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Learning and energy-entropy dependence in some nonlinear functional-differential systems (1969)
Categories Topics: Mathematical Foundations of Neural Networks, Models: Other,
Author(s) Grossberg, S. |
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 ...

Embedding fields: A theory of learning with physiological implications (1969)
Categories Topics: Biological Learning, Mathematical Foundations of Neural Networks, Models: Other,
Author(s) Grossberg, S. |
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 ...

Some physiological and biochemical consequences of psychological postulates (1968)
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 ...

Some nonlinear networks capable of learning a spatial pattern of arbitrary complexity (1968)
Categories Topics: Mathematical Foundations of Neural Networks, Models: Other,
Author(s) Grossberg, S. |
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 ~ ...

Global ratio limit theorems for some nonlinear functional differential equations, II (1968)
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 ...

Global ratio limit theorems for some nonlinear functional differential equations, I (1968)
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 ...

A prediction theory for some nonlinear functional-differential equations, II: Learning of patterns (1968)
Categories Topics: Mathematical Foundations of Neural Networks,
Author(s) Grossberg, S. |
Abstract This paper studies the following system of nonlinear difference-differentialequations: [...](3)where i, j, k = 1, 2,..., n, and /3 0. We will establish global limit andoscillation theorems for the nonnegative solutions of ...

A prediction theory for some nonlinear functional-differential equations, I: Learning of lists (1968)
Categories Topics: Mathematical Foundations of Neural Networks,
Author(s) Grossberg, S. |
Abstract In this paper, we study some systems of nonlinear functional-differentialequations of the form
X(t) = AX(t) + B(X,) X(t - r) + C(t), t 0, (1)which ...

Nonlinear difference-differential equations in prediction and learning theory (1967)
Categories Topics: Mathematical Foundations of Neural Networks,
Author(s) Grossberg, S. |
Abstract Introduction.-This note introduces some nonlinear difference-differential equations which can be interpreted as a learning theory or, alternatively, as a prediction theory whose goal is to discuss the prediction of ...

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