2.3.1 The McCulloch-Pitts Model of Neuron
The early model of an artificial neuron is introduced by Warren McCulloch and Walter Pitts in 1943. The McCulloch-Pitts neural model is also known as linear threshold gate. It is a neuron of a set of inputs
I1,I2,I3,…,Im
and one output
y
. The linear threshold gate simply classifies the set of inputs into two different classes. Thus the output
y
is binary. Such a function can be described mathematically using these equations:
Sum=∑i=1NIiWi,
(2.1)
y=f(Sum).
(2.2)
W1,W2,W3,…,Wm
are weight values normalized in the range of either
(0,1)
or
(−1,1)
and associated with each input line,
Sum
is the weighted sum, and
T
is a threshold constant. The function
f
is a linear step function at threshold
T
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
Unknown environment 'figure'
Unknown environment 'figure'
Figure 2.4: Symbolic Illustration of Linear Threshold Gate
Unknown environment 'figure'
Unknown environment 'figure'
The McCulloch-Pitts 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.
