Researchers recorded the electrical activity in over 1000 neurons around 100 sites of the brain during different states of consciousness in monkeys. The results were analyzed by machine learning. Results pointed away from the prefrontal cortex, an area monitored to safely maintain general anesthesia, and toward areas at the back of the brain. The study reveals deep brain and areas toward the back of the brain are more predictive of states of consciousness.
Axon myelination is significantly disrupted in patients with Alzheimer's disease. Researchers also found brain cells of men and women vary significantly in how their genes respond to the neurodegenerative disease.
Combining EEG brain function data, brain-computer interface technology, and artificial intelligence, researchers have created a system that can generate an image of what a person is thinking.
EmoNet, a new convolutional neural network, can accurately decode images into eleven distinct emotional categories. Training the AI on over 25,000 images, researchers demonstrate image content is sufficient to predict the category and valence of human emotions.
The brain quickly transitions from one network to another in regular patterns. The transition trajectories constitute a temporal circuit where the conscious brain cycles through a structured pattern of states over time.
Researchers have developed a machine learning tool that could help further understanding of synaptic activity in learning and neurodevelopment.
Using advances in machine learning, researchers have created a new model that predicts the ease with which individuals produce and comprehend complex sentences.
A new computational model predicts how information deep inside the brain could flow from one network to another, and how neural network clusters can self optimize over time.
A new computer model captures the human visual system's ability to quickly generate a detailed scene description from an image.
Researchers have developed a new, fully automated prosthetic arm that learns during normal use and adapts to varying conditions.
The patterns of reasoning deceptive people use may serve as indicators of truthfulness, a new AI algorithm discovered. Researchers say reasoning intent is more reliable than verbal changes and personal differences when trying to determine deception.
Rat R222 was born with hydrocephalus. Neuroimaging revealed most of its brain had compressed and collapsed as it filled with fluid. However, despite its condition, the animal was still able to see, hear, smell, and feel like other animals. The study sheds new light on neuroplasticity, and the findings could have implications for the development of new machine learning technologies.