Okay Drich, here's your citation, which I managed to find from an online preview of a chapter in the book, on Google books. There is not a full e-version of this book anywhere that I can find so I was lucky to find that. I'm having to type this thing out because the preview is in PDF form. The bolding and italics is theirs, but I'm adding underlining to point out what I've been talking about.
And this comes from "Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain (Bradford Books)", by Randall C. O'Reilly and Yuko Munakata, 2000, page 113.
You can read the chapter it comes from, and maybe others, same place I've quoted it from... (unless you also want to claim that this link doesn't work):
https://books.google.co.uk/books?id=BLf3...&q&f=false
Quote:Bidirectional (a.k.a. recurrent or interactive) connectivity is predominant in the cortex, and has several important functional properties not found in simple unidirectional connectivity. We emphasise the symmetric case (i.e. where both directions have the same weight value), which is relatively simple to understand compared to the asymmetric case. First, it is capable of performing unidirectional-like transformations, but in both directions, which enables top-down processing similar to mental imagery. It can also propagate information laterally among units within a layer, which leads to pattern completion when a partial input pattern is presented to the network and the excitatory connections activate the missing pieces of the pattern. Bidirectional activation propagation typically leads to the amplification of activity patterns over time due to mutual excitation between neurons. There are several other important subtypes of amplifying effects due to to bidirectional excitatory connections, including: mutual support, top-down support or biasing, and bootstrapping. Many of these phenomena are described under the general term of attractor dynamics, because the network appears to be attracted to a particular activation state.
And this comes from "Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain (Bradford Books)", by Randall C. O'Reilly and Yuko Munakata, 2000, page 113.
You can read the chapter it comes from, and maybe others, same place I've quoted it from... (unless you also want to claim that this link doesn't work):
https://books.google.co.uk/books?id=BLf3...&q&f=false
