Well, if I'm doing morningstar rhetoric, I'd best get my game face on.

First, in the paper below, their estimate of the information density of the brain is somewhat wrong. What you actually need is the number of neurons in the brain (10^11), squared, times two bytes for half-precision floating point storage of the strength of the synaptic connection, plus another two bytes to specify the class of neuron, times two, to add fudge factor for white matter doing whatever it is that white matter does, which all works out to 6.4 * 10^23.

Now that we've actually set up the problem, let's see if we can find a way it might still be possible. First, let's do the obvious thing and forget about the brain stem. Provided you've got enough other human brain templates on ice, that can probably be specified in a negligable number of terrabytes, to close enough accuracy that the cerebral cortex won't miss it. What we really care about are the 2 10^10 neurons in the cerebral cortex. Which brings out overall data usage down to 1 10^22. Not a huge improvement, I grant you, but we're working. Second, remember that our initial estimate was for the ammount of RAM needed, not the entropy. We're storing slots in a two dimensional array for each neuron to connect to every other neuron, which will never happen. Assuming 5000 synapses per neuron, that means that 5000 / ( 2* 10^9) of our dataset is going to be zeroes. Let's apply run-length encoding for zeroes only, and we should see a reduction by a factor of a hundred thousand, conservatively. That brings it down to 10^17 bits, or 11 petabytes.

Now let's consider that the vast majority of connectomes will never occur inside a human brain. If you generate a random connectome from radio noise and simulate it as an upload, even within the constraints already specified, the result will not be able to walk, talk, reason, or breathe. This doesn't happen to neurologically healthy adults, so we can deduce that human upbringings and neural topology tend to guide us into a relatively narrow section of connectome space. In fact, I suspect that there's a good chance that uploading this way would be a process of starting with a generic human template, and tweaking it. Either way, if you took a thousand of those randomly generated minds, I would be very surprised if any of them was anything resembling a human being, so we can probably shave another three orders of magnitude off the number of bits required. That's 10^15 bits of data, or 113 terrabytes. Not too shabby.

Based on this, and assuming that nights are a wash, and we get no data, in order to specify all those bits in ten years, we would need to capture something like 6.4 megabits a second of entropy, or a little less than a megabyte. This seems a little high. However, there are other tricks you could use to boost the gain. For example, if you have a large enough database of human brain images, you could meaningfully fill in gaps using statistics. For example: if, of the ten thousand people with these eight specific synaptic connections, 99% also have a particular ninth one, it'd be foolish not to include it.

In short, it seems somewhat infeasible, but not strictly impossible. You could augment by monitoring spinal activity, implanting electrodes under your scalp to directly record data on brain region activation, and by the future use of statistical analytics.

Now, actually deducing the states of the brain based on its output, as you said, might be difficult or impossible enough to put an end to the whole game before it starts. Still, it might actually work.