I just read Bishop book's Pattern Recognition and Machine Learning, I read the chapter 5.3 about the backpropagation, it said in his book that, In general feed-forward network, each unit computes a weighted sum of its inputs of the form `$$\a_j=\sum\limits_{i}w_{ji}z_i$$@`

Then the book say that the sum in above equation transformed by non linear activation function `$h(.)$` to give the activation `$z_j$` of unit `$j$` in the form `$$z_j=h(a_j)$$`.

I think the notation is somehow awkward, suppose I want to compute `$a_2$`, then
`$$a_2=w_{21}z_1+w_{2,2}z_2+\dots$$`

Then it means that `$$a_2=w_{21}z_1+w_{2,2}h(a_2)+\dots$$` it means that the neuron `$a_2$` connected to itself?

Please correct me if I wrong.