Whole Brain Emulation: Looking At Progress On C. elgans

Hi all,

Glad there's excitement on this subject. I'm currently coordinating an open source project whose goal is to do a full simulation of the c. elegans (http://openworm.googlecode.com). More on that in a minute.

If you are surveying past c. elegans simulation efforts, you should be sure not to leave out the following:

A Biologically Accurate 3D Model of the Locomotion of Caenorhabditis Elegans, Roger Mailler, U. Tulsa http://j.mp/toeAR8

C. Elegans Locomotion: An integrated Approach -- Jordan Boyle, U. Leeds http://j.mp/fqKPEw

Back to Open Worm. We've just published a structural model of all 302 neurons (http://code.google.com/p/openworm/wiki/CElegansNeuroML) represented as NeuroML (http://neuroml.org). NeuroML allows the representation of multi-compartmental models of neurons (http://en.wikipedia.org/wiki/Biological_neuron_models#Compartmental_models). We are using this as a foundation to overlay the c. elegans connectivity graph and then add as much as we can find about the biophysics of the neurons. We believe this represents the first open source attempt to reverse-engineer the c. elegans connectome.

One of the comments mentioned Andrey Palyanov's mechanical model of the c. elegans. He is part of our group and is currently focused on moving to a soft-body simulation framework rather than the rigid one they created here: http://www.youtube.com/watch?feature=player_embedded&v=3uV3yTmUlgo Our first goal is to combine the neuronal model with this physical model in order to go beyond the biophysical realism that has already been done in previous studies. The physical model will then serve as the "read out" to make sure that the neurons are doing appropriate things.

Our roadmap for the project is available here: http://code.google.com/p/openworm/wiki/Roadmap

We have a mailing list here: http://groups.google.com/group/openworm

We have regular meetings on Google+ Hangout. If you want to help, we can surely find a way to include you. If you are interested, please let us know and we'll loop you in.

Cheers, Stephen

David Dalrymple is also trying to emulate all of C. elegans, and was at the Singularity Summit.

http://syntheticneurobiology.org/people/display/144/26

Maybe a more troubling situation for the feasibility of human brain emulation would be if we had had nematode emulation working for a decade or more but had made no apparent headway to emulating the next level of still not very impressive neural complexity, like a snail. At the moment there's still the possibility we're just missing some kind of methodological breakthrough, and once that's achieved there's going to be a massive push towards quickly developing emulations for more complex animals.

http://www.computerra.ru/interactive/589824 A. Palianov now works in Russia on nematode brain emulation project

While I wouldn't say whole brain emulation could never happen, this looks to me like it is a very long way out, probably hundreds of years.

Does this assessment take into account the possibility of intermediate acceleration of human cognition?

I wrote to Ken Hayworth who is a neuroscience researcher working on scanning and interested in whole brain emulation, and he wrote back:

I have not read much on the simulation efforts on C. elegans but I have talked several times to one of the chief scientists who collected the original connectome data and has been continuing to collect more electron micrographs (David Hall, in charge of www.wormatlas.org). He has said that the physiological data on neuron and synapse function in C. elegans is really limited and suggests that no one spend time simulating the worm using the existing datasets because of this. I.e. we may know the connectivity but we don't know even the sign of many synapses.

If you look at a system like the retina I would argue that we already have quite good models of its functioning and thus it is a perfect ground for testing emulation from known connectivity.

So the short answer is that I think it may be far easier to emulate a well characterized and mapped part of the mammalian brain than it is to emulate the worm despite its smaller size.

While I wouldn't say whole brain emulation could never happen, this looks to me like it is a very long way out, probably hundreds of years.

Unbounded Scales, Huge Jury Awards, & Futurism:

I observe that many futuristic predictions are, likewise, best considered as attitude expressions. Take the question, "How long will it be until we have human-level AI?" The responses I've seen to this are all over the map. On one memorable occasion, a mainstream AI guy said to me, "Five hundred years." (!!)

Now the reason why time-to-AI is just not very predictable, is a long discussion in its own right. But it's not as if the guy who said "Five hundred years" was looking into the future to find out. And he can't have gotten the number using the standard bogus method with Moore's Law. So what did the number 500 mean?

As far as I can guess, it's as if I'd asked, "On a scale where zero is 'not difficult at all', how difficult does the AI problem feel to you?" If this were a bounded scale, every sane respondent would mark "extremely hard" at the right-hand end. Everything feels extremely hard when you don't know how to do it. But instead there's an unbounded scale with no standard modulus. So people just make up a number to represent "extremely difficult", which may come out as 50, 100, or even 500. Then they tack "years" on the end, and that's their futuristic prediction.

"How hard does the AI problem feel?" isn't the only substitutable question. Others respond as if I'd asked "How positive do you feel about AI?", only lower numbers mean more positive feelings, and then they also tack "years" on the end. But if these "time estimates" represent anything other than attitude expressions on an unbounded scale with no modulus, I have been unable to determine it.

Are these projects about emulation? The Oregon and Rome projects seem to treat the brain as a black box, rather than taking advantage of Brenner's connectome. I'm not sure about the others. That doesn't tell us much about the difficulty of emulation, except that they thought their projects were easier.

Brenner's connectome is not enough information. At the very least, you need to know whether synapses are exciting or inhibiting. This pretty much needs to be measured, which is rather different than what Brenner did. It might not require a lot of measurement: once you've measured a few, maybe you can recognize the others. Or maybe not.

How well can a single neuron or a few neurons be simulated? If we have good working models of those, which behave as we see in life, then that means WBE might be harder, if no such models yet exist, then the failures to model a 302-neuron system are not such good evidence for difficulty.

While I wouldn't say whole brain emulation could never happen, this looks to me like it is a very long way out, probably hundreds of years.

What kind of reasoning leads you to this time estimate? Hundreds of years is an awfully long time -- consider that two hundred years ago nobody even knew that cells existed, and there didn't exist any kind of computers.

From your description of the state of the field, I guess we won't see an uploaded nematode very soon, but getting there in a decade or two doesn't seem impossible. It seems a bit counter-intuitive to me that learning "no nematode know, but maybe in ten years" would move the point estimate for human uploads by several centuries. Because, what if we had happened to do this literature survey ten years later, and found out that indeed nematodes had been successfully uploaded? If the estimate is sensitive to very small changes like that, it must be very uncertain.

I was disappointed when I first looked into the C. elegans emulation progress. Now I'm not so sure it's a bad sign. It seems to me that at only 302 neurons the nervous system is probably far from the dominant system of the organism. Even with a perfect emulation of the neurons, it's not clear to me if the resulting model would be meaningful in any way. You would need to model the whole organism, and that seems very hard.

Contrast that with a mammal, where the brain is sophisticated enough to do things independently of feedback from the body, and where we can see these larges scale neural patterns with scanners. If we uploaded a mouse brain, presumably we could get a rough idea that the emulation was working without ever hooking it up to a virtual body.

I've reorganized this into a blog post incorporating what I've learned in the comments here.

None of the simulation projects have gotten very far...this looks to me like it is a very long way out, probably hundreds of years.

Couldn't you say the same about AGI projects? It seems to me that one of the reasons that some people are being relatively optimistic about computable approximations to AIXI, compared to brain emulations, is that progress on EM's is easier to quantify.

This depends on whether the problem is the basic complexity of modeling a neural network or learning how to do it. If the former, then we may be looking at a long time. But if it's the latter, then we really just need more attempts, successful or not, to learn from and a framework which allows a leap in understanding could arrive.

Typo in the title!

IBM claims to be doing a cat brain equivalent simulation at the moment, albeit 600 time slower and not all parts of the brain.

Any new developments on the C. Elegans simulation in the past 3+ years?