Activation functions are mathematical equations that determine the output of a neural network node, or neuron, based on its input. They introduce non-linearity into the model, allowing it to learn complex patterns in data. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh, each with unique properties that affect how the network learns. These functions help the network decide whether to activate a neuron, influencing the flow of information through the layers. By adjusting the weights and biases during training, activation functions play a crucial role in optimizing the performance of artificial neural networks.