Linear function neurons: Structure and training

DOI10.1007/BF00336991zbMath0583.92030OpenAlexW2078057316WikidataQ39729263 ScholiaQ39729263MaRDI QIDQ1070193

Steven Hampson, Dennis James Volper

Publication date: 1986

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00336991

zbMATH Keywords

neural learninganimal learningAplysia gill withdrawalbinary inputperceptron trainingRescorla-Wagner learningtraining algorithm for linear equations

Mathematics Subject Classification ID

Other natural sciences (mathematical treatment) (92F05) Physiological, cellular and medical topics (92Cxx)

Related Items (6)

Connectionistic models of Boolean category representation ⋮ Disjunctive models of Boolean category learning ⋮ On specifying Boolean functions by labelled examples ⋮ Using the Perceptron Algorithm to Find Consistent Hypotheses ⋮ Improved approximation of linear threshold functions ⋮ A parallel network that learns to play backgammon

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