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 hyperparameter to tune instead of one per layer. Just like the number of layers, you can try increasing the number of neurons gradually until the network starts overfitting. Another important question is: when would you want to add a max pooling layer rather than a convolutional layer with the same stride? The thing is that a max-pooling layer has no parameters at all, whereas a convolutional layer has quite a few.

Sometimes, adding a local response normalization layer that makes the neurons that most strongly activate inhibit neurons at the same location but in neighboring feature maps, encourages different feature maps to specialize and pushes them apart, forcing them to explore a wider range of features. It is typically used in the lower layers to have a larger pool of low-level features that the upper layers can build upon.