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 alsoknown as linear threshold gate. It is a neuron of a set of inputs I1,I2,I3 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:

(2.1)

(2.2)
W1,W2,W3,W4 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

Figure 2.3: Linear Threshold Function

Figure 2.4: Symbolic Illustration of Linear Threshold Gate

The McCulloch-Pitts model of a neuron is simple yet has substantialcomputing potential. It also has a precise mathematical definition. However,this model is so simplistic that it only generates a binary output and also theweight 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.