Now, it’s one thing to be able to simulate a brain. It’s another to actually have the exact wiring map of an individual’s brain to actually simulate. How do we build such a map? Even the best non-invasive brain scanners around – a high-end functional MRI machine, for example – have a minimum resolution of around ten thousand neurons or ten million synapses. They simply can’t see detail beyond this level. And while resolution is improving, it’s improving at a glacial pace. There’s no indication of a being able to non-invasively image a human brain down to the individual synapse level any time in the next century (or even the next few centuries at the current pace of progress in this field).
There are, however, ways to destructively image a brain at that resolution. At Harvard, my friend Kenneth Hayworth created a machine that uses a scanning electron microscope to produce an extremely high resolution map of a brain. When I last saw him, he had a poster on the wall of his lab showing a printout of one of his brain scans. On that poster, a single neuron was magnified to the point that it was roughly two feet wide, and individual synapses connecting neurons could be clearly seen. Ken’s map is sufficiently detailed that we could use it to draw a complete wiring diagram of a specific person’s brain.
Unfortunately, doing so is guaranteed to be fatal.
The system Ken showed “plastinates” a piece of a brain by replacing the blood with a plastic that stiffens the surrounding tissue. He then makes slices of that brain tissue that are thirty nanometers thick, or about one hundred thousand times thinner than a human hair. The scanning electron microscope then images these slices as pixels that are five nanometers on a side. But of course, what’s left afterwards isn’t a working brain – it’s millions of incredibly thin slices of brain tissue. Ken’s newest system, which he’s built at the Howard Hughes Medical Institute goes even farther, using an ion beam to ablate away five nanometer thick layers of brain tissue at a time. That produces scans that are of fantastic resolution in all directions, but leaves behind no brain tissue to speak of.
So the only way we see to “upload” is for the flesh to die. Well, perhaps that is no great concern if, for instance, you’re already dying, or if you’ve just died but technicians have reached your brain in time to prevent the decomposition that would destroy its structure.
In any case, the uploaded brain, now alive as a piece of software, will go on, and will remember being “you”. And unlike a flesh-and-blood brain it can be backed up, copied, sped up as faster hardware comes along, and so on. Immortality is at hand, and with it, a life of continuous upgrades.
Unless, of course, the simulation isn’t quite right.
How detailed does a simulation of a brain need to be in order to give rise to a healthy, functional consciousness? The answer is that we don’t really know. We can guess. But at almost any level we guess, we find that there’s a bit more detail just below that level that might be important, or not.
For instance, the IBM Blue Brain simulation uses neurons that accumulate inputs from other neurons and which then “fire”, like real neurons, to pass signals on down the line. But those neurons lack many features of actual flesh and blood neurons. They don’t have real receptors that neurotransmitter molecules (the serotonin, dopamine, opiates, and so on that I talk about though the book) can dock to. Perhaps it’s not important for the simulation to be that detailed. But consider: all sorts of drugs, from pain killers, to alcohol, to antidepressants, to recreational drugs work by docking (imperfectly, and differently from the body’s own neurotransmitters) to those receptors. Can your simulation take an anti-depressant? Can your simulation become intoxicated from a virtual glass of wine? Does it become more awake from virtual caffeine? If not, does that give one pause?
Or consider another reason to believe that individual neurons are more complex than we believe. The IBM Blue Gene neurons are fairly simple in their mathematical function. They take in inputs and produce outputs. But an amoeba, which is both smaller and less complex than a human neuron, can do far more. Amoebae hunt. Amoebae remember the places they’ve found food. Amoebae choose which direction to propel themselves with their flagella. All of those suggest that amoebae do far more information processing than the simulated neurons used in current research.
If a single celled micro-organism is more complex than our simulations of neurons, that makes me suspect that our simulations aren’t yet right.
Or, finally, consider three more discoveries we’ve made in recent years about how the brain works, none of which are included in current brain simulations. First, there are glial cells. Glial cells outnumber neurons in the human brain. And traditionally we’ve thought of them as “support” cells that just help keep neurons running. But new research has shown that they’re also important for cognition. Yet the Blue Gene simulation contains none. Second, very recent work has shown that, sometimes, neurons that don’t have any synapses connecting them can actually communicate. The electrical activity of one neuron can cause a nearby neuron to fire (or not fire) just by affecting an electric field, and without any release of neurotransmitters between them. This too is not included in the Blue Brain model. Third, and finally, other research has shown that the overall electrical activity of the brain also affects the firing behavior of individual neurons by changing the brain’s electrical field. Again, this isn’t included in any brain models today.
I’m not trying to knock down the idea of uploading human brains here. I fully believe that uploading is possible. And it’s quite possible that every one of the problems I’ve raised will turn out to be unimportant. We can simulate bridges and cars and buildings quite accurately without simulating every single molecule inside them. The same may be true of the brain.
Even so, we’re unlikely to know that for certain until we try. And it’s quite likely that early uploads, like Su-Yong Shu, will be missing some key piece or have some other inaccuracy in their simulation that will cause them to behave not-quite-right. Perhaps it’ll manifest as a creeping insanity, as in Su-Yong’s case. Perhaps it will be too subtle to notice. Or perhaps it will show up in some other way entirely.
Finally, I’ve written about more than neuroscience in Crux. And in particular I’ve written about the impact of climate change. Zoe, the storm that hits the eastern seaboard at the end of Crux, is a piece of fiction, but a plausible one. When I wrote the scenes with Zoe, in late 2012, superstorm Sandy had not yet appeared. (Imagine my surprise, when, a few weeks after I wrote about it, a late season storm struck the eastern seaboard and impacted a presidential election!) Since then, most of the public has learned that hurricanes can indeed arrive in early November, and that they feed off the power of warm surface waters in the Atlantic. It’s impossible to say that a changing climate caused a particular storm. But what it is possible to say is that the general warming we’ve experienced has made storms like Zoe (and Sandy) many times more likely to occur. As the planet continues to warm, we’ll see far more of them.