The term "perceptron" was introduced in the 1950s to designate a simple mechanism to achieve "perception."

See video. Definition of perceptron

It is basically a Feedforward neural network, with 0 hidden layers, and Heaviside step function activation functions. They are a simpler version of Logistic regression.

https://www.wikiwand.com/en/Perceptron

As most older models, the original perceptrons, had threshold activation functions (see Hopfield network).

The hopes to build "seeing machines" vanished when Minsky and Papert published their book Perceptrons [1969], in which they rigorously demonstrated that perceptrons are quite limited in their ability to extract global features from local information. They could only implement linearly separable classification.

Simple storing problem: is a certain training set linearly separable?

Multilayer perceptron

However, the reaction was too drastic, because adding more layers to the Feedforward neural network (giving so-called "multilayer perceptrons"), avoided the issues pointed out by Minksy and Papert.

Multilayered perceptrons work essentially in a manner similar to the prevailing neurophysiological view. According to this view, on arrival at the cortex, sensory information is subject to a hierarchy of feature extractions. Further comparison with the Cortex, is done in the Corticonics book (pages 200-203)