As computer scientists like to say, "Garbage in, garbage out:” If the neural connectivity of Blue Brain is wrong, the simulation will be too. But let's not be overly critical. In the future, Markram could always incorporate information from connectomes into Blue Brain. Then wouldn't his simulation become truly realistic?
To answer this question, let's again consider the roundworm C. elegans. Its connectome is already known, unlike that of the neocortex. It may come as a surprise that only small parts of its nervous system have been simulated. These models have been helpful for understanding some simple behaviors, but they are piecemeal efforts. No one has come close to simulating the entire nervous system.
Unfortunately, we lack good models of C. elegans neurons. As I mentioned earlier, most of them don't even spike, so the weighted voting model isn't valid. To model the neurons, we'd have to measure from them, but this turns out to be more difficult for C. elegans than for mouse or even human neurons. We also lack information about C. elegans synapses. The connectome did not even specify whether the synapses were excitatory or inhibitory.
So Blue Brain lacks a connectome, while C. elegans lacks models of neuron types. Both elements are needed to simulate a brain or nervous system. Thus the earlier claim should be revised to say, "You are your connectome plus models of neuron types' (Let's assume that a connectome is defined to specify the type of each neuron.) But the models of neuron types are likely to contain much less information than the connectome, as most scientists agree that there are far fewer neuron types than neurons. In this sense, "You are your connectome" would remain a very good approximation. Furthermore, we assumed above that all neurons of one type behave in the same way in all normal brains, just as all polar bears hunt seals under normal circumstances. If we uploaded multiple people, all the simulations could share the same models of neuron types. The only information unique to a person would be his or her connectome.
It's worth noting that the balance of information content is quite different in C. elegans. Its three hundred neurons have been classified into about one hundred types, which is not that much smaller than the number of neurons. Essentially every neuron (along with its twin on the other side of the body) is its own type. Every neuron may end up requiring its own model, and the total information in these models might exceed that in the connectome. So "You are your connectome" would be a terrible approximation for a worm, even though it might be almost perfect for us.
To put it another way, the C. elegans nervous system is like a machine built from parts that are all unique. The individual workings of the parts are just as important as their organization. The opposite extreme would be a machine built from a single type of part. (You may be old enough to remember old-fashioned Lego sets, which contained only one type of Lego block.) The functionality of such a machine would depend almost entirely on the organization of its parts.
Electronic devices are close to this extreme, as they contain only a few types of parts, like resistors, capacitors, and transistors. That's why a radio's wiring diagram determines so much of its function. The parts list for the human brain is longer, so it will take many years of effort to model every neuron type in the human brain. But the parts list is still far shorter than the total number of parts. That's why the organization of the parts is so important, and why connectomes are more crucial for humans than for worms.
There's one more important aspect of connectomes to include in brain simulations: change. Without it, your uploaded self would not be able to store new memories or learn new skills. Markram and Modha have included reweighting using mathematical models of Hebbian synaptic plasticity. But it's also important to include reconnection, rewiring, and regeneration. In general, our models for the four R's are much less refined than those for electrical signals in neurons. It will be possible to improve them, but it will take many more years of research.