Since the early 21st century, some transhumanist proponents and futuristic researchers claim that Whole Brain Emulation (WBE) is not merely science fiction - although still hypothetical, it's said to be a potentially viable technology in the near future. Such beliefs attracted significant fanfare in tech communities such as LessWrong.

In 2011 at LessWrong, jefftk did a literature review on the emulation of a worm, C. elegans, as an indicator of WBE research progress.

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?

There were three research projects from the 1990s to the 2000s, but all are dead-ends that were unable to reach the full research goals, giving a rather pessimistic vision of WBE. However, immediately after the initial publication of that post, LW readers Stephen Larson (slarson) & David Dalrymple (davidad) pointed out in the comments that they were working on it, the two ongoing new projects of their own made the future look promising again.

The first was the OpenWorm project, coordinated by slarson. Its goal is to create a complete model and simulation of C. elegans, and to release all tools and data as free and open source software. Implementing a structural model of all 302 C. elegans neurons in the NeuroML description language was an early task completed by the project.

The next was another research effort at MIT by davidad. David explained that the OpenWorm project focused on anatomical data from dead worms, but very little data exists from living animals' cells. They can't tell scientists about the relative importance of connections between neurons within the worm's neural system, only that a connection exists.

The "connectome" of C. elegans is not actually very helpful information for emulating it. Contrary to popular belief, connectomes are not the biological equivalent of circuit schematics. Connectomes are the biological equivalent of what you'd get if you removed all the component symbols from a circuit schematic and left only the wires. Good luck trying to reproduce the original functionality from that data.
What you actually need is to functionally characterize the system's dynamics by performing thousands of perturbations to individual neurons and recording the results on the network, in a fast feedback loop with a very very good statistical modeling framework which decides what perturbation to try next.
With optogenetic techniques, we are just at the point where it's not an outrageous proposal to reach for the capability to read and write to anywhere in a living C. elegans nervous system, using a high-throughput automated system. It has some pretty handy properties, like being transparent, essentially clonal, and easily transformed. It also has less handy properties, like being a cylindrical lens, being three-dimensional at all, and having minimal symmetry in its nervous system. However, I am optimistic that all these problems can be overcome by suitably clever optical and computational tricks.

In a year or two, he believed an automated device can be built to gather such data. And he was confident.

I'm a disciple of Kurzweil, and as such I'm prone to putting ridiculously near-future dates on major breakthroughs. In particular, I expect to be finished with C. elegans in 2-3 years. I would be Extremely Surprised, for whatever that's worth, if this is still an open problem in 2020.

When asked by gwern for a statement for PredictionBook.com, davidad said:

"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

(disappointingly, these statements were not actually recorded on PredictionBook).

Unfortunately, 10 years later, both projects appear to have made no significant progress and failed to develop a working simulation that is able to resemble biological behaviors. In a 2015 CNN interview, slarson said the OpenWorm project was "only 20 to 30 percent of the way towards where we need to get", and seems to be in the development hell forever since. Meanwhile, I was unable to find any breakthrough from davaidad before the project ended. David personally left the project in 2012.

When the initial review was published, there was already 25 years of works on C. elegans, and right now yet another decade has passed, yet we're still unable to "upload" a nematode. Therefore, I have to end my post with the pessimistic vision of WBE by quoting the original post.

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.

This is discouraging.

Closing thoughts: What went wrong? What are the unsolvable difficulties here?

Update

Some technical insights behind the failure was given in a 2014 update ("We Haven't Uploaded Worms"), jefftk showed the major problems are:

Knowing the connections isn't enough, we also need to know the weights and thresholds. We don't know how to read them from a living worm.
C. elegans is able to learn by changing the weights. We don't know how weights and thresholds are changed in a living worm.

The best we can do is modeling a generic worm - pretraining and running the neural network with fixed weights. Thus, no worm is "uploaded" because we can't read the weights, and these simulations are far from realistic because they are not capable of learning. Hence, it's merely a boring artificial neural network, not a brain emulation.

To see why this isn't enough, consider that nematodes are capable of learning. [...] For example, nematodes can learn that a certain temperature indicates food, and then seek out that temperature. They don't do this by growing new neurons or connections, they have to be updating their connection weights. All the existing worm simulations treat weights as fixed, which means they can't learn. They also don't read weights off of any individual worm, which means we can't talk about any specific worm as being uploaded.
If this doesn't count as uploading a worm, however, what would? Consider an experiment where someone trains one group of worms to respond to stimulus one way and another group to respond the other way. Both groups are then scanned and simulated on the computer. If the simulated worms responded to simulated stimulus the same way their physical versions had, that would be good progress. Additionally you would want to demonstrate that similar learning was possible in the simulated environment.

Furthermore, in a Quora answer, davidad hinted that his project was discontinued partially due to the lack of funding.

If I'd had $1 million seed, I wouldn't have had to cancel the project when I did...

Conclusion: Relevant neural recording technologies are needed to collect data from living worms, but they remain undeveloped, and the funding simply isn't there.

Update 2

I just realized David actually had an in-depth talk about his work and the encountered difficulties at MIRI's AI Risk for Computer Scientists workshop in 2020, according to this LW post ("AIRCS Workshop: How I failed to be recruited at MIRI").

Most discussions were pretty high level. For example, someone presented a talk where they explained how they tried and failed to model and simulate a brain of C. Elegansis. A worm with an extremely simple and well understood brain. They explained to us a lot of things about biology, and how they had been trying and scanning precisely a brain. If I understood correctly, they told us they failed due to technical constraints and what those were. They believe that, nowadays, we can theoretically create the technology to solve this problem. However there is no one interested in said technology, so it won't be developed and be available to the market.

Does anyone know any additional information? Is the content of that talk available in paper form?

Update 3

Note to the future readers: within a week of the initial publication of this post, I received some helpful insider comments, including David himself, on the status of this field. The followings are especially worth reading.

David explains why this field was understudied and underfunded in the past 10 years - a main reason of the slow progress.
- it's getting better now. Here's a list of recent works.
Delton explains recent progress on C. elegans simulations and what happened with the OpenWorm project.
- Especially worth noting: CarbonCopies Foundation did a workshop in June 2021 with Steven Larson on the OpenWorm project. A recording of the 4 hour event is online.