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 |
This paper makes some neurophysiological and biochemical predictionsconcerning transmitter production and release which are suggested bypsychological postulates. A main theme is the joint comrol of presynapticexcitatory ... |
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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 considers various aspects of the global limiting and oscillatorybehavior of the following system of nonlinear differential equations.sx;(t) ... |
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| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
Many of our sensory and motor organs have linearly ordered components, for example the fingers on a hand, the tonotopic organization of the auditory system, the successivjeo ints on arms and legs,t he spine,e tc. This paper ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
A mathematical model with both a psychological and neurophysiological interpretation is introduced to qualitatively explain data about serial learning of lists. Phenomenasuch as bowing, anchoring, chunking, backward ... |
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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 note describes limiting and oscillatory fea-tures of some nonlinear functional-differential systems having appli-cations in learning and nonstationary prediction theory. The mainresults discuss systems ... |
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| Categories |
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
A learning theory in continuous time is derived herein from simple psychologicalpostulates. The theory has an anatomical and neurophysiological interpretation interms of nerve cell bodies, axons, synaptic knobs, membrane ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Applications:
Other,
Models:
Other, |
| Author(s) |
Grossberg, S. |
|
| Abstract |
This note lists some psychological, physiological, and biochemical predictions that have been derived from simple psychological postu]ates. These psychological postulates have been used to derive a nev learning theory, 1-3 ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
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| Abstract |
Introduction: This note describes some nonlinear networks which caD learn a spatial pattern, in "black and white," of arbitrary size and complexity. These networks are a special case of a collection of learning machines ~ ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
|
| Abstract |
Introduction: A previous note [l] introduced some systems of nonlinear functional-differential equations of the form X(t) = AX(t) + B(Xt)X(t - r) + C(t) i £ 0, where X~(xi, • - * , xn) is nonnegative, B(Xt) is a ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
Other, |
| Author(s) |
Grossberg, S. |
|
| Abstract |
1. Introduction. We study some systems of nonlinear functional-differential equations of the form(1)X(t)= A X(1) + B(Xi)X(t - r) + CO), t' 0,where X=(xi,, x„) is nonnegative, B(Xj) =jjB;j(t)jj is a matrixof nonlinear ... |
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