Gail Carpenter

She defined the "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: while investigating normal and abnormal signal patterns in nerve cells.

It describes a number of neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.

[8] 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.

[8] Per Boston University, where Carpenter is a "Professor Emerita of Mathematics and Statistics," she is acknowledged as having been the very first woman to receive the Institute of Electrical and Electronics Engineers (IEEE) Neural Networks Pioneer Award in 2008.