Combining artificial intelligence technology with raw data from brain activity, researchers accelerate the understanding of how neural activity impacts specific behaviors.
Researchers propose a novel computational framework that uses artificial intelligence technology to disentangle the relationship between perception and memory in the human brain.
When convolutional neural networks are trained under experimental conditions, they are deceived by the brightness and color of a visual image in similar ways to the human visual system.
Researchers test machine learning algorithms to determine the metal workload and affective states of the human brain.
Deep convolutional neural networks were used to help process neonatal brain image data. Modified LiviaNET and HyperDense-Net deep learning neural networks are discussed.
Researchers have developed a smart onesie that accurately measures the spontaneous and voluntary movements of infants from five months. The smart-clothing could help assess abnormal neurological and motor development in infants.
Study calls into question the use of current machine learning technologies in the search for extra-terrestrial intelligent life.
A new convolutional neural network that utilizes MRI brain scans can forecast genetic mutations in glioma brain tumors.
Artificial vision networks can learn to recognize complex images faster by using topological data analysis.
A convolutional neural network, designed by researchers at MIT, uses MRI datasets to find anatomical structures of the brain. The system could help in diagnosing and treating a range of brain conditions.
A convolutional neural network algorithm is able to process an entire whole brain slice slide with 98.7% accuracy. The technology could help speed up the diagnosis of Alzheimer's disease.
Convolutional neural network model significantly outperforms previous methods and is as accurate as humans in segmenting active and overlapping neurons.
