Perceptrons were the first of many neural network models.
They were proposed by McCullogh and Pitts in 1943. As with all
computational models the perceptrons aim to show human cognitive
abilities. They use a network system consisiting of elementary or
neurone-like units or nodes that interlink together. The perceptron
has two inputs (x1 and x2) and one output (y). The output is based on
the inputs and is nearly always either +1, if the output is above a
certain threshold or -1 should the output fall below a certain
In Beat's article on Perceptrons he describes how
Rosenblat (1958) found that perceptrons were capable of learning
through feedback on trial and error tasks. When a correct response or
output is found the connections are strengthened. The connections
strength is decreased when a wrong response or output is given by the
perceptron. It can, therefore, learn 'AND' and 'INCLUSIVE OR'
INPUT (x1/x2) PROBLEM OUTPUT (y)
1 / 1 AND 0 = wrong
1 / 0 AND 0 = wrong
0 / 1 AND 0 = wrong
0 / 0 AND 1 = correct
1 / 1 INCLUSIVE OR 0 = wrong
1 / 0 INCLUSIVE OR 1 = correct
0 / 1 INCLUSIVE OR 1 = correct
0 / 0 INCLUSIVE OR 1 = correct
In the 'AND' condition, after trial and error with
feedback, it learns that when two '0' s are inputed this is the
correct response. In the 'OR' condition it can learn through trial
and error with feedback that it is the correct response when a '0' is
inputed from either input - x1 or x2.
However, the perceptron was criticised by Minsky because
it fails to be able to perform simple 'EXCLUSIVE OR' tests (or XOR).
It cannot learn the correct response to the problem when one input is
different from the other. e.g (it cannot perform this) :-
INPUT (x1/x2) PROBLEM OUTPUT
1 / 1 XOR 0 = wrong
1 / 0 XOR 1 = correct
0 / 1 XOR 1 = correct
0 / 0 XOR 0 = wrong
Up until this point, it had been thought that the
perceptron was a very good model for how the brain worked. Following
this finding by Minsky it could certainly not represent the brain.
Many of the day to day functionings of humans and animals is based
upon an 'EXCLUSIVE OR' problem. Later work did reveal, however, that
by adding extra 'hidden' layers the perceptron could solve the
'EXCLUSIVE OR' problem.
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