After Simple Neuron and Firing rule remaining rules are below :

# 3. Pattern Recognition:

After simple neuron, firing rules and important application of neural networks is pattern recognition. Pattern recognition can be enforced by using a pro-act neural network that has been trained accordingly during training the network is trained to associate outputs with input patterns. When the network is used it identifies the input pattern and tries to output the associated output pattern. The power of neural networks comes to life when a pattern that has no output associated with it, is given as an input. In this case, the network gives the output that corresponds to a trained input pattern that is least different from the given pattern.

Above example is trained to recognize the patterns T and H. The associated patterns are all black and all white respectively.

Input Output Input Output

Above white squares represent with 1 and black squares represent with 0 then the truth tables for the 3 neurons after generalizations are below truth table.

**Top Neuron:**

**Middle Neuron :**

**Bottom Neuron:**

From the above tables, it can be seen the following associations can be extracted

Input Output

In this case, it is obvious that the output should be all blacks since the input pattern is almost the same as the “T” pattern.

Input Output

In this case, it is obvious that the output should be all whites since the input pattern is almost the same as the ‘H’ Pattern.

Input Output

Above case, the top row is 2 errors away from the T and 3 from an H. So the top output is black. The middle row is 1 error away from both T and H so the output is random.

The bottom row is 1 error away from T and 2 away from H. Therefore the output is black. The total output of the network is still in favor of the T shape.

# 4. A more complicated Neuron:

The most sophisticated neuron is the McCulloch and Pitts model. It is a variety from the remaining model is that the inputs are ‘weighted’ the effect that each input has at decision making is dependent on the weight of the particular input.