The perceptron computes a weighted sum of inputs and applies a threshold to produce a binary decision. It can learn weights from labeled examples via simple update rules. Its limitations on non-linearly separable problems became a key historical lesson, motivating multi-layer networks and new training methods.

Despite its simplicity, the perceptron remains an important foundation for understanding modern linear models and the evolution toward deep learning.