Activation functions are decision making units of neural networks. They calculates net output of a neural node. Herein, heaviside step function is one of the most common activation function in neural networks. The function produces binary output. That is the reason why it also called as binary step function. The function produces 1 (or true) when input passes threshold limit whereas it produces 0 (or false) when input does not pass threshold. That’s why, they are very useful for binary classification studies.
Human reflexes act based on the same principle. A person will withdraw his hand when he touces on a hot surface. Because his sensory neuron detects high temperature and fires. Passing threshold triggers to respond and withdrawal reflex action is taken. You might think true output causing fire action.