Articles listed below focus on analysis and applications of neural network systems originally developed by CELEST faculty, including ART, ARTMAP, and BCS/FCS.
| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
Neural networks are derived from psychological postulates about punishment and avoidance. The classical notion that drive reduction is reinforcing is replaced by a precise physiological alternative akin to Miller s ""Go"" ... |
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| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Applications:
Other,
Models:
Other, |
| Author(s) |
Grossberg, S. |
Pepe, J. |
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| Abstract |
Possible dependencies of serial learning data on physiological parameters such as spiking thresholds, arousal level, and decay rate of potentials are considered in a rigorous learning model. Influence of these parameters on ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Applications:
Other,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
This note describes laws for the anatomy, potentials, spiking rules, and transmitters of some networks of formal neurons that enable them to learn spatial patterns by Pavlovian conditioning. Applications to spacetime pattern ... |
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| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Applications:
Other,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
Simple psychological postulates are presented which are used to derivepossible anatomical and physiological substrates of operant conditioning.These substrates are compatible with much psychological data aboutoperants. A ... |
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| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Applications:
Other,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
This article reviews results on a learning theory that can be derived from simple psychological postulates and given a suggestive neurophysiological, anatomical, and biochemical interpretation. The neural networks described ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
Pepe, J. |
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| Abstract |
The hypothesis has been advanced thatcertain schizophrenic patients are in acontinual state of overarousal, leading topoor attention, and perhaps to schizophrenicpunning (Kornetsky and Eliasson, 1969;Maher, 1968). ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
1. Introduction - This paper describes some networks ..lf that can learn, simultaneously remember,and perform individually upon demand any number of spatiotemporal patterns(e.g., "motor sequences" and "internal perceptual ... |
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| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
Some possible neural mechanisms of pattern discrimination are discussed, leading to neural networks which can discriminate any number of essentially arbitrarily complicated space-time patterns and activate cells which can ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
|
| Abstract |
1. Introduction. This paper describes some networks 9R that can learn,simultaneously remember, and individually reproduce on demand any numberof spatiotemporal patterns (e.g., "motor sequences") of essentially arbitrary ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
|
| Abstract |
This paper studies the variational systems of two closely related systemsof nonlinear difference-differential equations which arise in prediction- andlearning-theoretical applications ([1], [2], [31). The first system is ... |
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