## The McCulloch-Pitts Model of Neuron

**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

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.

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