Utilizing a classic neural network, researchers have created a new artificial intelligence model based on recent biological findings that shows improved memory performance.
Artificial neural networks based on human brain dynamics can outperform current deep learning models in learning capabilities.
"Off-line" periods during AI training mitigated "catastrophic forgetting" in artificial neural networks, mimicking the learning benefits sleep provides in the human brain.
Artificial intelligence helps shed new light on why many with autism have a difficult time when it comes to processing emotions via facial expressions.
Researchers trained an AI to determine which psychotropic agent a zebrafish had been exposed to based on the animal's behaviors and locomotion patterns.
AI network simulations become unstable following continuous periods of unsupervised learning. When the networks were exposed to states that are analogous to human brain waves during sleep, the stability was restored.
A new deep learning algorithm helped researchers identify a powerful new antibiotic compound that kills many of the world's most problematic, disease-causing bacterias, including those which have so far been resistant to common antibiotics.
A new robotic fly, dubbed Deep3DFly, uses active learning to improve its performance.
Artificial vision networks can learn to recognize complex images faster by using topological data analysis.
A biological neural network which is sculpted by evolution provides a scaffolding to facilitate quick and easy learning.
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.
Researchers look at how neuroscience can help with artificial intelligence technology and vice versa.
