[openasocket]

You don't need a single memory machine to simulate the human brain though. Because neurons have a clear spatial structure, you can break the problem up into a mesh, give each piece of the mesh to a different machine, and give them some interconnect so they can broadcast relevant portions of their state to neighboring machines. You don't need low latency direct memory access for every node to all of the memory. Look at something like the https://en.wikipedia.org/wiki/Tianhe-2 which has about a petabyte of RAM

EDIT: I think you're also not considering the ample opportunities to compress this data set. For instance, you can represent the connections between neurons with a sparse adjacency matrix, and there is a whole body of literature devoted to coming up with small representations of that kind of data, while still being able to perform operations efficiently. For the genome variance, keep in mind the variance is going to be absolutely tiny in comparison to the whole genome, so you can just store the genome of one of the neurons and then the diffs between it and all the other neurons or something.