S
Stephen Grossberg
Researcher at Boston University
Publications - 597
Citations - 61736
Stephen Grossberg is an academic researcher from Boston University. The author has contributed to research in topics: Artificial neural network & Adaptive resonance theory. The author has an hindex of 115, co-authored 596 publications receiving 60330 citations. Previous affiliations of Stephen Grossberg include Northeastern University & Center for Excellence in Education.
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Journal ArticleDOI
A massively parallel architecture for a self-organizing neural pattern recognition machine
TL;DR: A neural network architecture for the learning of recognition categories is derived which circumvents the noise, saturation, capacity, orthogonality, and linear predictability constraints that limit the codes which can be stably learned by alternative recognition models.
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Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
TL;DR: It remains an open question whether the Lyapunov function approach, which requires a study of equilibrium points, or an alternative global approach, such as the LyAPunov functional approach, will ultimately handle all of the physically important cases.
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Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
TL;DR: The fuzzy ARTMAP system is compared with Salzberg's NGE systems and with Simpson's FMMC system, and its performance in relation to benchmark backpropagation and generic algorithm systems.
PatentDOI
System for self-organization of stable category recognition codes for analog input patterns
TL;DR: ART 2, a class of adaptive resonance architectures which rapidly self-organize pattern recognition categories in response to arbitrary sequences of either analog or binary input patterns, is introduced.
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Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors
TL;DR: In this paper, a model for the parallel development and adult coding of neural feature detectors was proposed, where experience can retune feature detectors to respond to average features chosen from the set even if the average features have never been experienced.