I am not a brain scientist myself. I think that knowing strength of connection as well as plasticity are both very important. In fact, I think that plasticity considerations are one of the main things that Penrose correctly addresses in The Emperor's New MInd. However, from a graph-theoretic and machine learning point of view, this does not strike me as intractable. Just yesterday I witnessed some new results in the connectomics project in which they can essentially (95+% accuracy on training data sets) reconstruct the wiring diagram of non-trivial volumes of a mouse brain (10^(-7) m^3) in less than 5 minutes using Gibbs sampling methods.

The current problem is getting enough resolution to make the accuracy much higher than 95%. No engineer involved with the project believes this will be difficult to accomplish in a ~10 year time span. The next detail will be imaging at that resolution in a video of a functioning brain and they are already discussing ways to achieve this too. I agree there are some real difficulties in understanding neurotransmitter functionality. But nothing suggests it will be prohibitively difficult for engineering in the next 25 years... at least that's my opinion if we are to just brute force directly store detailed videos of neural activities at the 250 nm resolution level. If we make any breakthrough whatsoever about abstraction and principles in neural physics that allow us to discard some of that brute force data resolution, it will only make the problem easier and less expensive.