An activation function is a mathematical equation used in artificial neural networks to determine whether a neuron should be activated or not. It takes the input signal, processes it, and produces an output that helps the network learn complex patterns. Common types of activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit). Activation functions introduce non-linearity into the model, allowing it to learn from data more effectively. Without these functions, a neural network would behave like a linear regression model, limiting its ability to solve complex problems. They play a crucial role in the performance of deep learning models.