Perceptrons and their applications
A perceptron can be understood as anything that takes multiple inputs and produces one output. It is the simplest form of a neural network. The perceptron was proposed by Frank Rosenblatt in 1958 as an entity with an input and output layer and a learning rule based on minimizing the error. This learning function called error backpropagation alters connective weights (synapses) based on the actual output of the network with respect to a given input, as the difference between the actual output and the desired output.
The enthusiasm was enormous and the cybernetics industry was born. But later, scientists Marvin Minsky and Seymour Papert (1969) demonstrated the limits of the perceptron. Indeed, a perceptron is able to recognize, after a suitable training, only linearly separable functions. For example, the XOR logic function cannot be implemented by a perceptron.
The following image showns Frank Rosenblatt at the Cornell Aeronautical Laboratory (1957-1959),...
