion to certain smells and the neural mechanisms that underlie olfactory processing in those with ASD.
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.
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.
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.
