Artificial Intelligence
The Step Function: An Essential Tool in Neural Networks
The step function, or Heaviside function, is the simplest activation function in a neural network: it maps any input to a binary output, 0 or 1, depending on whether it crosses a fixed threshold. It was the core decision mechanism of Rosenblatt's perceptron in 1958, but its derivative is zero almost everywhere, so modern networks use sigmoid or ReLU instead.