Are connectomes the future?  Although the derivation of the word “connectome” makes no sense – as I understand it the “-ome” bit is copied from “genome”, which in turn was copied from “chromosome”, losing a crucial ‘s’ in the process* – it was coined simultaneously but separately by Olaf Sporns and Patric Hagmann, so it is clearly a word whose time to emerge has come.

It means a functionally coherent set of neural connections, or a map of the same. This may be the entire set of connections in a brain or a nervous system, but it may also be a smaller set which link and work together.  There is quite a lot going on in this respect: the Human Connectome Project is preparing to move into its second, data-gathering phase; there’s also the (more modest or perhaps more realistic) Mouse Connectome Project.  One complete connectome, that for the worm Caenorhabditis elegans, already exists (in fact I think it existed before the word “connectome”) and is often mentioned. The Open Connectome Project has a great deal of information about this and much besides.

The idea of the connectome was given a new twist by Sebastian Seung in his TED talk “I Am My Connectome”, and he has now published a book called (guess what) Connectome. In that he gently and thoughtfully backs away a bit from the unqualified claim that personal identity is situated in the connectome of the whole brain. It’s a useful book which falls into three parts:  a lucid exposition of the neural structure of the brain, some discussion and proposals on connectomic investigation; and some more fanciful speculation, examined seriously but without losing touch with common sense. Seung touches briefly on the spat between Henry Markram and Dharmendra Modha: Markram’s Blue Brain project, you may recall, aims to simulate an entire brain, and he was infuriated by Modha’s claim to have simulated a cat brain on the basis of a far less detailed approach (Markram’s project seeks to model the complex behaviour of real neurons: Modha’s treated them as standard nodes). Seung is quite supportive of these simulations, but I thought his discussion of the very large difficulties involved and the simplifications inherent even in Markram’s scrupulous approach was implicitly devastating.

What should we make of all this connectome stuff? In practical terms the emergence of the term “connectome” adds nothing much to our conceptual armoury: we could and did talk about neural networks anyway. It’s more that it represents a new surge of confidence that neurological approaches can shoulder aside the psychologists, the programmers, and the philosophers and finally get the study of the human mind moving forward on a scientific basis. To a large extent this confidence springs from technical advances which mean it has finally begun to seem reasonable to talk about drawing up a detailed wiring diagram of sets of neurons.

Curiously though, the term also betrays an unexpected lack of confidence. The deliberate choice of a word which resembles one from genetics and recalls the Human Genome Project clearly indicates an envy of that successful field and a desire to emulate it. This is not the way thrusting, successful fields of study behave; the connectonauts seem to be embarking on their explorations without having shed that slightly resentful feeling of being the junior cousin. Perhaps it’s just that like most of us they are slightly frightened of Richard Dawkins. However, it could also be a well-founded sense that they are getting into something which is likely to turn out complicated in ways that no-one could have foreseen.

One potential source of difficulty lies in the fact that looking for connectomes tends to imply a commitment to modularity.  The modularity (or otherwise) of mind has been extensively discussed by philosophers and psychologists, and neurologists have come up with pretty strong evidence that localisation of many functions is a salient feature of the brain: but there is a risk that the modules devised by evolution don’t match the ones we expect to find, and hence are difficult to recognise or interpret; and worse, it’s quite possible that important functions are not modularised at all, but carried out by heterogeneous and variable sets of neurons distributed over a wide area. If so, looking for coherent connectomes might be a bad way of approaching the brain.

In this respect we may be prey to misconceiving the brain through thinking of it as though it were an artefact. Human-designed machines need to have an intelligible structure so that they can be constructed and repaired easily; and for complex systems modularisation is best practice. A complex machine is put together out of replaceable sub-systems that perform discrete tasks; good code is structured to maximise reusability and intelligibility.  But Nature doesn’t have to work like that: evolution might find tangled systems that work fine and actually generate lower overheads.

That might be so, but when we look at animal biology the modularisation is actually pretty striking: the internal organs of a human being, say, are structured in a way that bears a definite resemblance to the components of a machine. Evolution never had to take account of the possibility of replacement parts, but (immune system aside) in fact our internal organisation facilitates transplant surgery much more than it need have done.

Why is that? I’d suggest that there is a secondary principle of evolution at work. When evolution is (so to speak) devising a creature for a new ecological niche, it doesn’t actually start from scratch: it modifies one of the organisms already to hand. Just as a designer finds it easier to build a new machine out of existing parts, a well-modularised creature is more likely to give rise to variant descendants that work in new roles. So besides fitness to survive, we have fitness to give rise to new descendant species; and modularisation enhances that second-order kind of fitness.  Lots of weird creatures that worked well back in the Cambrian did not lend themselves easily to redesign, and hence have no living descendant species, whereas some creature with a backbone, four limbs with five digits each and a tail, proved to be a fertile source of useable variation: leave out some digits, a pair of limbs or the tail; put big legs on the back and small ones on the front, and straightaway you’ve got a viable new modus operandi. In the same way a creature that bolted on an appendix to its gut might be more ready to produce descendants without the appendix function than one which had reconditioned the function of its whole system (I’m getting well out of my depth here). In short, maybe there is an evolutionary tendency to modularisation after all, so it is reasonable to look for connectomes.  As a further argument, we may note that it would seem to make sense in general for neurons that interact a lot to be close together, forming natural connectomes, though given the promiscuous connectivity of the brain some caution about that may be appropriate.

Anyway, what we care about here is consciousness, so the question for us must be: is there a consciousness connectome? In one sense, of course, there must be (and here we stub our toe on another potential danger of the connectome approach): if we just go on listing all the neurons that play a part in consciousness we will at some point have a full set.  But that might end up being the entire brain: what we want to know is whether there is a coherent self-contained module or set of modules supporting consciousness. Things we might be looking out for would surely include a Global Workspace connectome, and I think perhaps a Higher Order Thought Connectome: either might be relatively clearly identifiable on the basis of their pattern of connections.

I don’t think we’re in any position to say yet, but as a speculation I would guess that in fact there is a set of connectomes that have to act together to support a set  of interlocking functions making up consciousness:  sub-conscious thought,  awareness/input, conscious reflection, emotional tone, and so on. I’m not suggesting by any means that that is the actual list; rather I think it is likely that connectome research might cause us to rethink our categories just as research is already causing us to stop thinking that memory (as we had always supposed)  is a single function.

There is already some sign that connectomes might carve up the brain in ways that don’t match our existing ways of thinking about it:  Martijn van den Heuvel and Olaf Sporns have published a paper which seems to show that there are twelve sites of special interest where interconnections are especially dense: they call this a “rich club”, but I think the functional implications of these twelve special zones remain tantalisingly obscure for the moment.

In the end my guess is that by about 2040 we shall look back on the connectome as a paradigm that turned out to be inadequate to the full complexity of the brain, but one which inspired research essential to a much improved understanding.

*I do realise BTW that words are under no obligation to mean what the Latin or Greek they were derived from suggests – “chromosome” would mean “colour body” which is a trifle opaque to say the least.


  1. 1. Kevin Kim says:

    Does no one else see the word “connectome” and read it as “connect to me”?

  2. 2. Vicente says:

    I don’t mean to be Perfect Peter (Horrid Henry’s brother), but chromosome is a perfectly sensible naming, since chromatin, the nucleoprotein of chromosomes stains strongly with basic dyes. Actually showing nice bands. Sorry I started reading your post bottom-up.

    You’ll find that most citological and histollogical naming has to do with colours and shapes.

  3. 3. Doru says:

    The term “connectome” seems very adequate to comprehend the complexity of the brain as far as the ability to have conscious thoughts. The Connectome Project is probably destined for producing meaningless conclusions and a waste of funds. Brains get fully sprouted and the most synaptic connectivity at 2-3 years of age. After that new connections become insignificant and eventual as we age, all the unused connections fade away, leaving behind just the ones reinforced with “drug like” molecules (dopamine, endo-cannabinoids etc.) A more interesting project is the Brain on Chip project from MIT.
    Peter, it would be great if you have any comments on that as it relates somehow with the last posting.
    In one calculation, it would make possible a fully conscious entity implementation using several hundred millions of those silicon chips. Cost will be probably several tens of billions, size of a large building and the power consumption in several hundred megawatts. I would expect learning and training to be faster than in a human brain but very difficult to implement because the mobility (and interacting with the environment) constrains.

  4. 4. Tom says:

    The second page of this article has an interesting discussion of modularity in the context of evolution.
    Perhaps not relevant to the connectome, but fascinating nonetheless: anatomical modules and the genetic modules that underlie them function somewhat independently of one another, e.g. on an evolutionary timescale, the genetic code for an arm might vary while the arm itself remains constant in structure.

  5. 5. Richard J R Miles says:

    Tom,I could not get the URL to work.

  6. 6. scott bakker says:

    Have you had a chance to check out the National Academy of Engineering’s Grand Challenge, Peter?

    I’m inclined to agree with your prognosis. If you think of what the epigenetics revolution has meant with reference to the Human Genome Project, you could make the argument that these ‘hardware’ approaches might not be nearly so illuminating as we think. To pick up on your point, there’s organic modularity and there’s informatic modularity, and its the intrinsic dynamicism of the latter that makes the question of the ‘connectome’ so difficult. Actual, functionally operational ‘connectomes,’ will likely be informatic (in the non-semantic sense). There’s a sense in which, sticking to the metaphor, the brain is likely far more analogous to what they recently called ‘junk DNA’ than to the ‘genome.’ Given the astronomical complexities posed by the former (all but demolishing the grander claims geneticists were once inclined to make) we probably should expect the 38 petaflop (most recent estimate) environment of the brain to pose an informatic jungle that is many, many orders of magnitude more complex.

    My guess is that the computer we will need to design to fully figure our brains out will be the one that flips a coin to decide whether to keep us was pets.

  7. 7. Peter says:

    Scott – yes. The Grand Challenge is interesting: setting it up as a matter of reverse engineering may well be a productive approach. At the same time, there are some extra problems when you don’t even have a perfectly clear grasp of the function of the thing being analysed, which I should say is still the case for the brain.

    Doru – I have a few thoughts about the ‘brain on chip’ which I will put into a separate post.

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