# Tiny Neural Network

**Track:** Physics, Motion & Emergence — Creative Coding — the existing 50
**Framework / surface:** p5.js
**Level:** Advanced
**Prerequisites:** Perceptron as Line Classifier, Arrays of Objects

**In one line:** Perceptrons stacked in layers.

## Theory, aesthetics & inspiration

Stack perceptrons into layers and connect them, and the single dividing line becomes a surface that can bend: a network of weighted units, each feeding the next, capable of carving regions no straight boundary could. The hidden layer is the breakthrough—intermediate units that learn features—and training works by propagating error backward through the connections, the backpropagation algorithm popularized by Rumelhart, Hinton, and Williams in 1986, which answered the very limitation Minsky and Papert had exposed. The aesthetic is emergent capability from uniform parts: nothing in a single neuron anticipates the whole. This small architecture is the conceptual seed of every deep network that followed.