Obviously, the number of neurons in the input and output layers is determined by the type of input and output your task requires. For example, if your dataset has the shape of 28 x 28 it should expect to have input neurons with size 784 and the output neurons should be equal to the number of classes to be predicted. As for the hidden layers, a common practice is to size them to form a funnel, with fewer and fewer neurons at each layer, the rationale being that many low-level features can coalesce into far fewer high-level features. However, this practice is not as common now, and you may simply use the same size for all hidden layers.
If there are four convolutional layers with 256 neurons, that's just one ...
