Improving Connectomics by an Order of Magnitude
For a semester project in grad school, I once interviewed Ken Hayworth while he was at the Lichtman Lab at Harvard working on FIB-SEM tissue slicing and imaging technology to create extremely high-res imagery of slices of mouse brains.<p>The most interesting idea he discussed with me was the idea that getting to the point of pragmatic whole-brain imaging for purposes of connectomics-like neural reconstruction (and perhaps actual brain emulation) looked like it would be so hard that he expected there would be a funding model similar to the way astronomy labs handle expensive telescope time.<p>With telescope time, it&#x27;s not economical for any specific lab to totally own the entire observatory, as the equipment is really expensive to create and maintain, and any given project may only utilize the equipment for a tiny fraction of the time. So instead, you get a model similar to cloud computing: some consortium will build and operate the infrastructure and different labs would bid on actual scope time to dedicate some devices for their specific research needs. The more urgent or promising the research project is, the more they might be willing to pay to get priority scope time, and this would drive what types of astronomical discoveries are made.<p>With connectomics &#x2F; neural reconstruction, it could be similar. Someone might propose a certain section of the brain to map out because of a promising connection to a certain disease or development in cognitive science or an understanding of behavioral patterns. And over time we would get some piecemeal, patchwork &quot;planetarium&quot; map of an imaged brain where we have highly resolved detail about some regions, but have almost not information about others.<p>Incidentally, the project for which I conducted this interview was a semester project to try to pin down tight estimates on how long it would take using known technology to fully image a whole brain. There are several different physical techniques, but the FIB-SEM and ultramicrotome stuff was by far the most promising and most efficient.<p>To give some idea of the time scale involved in imaging a human brain, putting aside data storage, retrieval costs and the setup and preprocessing time to prepare the tissue into small enough column slides to be operated on by the particular device (FIB-SEM), a single 20 by 20 by 20 micron tissue cube can be imaged into 5 by 5 by 10 nm voxels in roughly 2 hours, assuming a 10 MHz optimized FIB-SEM device (which is a reasonable extrapolation from current technology).<p>It would take 30 years for one such FIB-SEM device to image a single cubic millimeter volume of tissue. The human brain is roughly 10^6 mm^3 in volume (it&#x27;s between 10^5 and 10^6, just using an upper bound here), and so even if we parallelized a set of 100 such FIB-SEM devices and set them running continuously for 10 years, we would only have imaged 0.003% of the human brain at this resolution!<p>I&#x27;m sure other technological advances can speed this up, but it is really hard to predict by how much.<p>In my thinking, this is one of the main remaining reasons why someone might feel that strong AI is unlikely to be developed via first emulating human brains in software (e.g. like Robin Hanson&#x27;s primary argument), compared with being developed through algorithmic research in machine learning and AI (the stuff that Friendly AI researchers are often more directly concerned about).