Gail Carpenter is not an electronics engineer, but that didn’t stop the Institute of Electrical and Electronics Engineers (IEEE) from offering her a fellowship. Carpenter, a Graduate School of Arts & Sciences professor of cognitive and neural systems and of mathematics, received the honor earlier this month for her development of the adaptive resonance theory (ART) and modeling of Hodgkin-Huxley neurons, both of which play a crucial role in neuroscience. ART can be used to model how the brain or a machine can quickly learn, remember, and recognize objects and events, and it can be applied to challenging engineering problems.
“I enjoy working with engineers,” says Carpenter. “Engineers appreciate the notion that the brain is a working model, and they’re used to having mathematical models that describe what they are trying to build and to predict what the bridge can hold or where the rocket will go.”
Virginia Sapiro, dean of Arts & Sciences, says she is delighted for Carpenter. “It is an important and well-deserved recognition of her work,” says Sapiro. “And it will shine a light on the first-rate science for which Boston University faculty are responsible.”
Carpenter says the fellowship shows how far neural modeling has come since she began her work in the field four decades ago. “Back then, neural modeling wasn’t even a field,” she says. “A small number of us would go to conferences in mathematics or psychology and maybe give a talk that was really marginal. It has evolved in a way that none of us would have anticipated.”
In 1987, Carpenter, working with Wang Professor of Cognitive and Neural Systems Stephen Grossberg, a CAS professor of mathematics and statistics and of psychology and a College of Engineering professor of biomedical engineering, developed the adaptive resonance theory. Their work has real-world applications in remote sensing, medical diagnosis, automatic target recognition, mobile robots, and database management. For example, when engineers at Boeing design systems for airplanes, they often redesign parts already in existence, because it takes so long to sort through the archived designs. Using an ART model, they can quickly learn how to retrieve designs that match a rough sketch of a new design.
Carpenter’s research includes the development, computational analysis, and application of neural models of vision, synaptic transmission, and circadian rhythms. Her work in vision has ranged from models of the retina to color processing and long-range figure completion.
Carpenter is a founding member of the Center for Adaptive Systems and the department of cognitive and neural systems and former co–principal investigator for the National Science Foundation’s Center of Excellence for Learning in Education, Science, and Technology (CELEST). She has been elected to successive three-year terms on the board of governors of the International Neural Network Society (INNS), received the INNS Gabor Award, and was named an INNS Fellow. She was the first woman to be given the IEEE Neural Networks Pioneer Award. She has also served as an elected member of the Council of the American Mathematical Society and is a charter member of the Association for Women in Mathematics. Carpenter earned a BA in mathematics from the University of Colorado, Boulder, and a PhD in mathematics from the University of Wisconsin–Madison.
The IEEE, with more than 400,000 members worldwide, is the world’s largest professional association dedicated to advancing technological innovation and excellence. Carpenter was one of 298 scientists named an IEEE Fellow this year.