Mapping the Connectome

Could Artificial Intelligence be the tool that finally allows us understand the natural kind?

We’ve talked before about the possibility; this Nautilus piece explains that scientists at the Max Planck Institute Of Neurobiology have come up with a way of using ‘neural’ networks to map, well, neural networks. There has been a continued improvement in our ability to generate images of neurons and their connections, but using those abilities to put together a complete map is a formidable task; the brain has often been described as the most complex object in the universe and drawing up a full schematic of its connections is potentially enough work for a lifetime. Yet that map may well be crucial; recently the idea of a ‘connectome’ roughly equivalent to the ‘genome’ has become a popular concept, one that suggests such a map may be an essential part of understanding the brain and consciousness. The Max Planck scientists have developed an AI, called ‘SyConn’ which tu4ns images into maps automatically with very high accuracy. In principle I suppose this means we could have a complete human connectome in the not-too-distant future.

How much good would it do us, though? It can’t be bad to have a complete map, but there are three big problems. The first is that we can already be pretty sure that connections between neurons are not the whole story. Neurons come in many different varieties, ones that pretty clearly seem to have different functions – but it’s not really clear what they are. They operate with a vast repertoire of neurotransmitters, and are themselves pretty complex entities that may have genuine computational properties all on their own. They are supported by a population of other, non-networked cells that may have a crucial role in how the overall system works. They seem to influence each other in ways that do not require direct connection; through electromagnetic fields, or even through DNA messages. Some believe that consciousness is not entirely a matter of network computation anyway, but resides in quantum or electrical fields; certainly the artificial networks that were originally inspired by biological neurology seem to behave in ways that are useful but quite distinct from those of human cognition. Benjamin Libet thought that if only he could do the experiment, he might be able to demonstrate that a sliver of the brain cut out from its neighbouring tissue but left in situ would continue to do its job. That, surely, is going too far; the brain didn’t grow all those connections with such care for nothing. The connectome may not be the full story, but it has to be important.

The second problem, though, is that we might be going at the problem from the wrong end. A map of the national road network tells us some useful things about trade, but not what it is or, in the deeper sense, how it works. Without those roads, trade would not occur in anything like the same form; blockages and poor connections may hamper or distort it, and in regions isolated by catastrophic, um, road lesions, trade may cease altogether. Of course to understand things properly we should need to know that there are different kinds of vehicle doing different jobs on those roads, that places may be connected by canals and railways as well as roads, and so on. But more fundamentally, if we start with the map, we have no idea what trade really is. It is, in fact, primarily driven and determined by what people want, need, and believe, and if we fall into the trap of thinking that it is wholly determined by the availability of trucks, goods, and roads we shall make a serious mistake.

Third, and perhaps it’s the same problem in different clothes, we still don’t have any clear and accepted idea of how neurology gives rise to consciousness anyway. We’re not anywhere near being able to look at a network and say, yup, that is (or could be) conscious, if indeed it is ever possible to reach such a conclusion.

So do we really even want a map of the connectome? Oh yes…

Synaptomes – and galaxies

A remarkable paper from a team at Edinburgh explains how every synapse in a mouse brain was mapped recently, a really amazing achievement. The resulting maps are available here.

We must try not to get too excited; we’ve been reminded recently that mouse brains ain’t human brains; and we must always remember that although we’ve known all about the (outstandingly simple) neural structure of the flatworm Caenorhabditis elegans for years, we still don’t know quite how it produces the flatworm’s behaviour, and cannot make simulations work. We haven’t cracked the brain yet.

In fact, though, the elucidation of the mouse ‘synaptome’ seems to offer some tantalising clues about the way brains work, in a way that suggests this is more like the beginning of something big than the end. A key point is the identification of some 37 different types of synapse. Particular types seem to become active in particular cognitive tasks; different regions have different characteristic mixes of the types of synapse, and it appears that regions usually associated with ‘higher’ cognitive functions, such as the neocortex and the hippocampus, have the most diverse sets of synapse types. Not only that; mapping the different synapse types reveals new boundaries and structures, especially within the neocortex and hippocampus, where the paper says ‘their synaptome maps revealed a plethora of previously unknown zones, boundaries, and gradients’.

What does it all mean? Hard to say as yet, but it surely suggests that knowledge of the pattern of connections between neurons isn’t enough. Indeed, it could well be that our relative ignorance of synaptic diversity and what goes on at that level is one of the main reasons we’re still puzzled by Caenorhabditis. Watch this space.

The number of neurons in the human brain, curiously enough, is more or less the same as the number of stars in a galaxy (this is broad brush stuff). In another part of the forest, Vazza and Felletti have found evidence that the structural similarities between brains and galaxies go much further than that. Quite why this should be so is mysterious, and it might or might not mean something; nobody is suggesting that galaxies are conscious (so far as I know).

Connectome

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.