On a high level, the inspiration for neural networks comes from the biological neurons in the human brain, which communicate through electrical signals. In 1943, Warren McCulloch and Walter Pitts proposed the first mathematical model of a neuron, showing that simple units could perform computation of a function. Later, in 1958, Frank Rosenblatt introduced the perceptron, an algorithm designed to perform pattern recognition. The perceptron is the historical ancestor of today’s networks: essentially a linear model with a constrained output. In the following section, we will dive into how neural networks borrow inspiration from the human brains to make decisions and recognize patterns.