I've been working on understanding how convolutional neural networks by building my own implementation and trying to run a small network. So far I think I've gotten a good handle on the feed-forward through the network. I also think I have a good grasp on how to backpropagate from the fully-connected layers to the pooling layers.
Unfortunately I've been having some issues working out the backpropagation from a convolutional layer up to a pooling layer. Since the output of the pooling layer is of a different dimension than the output of the convolution layer, I'm guessing that the backprop is a full convolution of the convolutional layer's weights with the errors. Is this the correct calculation to do?