"Off-line" periods during AI training mitigated "catastrophic forgetting" in artificial neural networks, mimicking the learning benefits sleep provides in the human brain.
A novel deep learning method that uses graph convolutional neural networks (gCNNs) can predict cognitive function based on the brain's size and structure. The algorithm may provide insights into the relationship between brain morphology and different cognitive functions, as well as declines in cognitive function.
Researchers interested in musical characteristics associated with sleep music and the effects of musical structure on sleep are working together using neuroscience, AI, and sleep medicine to create "super lullaby" soundscapes.
With the help of AI, researchers are developing digital biomarkers that use speech data to identify ALS and frontotemporal dementia.
A new artificial intelligence model automatically learns higher-level language patterns that can apply to different languages, enabling it to achieve better results.
New machine learning models assess the connection between hundreds of clinical variables, including doctor visits and health records for seemingly unconnected conditions, to predict the likelihood of ASD in young children.
A new machine learning algorithm can predict the in-game actions of volleyball players with 80% accuracy.
Neural silencing periods are not a disadvantage representing biological limitations, but rather an advantage for temporal sequencing identification.
Most AI models are unable to represent features of human vision, making them worse at recognizing images.
Using artificial neural networks to analyze neuroimaging data, researchers are able to accurately determine biological age.
A new multitask model artificial intelligence algorithm based on data from wearables predicts treatment outcomes on an individual basis for those with depression.
Researchers have designed a shared-laughter AI system that responds to human laughter in order to build a sense of empathy into dialogue.
