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The early model of an artificial neuron is introduced by Warren
McCulloch and Walter Pitts in 1943. The McCullochPitts neural model is also
known as linear threshold gate. It is a neuron of a set of inputs
and one output
. The linear threshold gate simply classifies the set of inputs
into two different classes. Thus the output
is
binary. Such a function can be described mathematically using these
equations:

(2.1) 

(2.2) 
are weight values
normalized in the range of either or and
associated with each input line,
is the weighted
sum, and
is a threshold constant. The function
is a linear step function at threshold
as shown in figure 2.3. The symbolic representation
of the linear threshold gate is shown in figure 2.4
[Has95].
Figure 2.3:
Linear Threshold Function

Figure 2.4:
Symbolic Illustration of Linear Threshold Gate

The McCullochPitts model of a neuron is simple yet has substantial
computing potential. It also has a precise mathematical definition. However,
this model is so simplistic that it only generates a binary output and also the
weight and threshold values are fixed. The neural computing algorithm has
diverse features for various applications [Zur92]. Thus, we need
to obtain the neural model with more flexible computational features.
Next: 2.3.2 The Perceptron
Up: 2.3 Artificial Neural Networks
Previous: 2.3 Artificial Neural Networks
Kiyoshi Kawaguchi
20000617