Byron M. Yu, Ph.D.

Researchers from Carnegie Mellon University, Einstein College of Medicine, and the Champalimaud Foundation introduce a new statistical method, Delayed Latents Across Groups or DLAG, to help detangle concurrent communication across brain areas.

From sunrise to sunset, the flow of communication across brain areas helps to facilitate every move we make. Seeing, hearing, walking, and singing, for example, are made possible by interactions between large collections of neurons that fire simultaneously in our brains. Collaborators from Carnegie Mellon University, Albert Einstein College of Medicine, and the Champalimaud Foundation have teamed up for more than a decade to better understand the flow of communication in the brain using state-of-the-art experimental and statistical methods. Their latest win is a brand-new statistical method, Delayed Latents Across Groups (DLAG), that disentangles signals relayed between brain areas, even when the communication between brain areas is bidirectional. Read the paper in Nature Computational ScienceOpens in new window.

“The method that we’ve developed, DLAG, fits within the broader category of machine learning or statistical methods that are examining high-dimensional neural signals. The novel aspect is to identity activity patterns that are shared across different brain areas,” said Evren Gokcen, a graduate student in electrical and computer engineeringOpens in new window at Carnegie Mellon. “For decades, studies have focused on recording one or a handful of neurons from one brain area at a time. But with advances in neural recording technology, the bottleneck has shifted to being able to analyze and interpret recordings of large populations of neurons from multiple brain areas… Continue reading.

Exploring how brain areas communicate with each other is the focus of a long-standing research collaboration between Carnegie Mellon University, Albert Einstein College of Medicine, and Champalimaud Research. The cross-continental team is simultaneously recording populations of neurons across multiple brain areas in the visual system and utilizing novel statistical methods to observe neural activity patterns being conveyed between the areas. Their latest findings reveal that feedforward and feedback signaling involve different neural activity patterns, lending fresh understanding into how the brain processes visual information.

A myriad of brain functions, such as seeing, hearing, and making decisions, require multiple brain areas to communicate with one another. Researchers have previously studied pairs of neurons or some aggregate metric of neuronal activity across areas to assess how information is taken in, processed, and then acted upon in everyday life. Few groups have studied, in such detail, populations of neurons together to see what type of activity patterns are being communicated across brain areas… Continue reading.

Every brain function, from standing up to deciding what to have for dinner, involves neurons interacting. Studies focused on neuronal interactions extend across domains in neuroscience, primarily using the approaches of spike count correlation or dimensionality reduction. Pioneering research from Carnegie Mellon University has identified a way to bridge these approaches, resulting in a richer understanding of neuronal activity.

Neurons use electrical and chemical signals to relay information throughout the body, and we each have billions of them. Understanding how neurons interact with each other is important, because these correlations influence learning, decision-making, motor control, and many other functions of life… Continue reading.

We’ve all heard the adage, “If at first you don’t succeed, try, try again,” but new research from Carnegie Mellon University and the University of Pittsburgh finds that it isn’t all about repetition. Rather, internal states like engagement can also have an impact on learning.

The collaborative research, published today in Nature Neuroscience in new window, examined how changes in internal states, such as arousal, attention, motivation, and engagement can affect the learning process using brain-computer interface (BCI) technology. Findings suggest that changes in internal states can systematically influence how behavior improves with learning, thus paving the way for more effective methods to teach people skills quickly, and to a higher level of proficiency… Continue reading.

WASHINGTON, D.C. — The American Institute for Medical and Biological Engineering (AIMBE) has announced the election of Byron M. Yu, Ph.D., to its College of Fellows. Dr. Yu was nominated, reviewed, and elected by peers and members of the College of Fellows for outstanding contributions to the study of how populations of neurons work together to enable sensory, cognitive, and motor function.

The College of Fellows is comprised of the top two percent of medical and biological engineers in the country. The most accomplished and distinguished engineering and medical school chairs, research directors, professors, innovators, and successful entrepreneurs comprise the College of Fellows. AIMBE Fellows are regularly recognized for their contributions in teaching, research, and innovation. AIMBE Fellows have been awarded the Nobel Prize, the Presidential Medal of Science and the Presidential Medal of Technology and Innovation and many also are members of the National Academy of Engineering, National Academy of Medicine, and the National Academy of Sciences… Continue reading.