With all its complexities, the human brain is still a long way from being understood. But that hasn’t discouraged three new IEEE Fellows who have been working hard on ways to better understand how this multifaceted organ functions. They were elevated to IEEE’s highest grade of membership this year for their contributions to brain imaging technology and neural networks.
SOLVING THE PUZZLE
To diagnose and treat schizophrenia and other disorders, it’s important to understand the physiological effects of these conditions on the brain. Gaining that knowledge is what drives Vince Calhoun, executive science officer and director of image analysis at Mind Research Network (MRN), a nonprofit organization in Albuquerque dedicated to advancing the diagnosis and treatment of mental illness. He is also a professor in the electrical computer engineering department at University of New Mexico, also in Albuquerque. Calhoun was named Fellow for “contributions to data-driven processing of multimodal brain imaging and genetic data.”
There are several ways to study the brain. For example, its inner structure can be visualized with an MRI machine, its tissue analyzed with a technique known as diffusion tensor imaging, or its activity monitored and studied by recording electrical signals along a person’s scalp—a process known as electroencephalography. Each of these methods has its limitations; none can provide a thorough analysis of the brain. That’s where Calhoun’s research comes in. To fill in at least some of the gaps, Calhoun and his group at MRN’s Medical Image Analysis Lab are combining data obtained through several brain-imaging techniques and developing algorithms to interpret the information. The lab is also writing software to determine how cognitive disorders affect a person’s brain structure and function.
Gail Carpenter is not an electrical or electronics engineer, but that didn’t stop her colleagues from nominating her for Fellow. She’s a professor of mathematics and neural systems and the director of the Cognitive and Neural Systems (CNS) Technology Lab at Boston University. Carpenter was honored for “contributions to adaptive resonance theory (ART) and modeling of Hodgkin-Huxley neurons.”
She began her work in neural modeling in the mid-1970s at the University of Madison, in Wisconsin. After earning a Ph.D. in mathematics from the university in 1974, she published a series of papers on Hodgkin-Huxley models, which define the characteristics of brain cell membranes and demonstrate how actions in neurons are initiated and carried out.
Carpenter became a research associate at Boston University in 1982, where she and her colleague Stephen Grossberg developed an ART model to show how the brain quickly learns and remembers new information without erasing older memories. The theory explains, for example, why we are able to meet a group of strangers and remember their faces without forgetting past acquaintances. Carpenter has helped develop several neural networks to demonstrate how this theory can be applied to problem solving and artificial intelligence. She became a professor at the university in 1989 and in 2002 was named director of the CNS Technology Lab.
In 2008 Carpenter became the first woman to receive the Neural Network Pioneer Award from the IEEE Computational Intelligence Society for work on neural models of cognitive learning principles that combine mathematical, psychological, and anatomical concepts.
Andrzej Cichocki is a professor of electrical engineering at Warsaw University, and he is also head of the Laboratory for Advanced Brain Signal Processing at the Rikagaku Kenkyūjo (known as RIKEN) Brain Science Institute, in Saitama, Japan, where he does most of his research. Cichocki was elevated to Fellow this year for “contributions to applications of blind signal processing and artificial neural networks.”
He and his research team at RIKEN focus on developing tools and software for detection, localization, extraction, recognition, and analysis of brain signals and patterns. They are creating models of cognitive systems in an effort to understand and illustrate how the brain processes and classifies visual, auditory, and olfactory information. One of the group’s areas of focus is blind signal processing, which is the way the human brain processes new information and adapts to unfamiliar environments. The signal is considered “blind” because the person has no prior knowledge of it. The team’s research has been applied to neural networks to demonstrate how computers can learn and adapt to new information in a similar way.
For the list of the 297 new IEEE Fellows, and information on how to nominate a colleague for the honor, read “Introducing the 2013 Fellows.”