Being able to treat the pattern of someone's brain as software to be run on a computer, perhaps in parallel or at a large speedup, would have a huge impact, both socially and economically. Robin Hanson thinks it is the most likely route to artificial intelligence. Anders Sandberg and Nick Bostrom of the Future Of Humanity Institute created out a roadmap for whole brain emulation in 2008, which covers a huge amount of research in this direction, combined with some scale analysis of the difficulty of various tasks.
Because the human brain is so large, and we are so far from having the technical capacity to scan or emulate it, it's difficult to evaluate progress. Some other organisms, however, have much smaller brains: the nematode C. elegans has only 302 cells in its entire nervous system. It is extremely well studied and well understood, having gone through heavy use as a research animal for decades. Since at least 1986 we've known the full neural connectivity of C. elegans, something that would take decades and a huge amount of work to get for humans. At 302 neurons, simulation has been within our computational capacity for at least that long. With 25 years to work on it, shouldn't we be able to 'upload' a nematode by now?
Reading through the research, there's been some work on modeling subsystems and components, but I only find three projects that have tried to integrate this research into a complete simulation: the University of Oregon's NemaSys (~1997), the Perfect C. elegans Project (~1998), and Hiroshima University's Virtual C. Elegans project (~2004). The second two don't have web pages, but they did put out papers: , , .
Another way to look at this is to list the researchers who seem to have been involved with C. elegans emulation. I find:
- Hiroaki Kitano, Sony 
- Shugo Hamahashi, Keio University 
- Sean Luke, University of Maryland 
- Michiyo Suzuki, Hiroshima University 
- Takeshi Goto, Hiroshima Univeristy 
- Toshio Tsuji, Hiroshima Univeristy 
- Hisao Ohtake, Hiroshima Univeristy 
- Thomas Ferree, University of Oregon 
- Ben Marcotte, University of Oregon 
- Sean Lockery, University of Oregon 
- Thomas Morse, University of Oregon 
- Stephen Wicks, University of British Columbia 
- Chris Roehrig, University of British Columbia 
- Catharine Rankin, University of British Columbia 
- Angelo Cangelosi, Rome Instituite of Psychology 
- Domenico Parisi, Rome Instituite of Psychology 
This seems like a research area where you have multiple groups working at different universities, trying for a while, and then moving on. None of the simulation projects have gotten very far: their emulations are not complete and have some pieces filled in by guesswork, genetic algorithms, or other artificial sources. I was optimistic about finding successful simulation projects before I started trying to find one, but now that I haven't, my estimate of how hard whole brain emulation would be has gone up significantly. 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.
Note: I later reorganized this into a blog post, incorporating some feed back from these comments.
 The Perfect C. elegans Project: An Initial Report (1998)
 A Dynamic Body Model of the Nematode C. elegans With Neural Oscillators (2005)
 A model of motor control of the nematode C. elegans with neuronal circuits (2005)
 Robust spacial navigation in a robot inspired by C. elegans (1998)
 Neural network models of chemotaxis in the nematode C. elegans (1997)
 Chemotaxis control by linear recurrent networks (1998)
 Computational rules for chemotaxis in the nematode C. elegans (1999)
 A Dynamic Network Simulation of the Nematode Tap Withdrawl Circuit: Predictions Concerning Synaptic Function Using Behavioral Criteria (1996)
 A Neural Network Model of Caenorhabditis Elegans: The Circuit of Touch Sensitivity (1997)
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.... (read more)
David Dalrymple is also trying to emulate all of C. elegans, and was at the Singularity Summit.
That's me. In short form, my justification for working on such a project where many have failed before me is:
"A complete functional simulation of the C. elegans nervous system will exist on 2014-06-08." 76% confidence
"A complete functional simulation of the C. elegans nervous system will exist on 2020-01-01." 99.8% confidence
It's worth noting that a 5nm SEM imaging pass will give you loads more information than a connectome, especially in combination with fancy staining techniques. It just so happens that most people doing SEM imaging intend to extract a connectome from the results.
That said, given the current state of knowledge, I don't think there's good reason to expect any one particular imaging technology currently known to man to be capable of producing a human upload. It may turn out that as we learn more about stereotypical human neural circuits, we'll see that certain morphological features are very good predictors of important parameters. It may be that we can develop a stain whose distribution is a very a good predictor of important parameters. Since we don't even know what the important parameters are, even in C. elegans, let alone mammalian cortex, it's hard to say with confidence that SEM will capture them.
However, none of this significantly impacts my confidence that human uploads will exist within my lifetime. It is an a priori expected feature of technologies that are a few breakthroughs away that it's hard to say what they'll look like yet.
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
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:... (read more)
Unbounded Scales, Huge Jury Awards, & Futurism:... (read more)
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.
There are many models of neurons, at many levels of detail. I think that the Neuron program uses the finest detail of any existing software.
I see the primary purpose of a simulating a nematode as measuring how well such models actually work. If they do work, it also lets us estimate the amount of detail needed, but the first question is whether these models are biologically realistic. An easier task would be to test whether the models accurately describe a bunch of neurons in a petri dish. The drawback of such an approach is that it is not clear what it would mean for a model to be adequate for that purpose, whereas in a organism we know what constitutes biologically meaningless noise. Also, realistic networks probably suppress certain kinds of noise.
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 m... (read more)
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 ca... (read more)
I've reorganized this into a blog post incorporating what I've learned in the comments here.
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.
Henry Markram of the Blue Brain Project described this claim as a "hoax and a PR stunt", "shameful and unethical", and "mass deception of the public".
Any new developments on the C. Elegans simulation in the past 3+ years?