A 1958 artificial neuron that adjusts its own weights when it gets an example wrong.
The perceptron is an artificial neuron introduced in 1958 by American psychologist Frank Rosenblatt. It multiplies each incoming signal by a weight, adds them up, and outputs 1 if the total clears a threshold, 0 if not. The new part: whenever it got an example wrong, it adjusted its own weights a little.
A single-layer perceptron has clear limits. In 1969 Marvin Minsky and Seymour Papert proved mathematically that it can't solve problems such as XOR, which can't be split by one straight line. Neural network research lost momentum for years, until stacking layers and training them with backpropagation caught on.
Think of it like this
Think of a scoring sheet that adds up a few scores and says "pass" if the total clears a cutoff. A perceptron is a scoring sheet that changes how much each score counts whenever it makes a wrong call. But one cutoff line can only capture simple rules.
For example
XOR, "output 1 only when exactly one of two switches is on", can't be solved by a single-layer perceptron.
Records about this
Source-checked moments from AI history.
Pinned down the limits mathematically, triggering a slump in neural network research that lasted 10 years or more
The first learning procedure to update weights from errors. Psychological Review 65(6) 386–408