If we add values such as (1,1) together, we might end up with numbers greater than 1, but we want our output to be between 0 and 1! To solve this, we can apply a function which limits our actual output to 0 or 1, even if the result of the neuron's math was not within the range. In more complicated neural networks, we apply a function (such as sigmoid) to the neuron, so that its value will be between 0 or 1 (such as 0.66), and then we pass on this value to the next neuron all the way until we need our output.