In equations 2.1-2.4, y refers to unit output regardless of whether the unit is sending or receiving, and x refers to net input to a receiving unit regardless of where that unit resides. This labeling consistency can make it easier to understand what is happening in the transmission between any two layers. Alternatively, when considering transmission across several network layers, it can be clearer to reserve different labels for different layers of units.
In a number of places in the book, I use the term generative to describe cascade-correlation networks that build their own topology as they learn. This made sense because the learning algorithm generates its own topological structure by recruiting new hidden units as needed in learning. However, the term generative is now also used to signify neural networks that generate their own input patterns, usually by having top-down weights that run from outputs to hiddens to inputs. To avoid this dual use of the term generative, in more recent publications I refer to cascade-correlation and related networks as constructive learners.