Most of the sensible people seem to be saying that the relevant neural features can be observed at a 5nm x 5nm x 5nm spatial resolution, if supplemented with some gross immunostaining to record specific gene expressions and chemical concentrations. We already have SEM setups that can scan vitrified tissue at around that resolution, they’re just (several) orders of magnitude too slow. Outfitting them to do immunostaining and optical scanning would be relatively trivial. Since multi-beam SEMS are expected to dramatically increase the scan rate in the next couple of years, and since you could get excellent economies of scale for scanning on parallel machines, I do not expect the scanners themselves to be the bottleneck technology.
The other possible bottleneck is the actual neuroscience, since we’ve got a number of blind spots in the details of how large-scale neural machinery operates. We don’t know all the factors we would need to stain for, we don’t know all of the details of how synaptic morphology correlates with statistical behavior, and we don’t know how much detail we need in our neural models to preserve the integrity of the whole (though we have some solid guesses). We also do not, to the best of my knowledge, have reliable computational models of glial cells at this point. There are also a few factors of questionable importance, like passive neurotransmitter diffusion and electrical induction that need further study to decide how (if at all) to account for them in our models. However, progress in this area is very rapid. The Blue Brain project alone has made extremely strong progress in just a few years. I would be surprised if it took more than fifteen years to solve the remaining open questions.
Large scale image processing and data analytics, for parsing the scan images, is a sufficiently mature science that it’s not my primary point of concern. What could really screw it up is if Moore’s law craps out in ten years like Gordon Moore has predicted, and none of the replacement technologies are advanced enough to pick up the slack.
Maybe, but scanning a vitrified brain with such a high resolution that a copy would feel more or less like the same person might take a bit longer.
Most of the sensible people seem to be saying that the relevant neural features can be observed at a 5nm x 5nm x 5nm spatial resolution, if supplemented with some gross immunostaining to record specific gene expressions and chemical concentrations. We already have SEM setups that can scan vitrified tissue at around that resolution, they’re just (several) orders of magnitude too slow. Outfitting them to do immunostaining and optical scanning would be relatively trivial. Since multi-beam SEMS are expected to dramatically increase the scan rate in the next couple of years, and since you could get excellent economies of scale for scanning on parallel machines, I do not expect the scanners themselves to be the bottleneck technology.
The other possible bottleneck is the actual neuroscience, since we’ve got a number of blind spots in the details of how large-scale neural machinery operates. We don’t know all the factors we would need to stain for, we don’t know all of the details of how synaptic morphology correlates with statistical behavior, and we don’t know how much detail we need in our neural models to preserve the integrity of the whole (though we have some solid guesses). We also do not, to the best of my knowledge, have reliable computational models of glial cells at this point. There are also a few factors of questionable importance, like passive neurotransmitter diffusion and electrical induction that need further study to decide how (if at all) to account for them in our models. However, progress in this area is very rapid. The Blue Brain project alone has made extremely strong progress in just a few years. I would be surprised if it took more than fifteen years to solve the remaining open questions.
Large scale image processing and data analytics, for parsing the scan images, is a sufficiently mature science that it’s not my primary point of concern. What could really screw it up is if Moore’s law craps out in ten years like Gordon Moore has predicted, and none of the replacement technologies are advanced enough to pick up the slack.