Researchers at MIT have developed a new deep learning neural network that can identify speech patterns indicative of depression from audio data. The algorithm, researchers say, is 77% effective at detecting depression.
A new deep learning system is able to predict, with great accuracy, how different brain areas respond to specific words. The model also found concepts localized to the auditory cortex are less dependent on context.
Researchers report a convolutional neural network has been used to decode brain signals from EEG data. Scientists believe deep learning systems could be important tools for neuroscience analysis and could help revolutionize brain research.