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Gail Alexandra Carpenter, Ph.d (born in 1948) is a cognitive scientist, neuroscientist and mathematician. She is now a "Professor Emerita of Mathematics and Statistics, Boston University." She had also been a Professor of Cognitive and Neural Systems at Boston University, and the director of the Department of Cognitive and Neural Systems (CNS) Technology Lab at Boston University.

Early Life
Gail Carpenter is the only daughter of Chadwick Hunter "Chad" Carpenter (1920-1996) and Ruth M. (nee Stevenson) Carpenter (1920-2010). She has four brothers. )

Carpenter attended the International School of Geneva (1961-1966) then went to the University of Colorado in Boulder earning a B.A. in 1970 (summa cum laude, mathematics). She then earned a Ph.D. in mathematics at the University of Wisconsin–Madison. Carpenter then taught at MIT and Northeastern University before moving to Boston University.

Carpenter married Stephen Grossberg on June 16, 1979 in Boston University Castle in Boston, Massachusetts.

Adaptive resonance theory
Carpenter's neural modeling work began with her 1974 mathematics PhD thesis, [https://pdf.sciencedirectassets.com/272398/1-s2.0-S0022039600X03336/1-s2.0-0022039677901164/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEC8aCXVzLWVhc3QtMSJHMEUCIHRvtBNGbIV%2FO9hYmP%2FPjbxvVYOa4Hc5eZpZdYBL6wgmAiEA5S1BzLzEiTk9HxrYJx3QdjaDSduaFgeWwYPQKwjan90q%2BgMIaBAEGgwwNTkwMDM1NDY4NjUiDPShhRN%2F1iASbv2%2FGSrXA0lmpA8VNmek%2Bi0SANyjWLQzp0w56ky52H7ZWF6W%2FCi7qU0u5MU6dCGfH1eL5oN4cUPe8kjGGtWIMDs%2BxLPJArrzu%2BqEOuotRkwSC0DFcThpSmSucZz0xMP6bl29ZsKQXrLvQaTWuQGDM6sCO8fvnSvGvhOykw08jg0AHS9D4JK51vjjO0eeyXRXmJu1gQAaQdS21vaBcv5t8t%2BdJns%2FXAShxBxU1BtpAWXQKf08%2FymDs3ikxYm5e%2FQBUEmdVzdsj%2FORv%2BZYYrhmVAfsWbKsFSE%2Bd3aV4lB9RpL%2BCU5BJEzyzOHJg0d6DtpB9sLWKsRcZ6bRuBOLywA0y9%2FUxkp6%2Bu%2F3gjCXA2oN%2FlkiXo5CRVOpGpKUugVGXl%2FbbQdz09FcDuI4N%2BJ4Axpe%2B8rixXA4UuvbJy9eRIBz8KL6wbtGfC1HwqAXumQ3aSGdclMAgfjA838necYpVPbCP3KmFDONLNeOGtKLCQjFrEpSYksFQvBovZ%2Ba3LOzNFEyWk1r1XYhQrCNrsVlmP1g91gXkUyxIwTEcUmv9yg6DmaKtPj9UwTZ0Ad6svivreC1M6xvGiNjMRqPpYN2gUGPGgOHSuSj2lD8BU%2BD2%2F5Z0PwvmivXultMzPFL0HVqkDCWmeyPBjqlAe7mhOgisRG3ktvjRBGETQO8CX105I85ZwJ3QpPfLyQLL6EnqPz%2BeQsbw%2FUdXyPcnaj5hKs3DQFm2og5zi8FNBQq5dw38WGUK0Boi1pGYaiQGWa%2BjmWc71zdmnkyxG18fOMBFclqpSLJSejzAaF%2F8NxBh9CnbkJIoGLwmtDy8vAC56h7v5BA7hSS0YgOKDA9SIKrI%2BQBfaWN7cWkw%2BoRXpJwyci81w%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220203T000424Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHB62F32%2F20220203%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=6020f89c70d100680137017db000bdc963cc7ccdef51ed56f772e0bcebcf4f78&hash=124fa557dd2016616c17a812ab6765478cc3567faf3e4d0a771d7ff83ac0fd0e&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=0022039677901164&tid=spdf-c2967795-d284-4c1d-8e58-be37aa14da6b&sid=b8322a47902e884770089ec7c858ec752ba4gxrqa&type=client&ua=500a545154560251530d&rr=6d7766d42b407bad| Traveling wave solutions of nerve impulse equations] at the University of Wisconsin Department of Mathmatics working with Charles C. Conley. In a series of papers published in the 1970s, she defined generalized Hodgkin-Huxley models, used dynamical systems techniques to analyze their solutions, and characterized the qualitative properties of the burst suppression patterns that a typical neuron may propagate and investigated normal and abnormal signal patterns in nerve cells.

Together with her students and colleagues, Carpenter has since the 1980s, developed the adaptive resonance theory (ART) family of neural networks for fast stable online learning, pattern recognition, and prediction. ART models have been used for a wide range of applications, including remote sensing, medical diagnosis, automatic target recognition, mobile robots, and database management.

Other aspects of her research include the development, computational analysis, and application of neural models of vision, synaptic transmission, and circadian rhythms. Her work in vision ranges from models of photoreceptors to color processing and long-range figure completion]

At Boston University, she served as founder and director of the CNS Technology Lab and as a founding member of the | Center for Adaptive Systems and the | Department of Cognitive and Neural Systems.

Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.

The primary intuition behind the ART model is that object identification and recognition generally occur as a result of the interaction of 'top-down' observer expectations with 'bottom-up' sensory information. The model postulates that 'top-down' expectations take the form of a memory template or prototype that is then compared with the actual features of an object as detected by the senses. This comparison gives rise to a measure of category belongingness. As long as this difference between sensation and expectation does not exceed a set threshold called the 'vigilance parameter', the sensed object will be considered a member of the expected class. The system thus offers a solution to the 'plasticity/stability' problem, i.e. the problem of acquiring new knowledge without disrupting existing knowledge that is also called incremental learning.

Academic acknowledgements
She was the first woman to receive the Institute of Electrical and Electronics Engineers (IEEE) Neural Networks Pioneer Award in 2008. She has been elected to successive three-year terms on the Board of Governors of the International Neural Network Society (INNS) ) since its founding, in 1987, and received the INNS Gabor Award in 1999. 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.

Additional awards and honors include: Institute of Electrical and Electronics Engineers IEEE Fellow Award (2013) IEEE Senior Membership Award (2011) IEEE Neural Networks Pioneer Award (2008) International Neural Network Society INNS Fellow Award (2011) College of Fellows (2011 – ) Governing Board (1987–2010) Secretary & Executive Committee (1994–2000) Vice President (1988 1989) American Mathematical Society AMS Council – Member at Large (1996–1999) Committee on the Profession (1996–1999) Liaison Committee with AAAS (2004–2006) Editorial Boards Brain Research IEEE Transactions on Neural Networks Neural Networks Biologically Inspired Cognitive Architectures Memberships American Mathematical Society (AMS) Association for Women in Mathematics (AWM) Institute of Electrical and Electronics Engineers (IEEE) IEEE Computational Intelligence Society International Neural Network Society (INNS)

Published articles include
Carpenter, G. A. (2019). Looking to the future: Learning from experience, averting catastrophe. Neural Networks. Carpenter, G. A., & Grossberg, S. (1987). A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics and Image Processing, 37(1), 54–115. https://doi.org/10.1016/S0734-189X(87)80014-2 Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., & Rosen, D. B. (1992). Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps. IEEE Transactions on Neural Networks, 3(5), 698–713. https://doi.org/10.1109/72.159059 Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991). ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks, 4(5), 565–588. https://doi.org/10.1016/0893-6080(91)90012-T Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991). Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4(6), 759–771. https://doi.org/10.1016/0893-6080(91)90056-B