# Perceptron as Line Classifier

**Track:** Physics, Motion & Emergence — Creative Coding — the existing 50
**Framework / surface:** p5.js
**Level:** Advanced
**Prerequisites:** Vectors: Magnitude & Direction, Trigonometry with Sin & Cos

**In one line:** Weighted inputs that output yes or no — the simplest neuron.

## Theory, aesthetics & inspiration

A perceptron is the simplest learning unit: it multiplies each input by a weight, sums the results, and fires a yes or no depending on whether the total clears a threshold. Frank Rosenblatt introduced it in 1958, building on the earlier McCulloch–Pitts neuron, and showed it could adjust its own weights from labeled examples—learning, geometrically, to place a dividing line that separates one class from another. The aesthetic is conceptual rather than visual: a boundary drifting until it snaps into the right orientation. Its limits are equally instructive—Minsky and Papert noted in 1969 that a single perceptron cannot solve problems, like XOR, that no straight line can divide.