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2013 | OriginalPaper | Chapter

3. Threshold Logic Units

Authors : Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held

Published in: Computational Intelligence

Publisher: Springer London

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Abstract

The description of biological neural networks in the preceding chapter makes it natural to model neurons as threshold logic units: if a neuron receives enough excitatory input that is not compensated by equally strong inhibitory input, it becomes active and sends a signal to other neurons. Threshold logic units are also known as McCulloch–Pitts neurons. Another name which is commonly used for a threshold logic unit is perceptron.

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previous chapter Introduction
next chapter General Neural Networks
Footnotes
1
In a perceptron there is, besides the actual threshold logic unit, an input layer that executes additional operations on the input signals. However, this input layer consists of immutable functional elements and therefore is often neglected.
 
2
The somewhat imprecise notion “almost all points” can be made mathematically precise by drawing on measure theory: the set of points at which the error function changes has measure 0.
 
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Metadata
Title
Threshold Logic Units
Authors
Rudolf Kruse
Christian Borgelt
Frank Klawonn
Christian Moewes
Matthias Steinbrecher
Pascal Held
Copyright Year
2013
Publisher
Springer London
Book
Computational Intelligence

Print ISBN: 978-1-4471-5012-1

Electronic ISBN: 978-1-4471-5013-8

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4471-5013-8_3

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