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.
Most AI models are unable to represent features of human vision, making them worse at recognizing images.
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.
People have trouble distinguishing between real people's faces and AI StyleGAN2 synthesized faces. People also consider AI-generated faces to be more trustworthy.
A new machine-learning algorithm is able to teach itself to smell within a few minutes of training. As it learns, the system builds an artificial network that mimics the brain's olfactory system.
Findings could advance the development of deep learning networks based on real neurons that will enable them to perform more complex and more efficient learning processes.
A new AI algorithm can predict the onset of Alzheimer's disease with an accuracy of over 99% by analyzing fMRI brain scans.
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.
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.
