A perceptron is the simplest neural unit. It multiplies each input by a weight, sums the results, adds a bias, then passes that total through a step threshold. Output is one or zero. Training nudges weights whenever a prediction is wrong, moving the boundary toward the mistaken example.
What a single layer can do is draw one straight boundary through the input space. That handles linearly separable problems like AND and OR. It cannot solve XOR, because no single line separates those four points.
This limit is why depth exists. Stacking units with a hidden layer between them lets the network bend the boundary into arbitrary shapes. The cost is that the simple perceptron update no longer works, so you need gradient-based training instead.
Rewriting in plainer words…
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
