Wednesday, 22nd July, 2020
Stephen Grossberg is Wang Professor of Cognitive and Neural Systems; Professor Emeritus of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering; and Director of the Center for Adaptive Systems at Boston University. He is a principal founder and current research leader in computational neuroscience, theoretical psychology and cognitive science, and neuromorphic technology and AI. In 1957-1958, he introduced the paradigm of using systems of nonlinear differential equations to develop models that link brain mechanisms to mental functions, including widely used equations for short-term memory (STM), or neuronal activation; medium-term memory (MTM), or activity-dependent habituation; and long-term memory (LTM), or neuronal learning. His work focuses upon how individuals, algorithms, and machines adapt autonomously in real-time to unexpected environmental challenges. The neural network models discovered in this way together provide a blueprint for designing autonomous adaptive intelligent agents. Grossberg founded key infrastructure of the field of neural networks, including the International Neural Network Society and the journal Neural Networks, and has served on the editorial boards of 30 journals. He was General Chairman of the first IEEE International Conference on Neural Networks in 1987, and played a key role while serving as the first INNS President in organizing the INNS First Annual Meeting in 1988. These two meetings fused to become IJCNN. His lecture series at MIT Lincoln Lab led to the national DARPA Study of Neural Networks. He is a fellow of AERA, APA, APS, IEEE, INNS, MDRS, and SEP. He has published 17 books or journal special issues, over 550 research articles, and has 7 patents. He was most recently awarded the 2015 Norman Anderson Lifetime Achievement Award of SEP, the 2017 Frank Rosenblatt computational neuroscience award of IEEE, and the 2019 Donald O. Hebb award for biological learning of INNS.
See the following web pages for further information: