A new AI algorithm can independently discover and categorize an animal's behavior by analyzing patterns of body movements.
Researchers discuss different current neural network models and consider the steps that need to be taken to make them more realistic, and thus more useful, as possible.
Combining artificial intelligence technology with raw data from brain activity, researchers accelerate the understanding of how neural activity impacts specific behaviors.
A new AI model can accurately classify a brain tumor of one of six common cancer types from a single MRI brain scan image.
Artificial neural networks modeled on human brain connectivity can effectively perform complex cognitive tasks.
Artificial neural networks help researchers uncover new clues as to why people on the autism spectrum have trouble interpreting facial expressions.
Combining artificial intelligence, mathematical modeling, and brain imaging data, researchers shed light on the neural processes that occur when people use mental abstraction.
Blood tests revealed specific epigenetic biomarkers for schizophrenia. Researchers applied machine learning to analyze the CoRSIVs region of the human genome to identify the schizophrenia biomarkers. Testing the model with an independent data set revealed the AI technology can detect schizophrenia with 80% accuracy.
Researchers propose a novel computational framework that uses artificial intelligence technology to disentangle the relationship between perception and memory in the human brain.
Artificial intelligence technology was able to accurately predict attachment in young children.
Facial expression analysis reveals bronze medalists were more likely to smile on the podium than silver medalists, while gold medalists were displayed the happiest facial expressions.
A new artificial intelligence algorithm can find the quickest trajectory to fly a drone through a series of waypoints on a circuit. The AI proved to be faster at controlling the drone and completing the track than two world-class human pilots.
