An activation function is a crucial non-linear function applied to the output of each neuron in a neural network, including those in hidden layers and often the output layer. Its primary purpose is to introduce non-linearity into the network, enabling it to learn and model complex, non-linear relationships within the data. Without activation functions, a neural network, regardless of its depth, would effectively behave like a single linear model, limiting its capacity to learn intricate patterns. Common activation functions include the Sigmoid (for binary classification output), Tanh, ReLU (Rectified Linear Unit), and its variants (e.g., Leaky ReLU, ELU). Each function has distinct properties regarding its range, differentiability, and computational efficiency, influencing the network's training dynamics and overall performance by determining whether a neuron should be "activated" or "fired" based on the weighted sum of its inputs.