Artificial Neural Network (ANN)
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The formal name for a neural network: a computing system of connected artificial neurons, loosely modeled on how the brain processes information.
What is an Artificial Neural Network?
An artificial neural network (ANN) is a computing system of connected artificial neurons that learns patterns from data, loosely modeled on the neurons of a biological brain. It is the same thing as a neural network; the shorter name is simply more common.
This page covers the full term and its biological roots. For the structure, types, and workings of these networks, see the main entry: neural network.
How an Artificial Neural Network Works
The design borrows one idea from biology. A brain neuron collects signals from other neurons and fires only when the combined signal is strong enough; an artificial neuron does the arithmetic version, summing weighted inputs and passing the total through an activation function that decides what to send onward.
Connect thousands of these units in layers and the system can learn: training adjusts the connection weights so that useful signals get amplified and noise gets suppressed. The word “artificial” marks the boundary of the analogy. An ANN is not a brain simulation – real neurons are vastly more complicated, and the resemblance stops at the wiring diagram.
The qualifier mattered more when the field needed to distinguish its models from work in neuroscience. Today almost everyone drops it and says “neural network.”
Example of an Artificial Neural Network
The first working ANN was the perceptron, demonstrated by Frank Rosenblatt in 1958. It was a single layer of artificial neurons, built partly in hardware, that learned to classify simple visual patterns.
The learning loop already had the modern shape. The perceptron made a guess, compared it to the correct label, and adjusted its weights after each mistake. Its limits (a single layer cannot learn certain patterns) later pushed researchers toward the multi-layer networks that deep learning is built on.
Related AI terms: Neural Network · Deep Learning · Weights · Activation Function
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