This chapter presents the incorporation of fractional calculus into neural network activation functions to enhance performance. Traditional activation functions like Sigmoid, Tansig, and ReLU are crucial for introducing non-linearity in neural networks, but they have limitations, such as vanishing gradients, non-differentiability, and inactive neurons. Fractional calculus, which generalizes traditional derivatives to non-integer orders, offers a promising approach to overcoming these challenges. By incorporating fractional-order derivatives, this work develops and analyzes fractional-order activation functions, including Binary StepBinary step, Sigmoid, ReLU, and their variants. The book demonstrates that fractional-order activation functions improve adaptability, non-linearity, and predictive accuracy, particularly in complex systems with long-range dependencies.