This does not seem to me to change predictions much. There is a huge range of hypotheses of how much detail is needed and this is only relevant to a small subset of them. One hypothesis is that the activity of individual neurons is unimportant noise which produces simpler behavior at the level of cortical columns, which can modeled as black boxes. This does not provide evidence against such coarse theories. It does provide evidence against neuron-level emulation, pushing the posterior from neurons to sub-neuron hypotheses. But the prior on emulating neurons was already pretty low: no one believes that neurons are "simple little machines."
If you think that this behavior is important, if you think that, say, Michael Hines's Neuron software is a serious attempt to emulate neurons at the coarsest plausible sub-neuron level, and if the software fails to generate this behavior, the conjunction is evidence is evidence that the software works on too coarse a level and a finer level is necessary to emulate neurons. But I am skeptical about all three clauses!
Fair enough-- I don't think I've seen the sophisticated work that's been done.
Does neurogenesis add significant complexity?
Neurons aren't simple little machines, axons talk to each other.
The original article (paywall).
Assuming this is all true, how does it affect the feasibility of uploading? Anyone want to bet on whether things are even more complicated than the current discoveries?
ETA: It seems unlikely to me that you have to simulate every atom to upload a person, and more unlikely that it's enough to view neurons as binary switches. Is there any good way to think about how much abstraction you can get away with in uploading?
Yes, I know it's a vague standard. I'm not sure how good an upload needs to be. How good would be good enough for you?