Artificial Neural Networks : Firing Rules

Firing rules

The firing rules determine the sequence of activities exhibited by neural networks. These rules decide whether a neuron should fire (respond) for any input pattern and include every input pattern.

An Illustration: Pattern Recognition

Pattern recognition is one of the most important applications of Neural Networks.

Pattern recognition can be implemented by using a feed-forward neural network (fig 1). During training phase, the network associates outputs with input patterns. When the network is used, it tries to give the associated output pattern at its output by identifying the input pattern.

If a neural network is given an unknown pattern (having no associated output pattern) at its input, the network gives the output that corresponds to a taught input pattern that is least different from the given pattern.

The network of figure 1 is trained to recognize the patterns T and H. The associated patterns are all black and all white respectively as shown below.

If we represent black squares with 0 and white squares with 1 then the truth tables for the 3 neurons after generalization are:

X11:	0	0	0	1	1	1	1
X12:	0	1	1	0	0	1	1
X13:	1	0	1	0	1	0	1
OUT:	0	1	1	0	0	1	1

Top neuron

X21:	0	0	0	0	1	1	1	1
X22:	0	0	1	1	0	0	1	1
X23:	0	1	0	1	0	1	0	1
OUT:	1	0/1	1	0/1	0/1	0	0/1	0

Middle neuron

X21:	0	0	0	0	1	1	1	1
X22:	0	0	1	1	0	0	1	1
X23:	0	1	0	1	0	1	0	1
OUT:	1	0	1	1	0	0	1	0

Bottom neuron

From the tables it can be seen the following associations can be extracted:

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.

Here also, it is obvious that the output should be all whites since the input pattern is almost the same as the ‘H’ pattern.

Here, 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.