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The model proposed by McCulloch and Pitts is documented as linear threshold gate [1]. The artificial neuron takes a set of input I₁, I₂, I₃, … …, IN ∈ {0, 1} and produces one output, y ∈ {0, 1}. Input sets are of two types: one is dependent input termed as excitatory input and the other is independent input termed as inhibitory input. Mathematically, the function can be expressed by the following equations:

(2.1)

(2.2)

where

W₁, W₂, W₃, …, …, WN ≡ weight values associated with the corresponding input which are normalized in the range of either (0, 1) or (−1, 1);

S ≡ weighted sum;

θ ≡ threshold constant.

The function f is called linear step function shown in Figure 2.3.

The schematic diagram of linear threshold gate is given in Figure 2.4.

This initial two-state model of ANN is simple but has immense computational power. The disadvantage of this model is lack of flexibility because of fixed weights and threshold values. Later McCulloch-Pitts neuron model has been improved incorporating more flexible features to extend its application domain.