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The Central Nervous System

Simulating the Brain. Sure Thing.

My mention the other day of Japan’s Fifth Generation computer project prompted a reader to send along this link, which I thoroughly enjoyed. It concerns the Human Brain Project, currently being funded by the EU, and if you’re offended by procreating inanimate objects, you probably shouldn’t read it. But I feel the author’s frustration. The HBP is a ten-year effort, in its second year now, with a goal of doing brain and neuron simulation. Unfortunately, as the author correctly states, we have no reproducing way of knowing how to do that yet:

We can’t simulate the brain of C. Elegans, a very well studied roundworm (first animal to have its genome sequenced) in which every animal has exactly the same 302-neuron brain (out of 959 total cells) and we know the wiring diagram and we have tons of data on how the animal behaves, including how it behaves if you kill this neuron or that neuron. Pretty much whatever data you want, we can generate it. And yet we don’t know how this brain works. Simply put, data does not equal understanding. You might see a talk in which someone argues for some theory for a subnetwork of 6 or 8 neurons in this animal. Our state of understanding is that bad.

And things don’t get any better at the other levels that this project can be approached from:

. . .microscopically, we have no clue. It looks pretty random. We collect statistics (with great difficulty), and do tons of measurements (also with great difficulty), but not on humans. Even for well studied animals such as cats, rats, and mice, it’s anyone’s guess what the fine structure of the connectivity matrix is. As an overly simplistic comparison, imagine taking statistics on the connectivity of transistors in a Pentium chip and then trying to make your own chip based on those statistics. There’s just no way it’s gonna work.

That’s actually a very good analogy – and remember, as I always like to point out, that computer processing chips were designed by humans, and are thus far, far easier for humans to understand. Your chances of success with that statistical approach to a Pentium chip are not good at all, but they’re a lot better than the chances of it working on brain tissue. This brings to mind the famous “Can A Biologist Fix a Radio” paper (which I’ve also referenced here, here, here and here), which made almost the exact same point, imagining teams of biologists taking radios apart, one after the other, and cataloging the size, shape, and color of all the parts. It’s a telling point. (Where I part company with that paper, though, is its suggestion that there are clearly better ways to do such analysis that the biologists are ignoring – I think that the situation is just as bad as described, but I don’t see a clear remedy for it yet myself).

The (anonymous) author of the brain project rant finishes up thusly:

So, the next time you see a pretty 3D picture of many neurons being simulated, think “cargo cult brain”. That simulation isn’t gonna think any more than the cargo cult planes are gonna fly. The reason is the same in both cases: We have no clue about what principles allow the real machine to operate. We can only create pretty things that are superficially similar in the ways that we currently understand, which an enlightened being (who has some vague idea how the thing actually works) would just laugh at.

Exactly so. The same goes for too many other beautifully rendered simulation, but it’s especially true for the central nervous system. The brain is the darkest of the black boxes. That’s not a reason for despair, though. We’re going to learn an incredible amount as we start opening it up, but we’ve just barely started. Beware anyone who tells you otherwise.

13 comments on “Simulating the Brain. Sure Thing.”

  1. John Wayne says:

    My wife and I are very extroverted; as such, I get into a lot of ‘cocktail conversations’ with intelligent people who do not have science backgrounds. When people ask me questions about technology in the news cycles, the most common theme of my answer is how we (humans) don’t really know what is going on most of the time. I’ve been trying to figure out why the average American is so surprised by the limits of knowledge. Is it the way we teach science in schools? Why do people think we know what we’re doing? Where did that even come from? Is it as simple as, ‘you have to know a lot before you realize you don’t know anything?’

    1. John Dallman says:

      A lot of people are confused by the way that Hollywood portrays science (and also technology, engineering and medicine). The brief explanation is that Hollywood gets STEM rather more thoroughly wrong than they get the driving of cars (because, hey, most scriptwriters and producers can drive). That’s right. Action-movie exploding car scenes have more to do with actual driving than “Hollywood science” has with real science.

  2. Rule (of 5) Breaker says:

    My favorite thing to tell people is what a chemistry professor once told me, “We know almost nothing about almost everything.” I think that sums it up nicely.

  3. Isidore says:

    I think that too much exposure to Science Fiction has if not erased certainly made very fuzzy in the minds of many the distinction between what’s real and what’s speculation or wishful thinking or just fantasy. And such fiction is no longer confined to “Sci-Fi” movies and TV shows and books but can be found in much of mainstream entertainment. Of course many scientists are also to blame with overtly optimistic projections as to what the future holds.

  4. Hap says:

    I’m guessing that we gravitate to sources of knowledge outside our expertise (or things that look like knowledge) that make us feel like we actually know something because we don’t like uncertainty; if we have a choice, we choose sources that tell convincing and complete stories rather than ones that tell incomplete stories. Maybe we get confused and think that everything acts this way, and are surprised when it doesn’t.

  5. Jim says:

    It all reminds me of a quote I heard when beginning by graduate work: “If the brain were so simple that we could understand it, then we would be too simple to do so.”

  6. Nathaniel Mishkin says:

    WRT the amount of knowledge scientists do or don’t have, I do have to say (as a software engineer, not a “scientist” 🙂 that I’m regularly amazed (e.g., by articles in this very blog) at the level of detail about various biological processes that _is_ known. I mean, yow, how did someone work out all those details about how some molecule in some bit of DNA translates into some protein that inside a cell interacts with some other protein to achieve one or another bit of macro-behavior (e.g., a disease)? All in all, pretty impressive. I just guess that these explanations get only “macro” to some level. Not to, say, how a whole brain works.

    Somewhat apropos, this bit from a 2007 article from The New Yorker (

    “Paul and Pat Churchland believe that the mind-body problem will be solved not by philosophers but by neuroscientists, and that our present knowledge is so paltry that we would not understand the solution even if it were suddenly to present itself. “Suppose you’re a medieval physicist wondering about the burning of wood”, Pat likes to say in her classes. “You’re Albertus Magnus, let’s say. One night, a Martian comes down and whispers, ‘Hey, Albertus, the burning of wood is really rapid oxidation!’ What could he do? He knows no structural chemistry, he doesn’t know what oxygen is, he doesn’t know what an element is–he couldn’t make any sense of it. And if some fine night that same omniscient Martian came down and said, ‘Hey, Pat, consciousness is really alkjasdsdfjl!’ I would be similarly confused, because neuroscience is just not far enough along.”

    1. z says:

      Might be dup post. Still getting posting too fast errors.

      I think it’s more to do with how “science” is presented to the public.

      It is almost always introduced as discoveries or results- not the increase of knowledge (of any non-statistical confidence).
      As most of our medical results seem to be gained from taking blind putts and checking to see if anything entered one of the holes (that we can find and are looking at), we might know that A works and seems safe, but not necessarily why A works (or why not B).

      Over the years we have gotten quite a number of scientific discoveries and technologies/products that use them, but that doesn’t translate well into insight in how to build something from the ground up.

  7. Erebus says:

    Very true.
    And I can think of quite a few specific factors that the rant did not mention…

    -C.elegans has around 50 glial cells. Some of these are presumably involved in movement (GLR cells), others in modulating neuron maintenance, development, and activity. The GLR cells, in particular, appear to play an important role at the convergence point between the c.elegans CNS and its muscle cells. As far as I can tell, glial cells are not part of the OpenWorm model.

    -Researchers have identified over 1000 genes which code for neuropeptides in c.elegans, the vast majority of which have no known role or receptor. At the same time, there are quite a large number of orphan receptors with no known function. These, too, are apparently not part of the model. (One might ask: How could they be part of the model, anyway?)

    -C.elegans is extremely sensitive to small molecule cues. It can detect & respond to ascarosides (nematode hormones/pheromones) like daumone at femtomolar concentrations. (!!) It also secretes ascarosides — and presumably other, structurally different small molecule hormones/pheromones — when exposed to certain environmental triggers, e.g. overcrowding or food shortages. The extent and importance of small molecule signalling in elegans CNS activity is largely unknown. I don’t know if modeling small molecule signalling is part of the OpenWorm model — but as so much of it is entirely unknown, any current model will not accurately reflect biological reality.

    …I must be missing at least half a dozen other factors. I’m no elegans specialist, but it seems to me that just making a wiring diagram of neurons is not going to do the trick. Ever. There’s simply a lot more to it than that. I’m reminded of this quote from an interesting Chomsky interview.

    “So if we take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don’t stop to ask if the wiring diagram is the right level of abstraction — maybe it’s not, so what is your view on that?”

    Chomsky: “Well, there are much simpler questions. Like here at MIT, there’s been an interdisciplinary program on the nematode C. elegans for decades, and as far as I understand, even with this miniscule animal, where you know the wiring diagram, I think there’s 800 neurons or something …”

    “I think 300..”

    Chomsky: “…Still, you can’t predict what the thing [C. elegans nematode] is going to do. Maybe because you’re looking in the wrong place.”

    1. Shion Arita says:

      I agree that there’s a lot more to it than just the connectivity: for one, I’ve read some stuff saying that a big part of it is the relative magnitudes of the connections.

      That being said, I don’t see how we’re going to figure it out without mapping the connections. That is certainly a very important and necessary thing to learn. It’s not the whole answer by a long shot, but it is definitely something we do have to come to understand along the way.

      So globally, I think that the human brain project may be worthwhile, even though it’s unlikely to reach its goals. Unless they’re going about things completely stupidly, they have a good chance of learning some very important things.

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