H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 108 Citations 62,743 324 World Ranking 98 National Ranking 65

Research.com Recognitions

Awards & Achievements

2017 - IEEE Frank Rosenblatt Award

2008 - Fellow of the American Educational Research Association

1994 - Fellow of the American Psychological Association (APA)

1991 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society

1969 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Neuroscience
  • Artificial neural network

His primary areas of investigation include Artificial intelligence, Artificial neural network, Neuroscience, Adaptive resonance theory and Cognition. He has researched Artificial intelligence in several fields, including Machine learning, Visual cortex, Computer vision and Pattern recognition. His study in Visual cortex is interdisciplinary in nature, drawing from both Cognitive neuroscience of visual object recognition, Visual perception, Perception, Cerebral cortex and Illusory contours.

His work carried out in the field of Artificial neural network brings together such families of science as Statistical hypothesis testing, Motor control, Control theory, Nonlinear system and Reinforcement. His studies examine the connections between Adaptive resonance theory and genetics, as well as such issues in Categorization, with regards to Amnesia. His research integrates issues of Cognitive psychology, Information processing, Cognitive science and Amygdala in his study of Cognition.

His most cited work include:

  • A massively parallel architecture for a self-organizing neural pattern recognition machine (2316 citations)
  • Absolute stability of global pattern formation and parallel memory storage by competitive neural networks (2078 citations)
  • Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps (1851 citations)

What are the main themes of his work throughout his whole career to date?

Stephen Grossberg mainly focuses on Artificial intelligence, Artificial neural network, Computer vision, Neuroscience and Visual cortex. His Artificial intelligence study combines topics from a wide range of disciplines, such as Visual perception, Perception and Pattern recognition. His Perception research includes themes of Cognitive science and Cognition.

Stephen Grossberg focuses mostly in the field of Artificial neural network, narrowing it down to topics relating to Speech recognition and, in certain cases, Speech perception. His work on Computer vision is being expanded to include thematically relevant topics such as Communication. His Visual cortex research is multidisciplinary, incorporating perspectives in Motion perception, Illusory contours, Neurophysiology and Receptive field.

He most often published in these fields:

  • Artificial intelligence (46.82%)
  • Artificial neural network (24.34%)
  • Computer vision (21.55%)

What were the highlights of his more recent work (between 2008-2021)?

  • Artificial intelligence (46.82%)
  • Computer vision (21.55%)
  • Perception (12.25%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Computer vision, Perception, Cognitive psychology and Neuroscience are his primary areas of study. He has included themes like Concept learning and Visual cortex in his Artificial intelligence study. Stephen Grossberg interconnects Supervised learning and Invariant in the investigation of issues within Concept learning.

His Computer vision study integrates concerns from other disciplines, such as Depth perception and Communication. His work in the fields of Neuroscience, such as Hippocampal formation, Spatial memory and Temporal cortex, overlaps with other areas such as Acetylcholine. His work deals with themes such as Vigilance, Cognitive science, Cognition and Categorization, which intersect with Adaptive resonance theory.

Between 2008 and 2021, his most popular works were:

  • Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world (322 citations)
  • Recurrent neural networks (233 citations)
  • View-invariant object category learning, recognition, and search: how spatial and object attention are coordinated using surface-based attentional shrouds. (110 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Neuroscience
  • Machine learning

Stephen Grossberg mainly investigates Artificial intelligence, Computer vision, Perception, Neuroscience and Eye movement. The various areas that Stephen Grossberg examines in his Artificial intelligence study include Machine learning, Visual cortex and Communication. His studies in Computer vision integrate themes in fields like Figure–ground and Depth perception.

His Perception research also works with subjects such as

  • Concept learning that connect with fields like Cognitive neuroscience of visual object recognition, Categorization and Adaptive resonance theory,
  • Cognition together with Cognitive science. His Eye movement research incorporates themes from Superior colliculus, Form perception and Gaze. Stephen Grossberg works mostly in the field of Artificial neural network, limiting it down to concerns involving Human–computer interaction and, occasionally, Reinforcement learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A massively parallel architecture for a self-organizing neural pattern recognition machine

Gail A Carpenter;Gail A Carpenter;Stephen Grossberg.
Graphical Models /graphical Models and Image Processing /computer Vision, Graphics, and Image Processing (1987)

3806 Citations

Absolute stability of global pattern formation and parallel memory storage by competitive neural networks

M. A. Cohen;S. Grossberg.
systems man and cybernetics (1983)

2912 Citations

Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps

G.A. Carpenter;S. Grossberg;N. Markuzon;J.H. Reynolds.
IEEE Transactions on Neural Networks (1992)

2660 Citations

System for self-organization of stable category recognition codes for analog input patterns

Gail A. Carpenter;Stephen Grossberg.
Applied Optics (1987)

2566 Citations

Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors

S. Grossberg.
Biological Cybernetics (1976)

2439 Citations

Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system

Gail A. Carpenter;Stephen Grossberg;David B. Rosen.
Neural Networks (1991)

2247 Citations

Nonlinear neural networks: Principles, mechanisms, and architectures

Stephen Grossberg.
Neural Networks (1988)

2029 Citations

How does a brain build a cognitive code

Stephen Grossberg.
Psychological Review (1980)

1964 Citations

The ART of adaptive pattern recognition by a self-organizing neural network

G.A. Carpenter;S. Grossberg.
IEEE Computer (1988)

1926 Citations

Competitive Learning: From Interactive Activation to Adaptive Resonance

Stephen Grossberg.
Cognitive Science (1987)

1826 Citations

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