Activation functions are crucial for neural network performance, especially in dense layers. Options include ReLU, sigmoid, tanh, ELU, and SELU, each affecting convergence and accuracy differently. Softmax is ideal for multi-class tasks. Custom functions can enhance model flexibility. The choice should align with dataset characteristics and architecture.