What is Perceptron?

An early type of neural network, developed in the 1950s. It received great hype but was then shown to have limitations, suppressing interest in neural nets for years.

A neural network is an interconnected system of perceptrons, so it is safe to say perceptrons are the foundation of any neural network. Perceptrons can be viewed as building blocks in a single layer in a neural network, made up of four different parts:

  1. Input Values or One Input Layer
  2. Weights and Bias
  3. Net sum
  4. Activation function

A neural network, which is made up of perceptrons, can be perceived as a complex logical statement (neural network) made up of amazingly simple logical statements (perceptrons); of “AND” and “OR” statements. A statement can only be true or false, but never both at the same time. The goal of a perceptron is to determine from the input whether the feature it is recognizing is true, in other words, whether the output is going to be a 0 or 1. A complex statement is still a statement, and its output can only be either a 0 or 1.

Following the map of how a perceptron function is not very difficult: summing up the weighted inputs (product of each input from the previous layer multiplied by their weight), and adding a bias (value hidden in the circle), will produce a weighted net sum. The inputs can either come from the input layer or perceptrons in a previous layer. The weighted net sum is then applied to an activation function which then standardizes the value, producing an output of 0 or 1. This decision made by the perceptron is then passed onto the next layer for the next perceptron to use in their decision.


Experts call the perceptron algorithm a supervised classification because the computer is aided by the human classification of data points. It is also related to the development of “artificial neural networks,” where computing structures are based on the design of the human brain.

In perceptron, the algorithm takes a set of inputs and returns a set of outputs. These are often presented visually in charts for users. In many computer programming languages, a perceptron algorithm can take the form of a “for” or a “while” loop, where each input is processed to produce an output. The results show how these advanced types of algorithms learn from data — one of the defining characteristics of the perceptron is that it is not just an iterative set of processes, but an evolving process where the machine learns from data intake over time.

Together, these pieces make up a single perceptron in a layer of a neural network. These work together to classify or predict inputs successfully, bypassing on whether the feature it sees is present (1) or is not (0). The perceptrons are essentially messengers, passing on the ratio of features that correlate with the classification vs the total number of features that the classification has. For example, if 90% of those features exist then it is probably true that the input is the classification, rather than another input that only has 20% of the features of the classification. It is just as Helen Keller once said, “Alone we can do so little; together we can do so much.” and this is true for perceptrons all around.


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