In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a model which can decide whether an input belongs to some specific class.
Neural Networks work the same way as the perceptron. Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
The evolution of the artificial neuron has progressed through several stages. The roots of which, are firmly grounded within neurological work done primarily by Santiago Ramon y Cajal and Sir Charles Scott Sherrington . Ramon y Cajal was a prominent figure in the exploration of the structure of nervous tissue and showed that, despite their ability to communicate with each other, neurons were physically separated from other neurons. With a greater understanding of the basic elements of the brain, efforts were made to describe how these basic neurons could result in overt behaviors, to which William James was a prominent theoretical contributor.
Working from the beginnings of neuroscience, Warren McCulloch and Walter Pitts in their 1943 paper, "A Logical Calculus of Ideas Immanent in Nervous Activity," contended that neurons with a binary threshold activation function were analogous to first order logic sentences.