26 Facts About Stephen Grossberg

Stephen Grossberg was born on December 31,1939 and is a cognitive scientist, theoretical and computational psychologist, neuroscientist, mathematician, biomedical engineer, and neuromorphic technologist.

Stephen Grossberg's father died from Hodgkin's lymphoma when he was one year old.

Stephen Grossberg's mother remarried when he was five years old.

Stephen Grossberg then moved with his mother, stepfather, and older brother, Mitchell, to Jackson Heights, Queens.

Stephen Grossberg attended Stuyvesant High School in lower Manhattan after passing its competitive entrance exam.

Stephen Grossberg graduated first in his class from Stuyvesant in 1957.

Stephen Grossberg began undergraduate studies at Dartmouth College in 1957, where he first conceived of the paradigm of using nonlinear differential equations to describe neural networks that model brain dynamics, as well as the basic equations that many scientists use for this purpose today.

Stephen Grossberg then continued to study both psychology and neuroscience.

Stephen Grossberg then went to Stanford University, from which he graduated in 1964 with an MS in mathematics and transferred to The Rockefeller Institute for Medical Research in Manhattan.

Stephen Grossberg received a PhD in mathematics from Rockefeller in 1967 for a thesis that proved the first global content addressable memory theorems about the neural learning models that he had discovered at Dartmouth.

Stephen Grossberg was hired in 1967 as an assistant professor of applied mathematics at MIT following strong recommendations from Mark Kac and Rota.

In 1969, Stephen Grossberg was promoted to associate professor after publishing a stream of conceptual and mathematical results about many aspects of neural networks, including a series of foundational articles in the Proceedings of the National Academy of Sciences between 1967 and 1971.

Stephen Grossberg was hired as a full professor at Boston University in 1975, where he is still on the faculty today.

Stephen Grossberg is a pioneer of the fields of computational neuroscience, connectionist cognitive science, and neuromorphic technology.

Stephen Grossberg's work focuses upon the design principles and mechanisms that enable the behavior of individuals, or machines, to adapt autonomously in real time to unexpected environmental challenges.

Stephen Grossberg has published 18 books or journal special issues, over 560 research articles, and has 7 patents.

Stephen Grossberg has studied how brains give rise to minds since he took the introductory psychology course as a freshman at Dartmouth College in 1957.

At that time, Stephen Grossberg introduced the paradigm of using nonlinear systems of differential equations to show how brain mechanisms can give rise to behavioral functions.

Stephen Grossberg has introduced, and developed with his colleagues, fundamental concepts, mechanisms, models, and architectures across a wide spectrum of topics about brain and behavior.

Stephen Grossberg has collaborated with over 100 PhD students and postdoctoral fellows.

In 1987, Stephen Grossberg founded and was first President of the International Neural Network Society, which grew to 3700 members from 49 states of the United States and 38 countries during the fourteen months of his presidency.

Stephen Grossberg founded the INNS official journal, and was its Editor-in-Chief from 1987 to 2010.

Stephen Grossberg has organized and chaired the annual International Conference on Cognitive and Neural Systems from 1997 to 2013, as well as many other conferences in the neural networks field.

Stephen Grossberg has predicted that "all conscious states are resonant states".

Stephen Grossberg has hereby shown how properties of learning without catastrophic forgetting are ensured in ART via attentional matching between bottom-up feature patterns and learned top-down expectations, leading to a resonant state that persists long enough to drive learning between attended critical feature patterns and active recognition categories.

Stephen Grossberg has introduced and led the development of two computational paradigms that are relevant to biological intelligence and its applications:.