Psychologist [Frank Rosenblatt](https://en.wikipedia.org/wiki/Frank_Rosenblatt) built the first [neural networks](https://en.wikipedia.org/wiki/Feedforward_neural_network#Single-layer_perceptron) in [1957/8](https://en.wikipedia.org/wiki/Perceptron#History). Today it is trivial to build a neural network using software, but Rosenblatt built a neural network using analogue hardware. 

The perceptron could tell the difference between triangles, circles and and squares, or between different letters of the alphabet. Most critically, it could *get better at doing so* as it was given feedback on whether it was correct or incorrect about previous predictions. In this sense, it could 'learn'.

### A hardware implementation of a neural network

The input to the perceptron was an array of 20 x 20 grid of light sensitive resistors

[![enter image description here][1]][1] 

And the weights/bias values of the primitive neural network were stored in racks of cylindrical objects each consisting of an electrical motor and potentiometer (rotary resistor).

[![enter image description here][2]][2]


  [1]: https://i.sstatic.net/2zeeS.png
  [2]: https://i.sstatic.net/IjzLv.png