Whole Brain Emulation: No Progress on C. elegans After 10 Years

Let's look at a proxy task. "Rockets landing on their tail". The first automated landing of an airliner was in 1964. Using a similar system of guidance signals from antenna on the ground surely a rocket could land after boosting a payload around the same time period. While SpaceX first pulled it off in 2015.

In 1970 if a poorly funded research lab said they would get a rocket to land on its tail by 1980 and in 1980 they had not succeeded, would you update your estimated date of success to "centuries"? C elegans has 302 neurons and it takes, I think I read 10 ANN nodes to mimic the behavior of one biological neuron. With a switching frequency of 1 khz and it's fully connected you would need 302 * 100^2 *1000 operations per second. This is 0.003 TOPs, and embedded cards that do 200-300 TOPs are readily available.

So the computational problem is easy. Did David build an automated device to collect data from living cells? If not, was the reason it wasn't done because of some sudden unexpected huge difficulty that 100+ people and multi-million dollar budget couldn't solve, or was it because...those people weren't there and neither was the funding?

My larger point is that if the 'math checks out' on the basic feasibility of an idea, either there is something about the problem that makes it enormously harder than it appears, or simply not enough resources were invested to make progress. SpaceX, for example, had approximately 2-5 billion dollars spent by 2015 and the first rocket landing. A scrappy startup with say 1 million dollars might not get anywhere. How much funding did these research labs working on C Elegans have?

I might have time for some more comments later, but here's a few quick points (as replies):

While we're sitting around waiting for revolutionary imaging technology or whatever, why not try and make progress on the question of how much and what type of information can we obscure about a neural network and still approximately infer meaningful details of that network from behavior. For practice, start with ANNs and keep it simple. Take a smallish network which does something useful, record the outputs as it's doing its thing, then add just enough random noise to the parameters that output deviates noticeably from the original. Now train the perturbed version to match recorded data. What do we get here, did we recover the weights and biases almost exactly? Assuming yes, how far can this go before we might as well have trained the thing from scratch? Assuming success, does it work equally on different types and sizes of networks, if not what kind of scaling laws does this process obey? Assuming some level of success, move on to a harder problem, a sparse network, this time we throw away everything but connectivity information and try to repeat the above. How about something biologically realistic but we try to simulate the spiking neurons with groups of standard artificial ones.. you get the drift.

The tone of strong desirability for progress on WBE in this was surprising to me. The author seems to treat progress in WBE as a highly desirable thing; a perspective I expect most on LW do not endorse.

The lack of progress here may be a quite good thing.

Connectome scanning continues to scale up drastically, particularly on fruit flies. davidad highlights some very recent work:

I could be wrong, but we're still currently unable to get that original C. elegans neural map to do anything (like run a simulated worm body), right?

I think @AndrewLeifer is almost there but, yes, still hasn’t gone all the way to a demonstration of behavior in a virtual environment: "A11.00004 : A functional connectivity atlas of C. elegans measured by neural activation".

Neural processing and dynamics are governed by the details of how neural signals propagate from one neuron to the next through the brain. We systematically measured functional properties of neural connections in the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous calcium imaging of 10,438 neuron pairs. By measuring responses to neural activation, we extracted the strength, sign, temporal properties, and causal direction of the connections and created an atlas of causal functional connectivity.

We find that functional connectivity differs from predictions based on anatomy, in part, because of extrasynaptic signaling. The measured properties of the connections are encoded in kernels which describe signal propagation in the network and which we fit from the data. Using such kernels, we can run numerical simulations of neural activity in the worm's brain using exclusively information that comes from the data, as opposed to simulations based on the anatomical connectome which require assumptions on many parameters.

We show that functional connectivity better predicts spontaneous activity than anatomy, suggesting that functional connectivity captures properties of the network that are critical for interpreting neural function.

An important feature of his work is that he’s shown functional connectivity is not only strictly more informative than anatomical connectomics, but that simulations actually get worse when incorporating connectomic constraints (because e.g. hormones work external to synapses).

Also relevant: "A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing", Shiu et al 2023:

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain.

Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors.

We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes.

Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

"Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution", Lappalainen et al 2023:

We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question.

Here we show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single neuron and single synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion.

Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies.

Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.

I want to point out that there has been some very small amounts of progress in the last 10 years on the problem of moving from connectome to simulation rather than no progress. 

First, there has been interesting work at the JHU Applied Physics Lab which extends what Busbice was trying to do when he tried to run as simulation of c elegans in a Lego Mindstorms robot (by the way, that work by Busbice was very much overhyped by Busbice and in the media, so it's fitting that you didn't mention it). They use a basic integrate and fire model to simulate the neurons (which is probably actually not very accurate here because c elegans neurons don't actually seem to spike much and seem to rely on subthreshold activity more so than in other organisms). To assign weights to the different synapses they used what appears to be a very crude metric - the weight was determined in proportion to the total number of synapses the two neurons on either side of the synapse share. Despite the crudeness of their approach, the simulated worm did manage to reverse it's direction when bumping into walls.  I believe this work was a project that summer interns did and didn't have a lot of funding, which makes it more impressive in my mind than it might seem at first glance. 

Another line of work that seems worth pointing out is this 2018 work simulating "hexagonal cells" in the drosophilia which has been done at Janelia: "A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System". They claim "Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit". I skimmed this paper and found it a bit underwhelming since it appears the validation of the model was mostly in terms of summary statistics. 

Finally, for anyone who wants to learn what happened with the OpenWorm project, the CarbonCopies Foundation did a workshop in June 2021 with Steven Larson. A recording of the 4 hour event is online. I was present for a bit of it at the time it aired, but my recollection is dim. I believe part of the issue they ran into was figuring out how to simulate the physiology of the neuron (ie all the non-neuronal cells). Some people in the OpenWorm open source community managed to build a  3D model (you can view it here). If I recall correctly, he mentioned there was some work to embed that model in a fluid dynamics simulation and "wire it" with a crude simulation of the nervous system, and they got it to wiggle in some way that looked plausible. 

Imagine you have two points, A and B. You're at A, and you can see B in the distance. How long will it take you to get to B?

Well, you're a pretty smart fellow. You measure the distance, you calculate your rate of progress, maybe you're extra clever and throw in a factor of safety to account for any irregularities during the trip. And you figure that you'll get to point B in a year or so.

Then you start walking.

And you run into a wall. 

Turns out, there's a maze in between you and point B. Huh, you think. Well that's ok, I put a factor of safety into my calculations, so I should be fine. You pick a direction, and you keep walking. 

You run into more walls.

You start to panic. You figured this would only take you a year, but you keep running into new walls! At one point, you even realize that the path you've been on is a dead end — it physically can't take you from point A to point B, and all of the time you've spent on your current path has been wasted, forcing you to backtrack to the start.

Fundamentally, this is what I see happening, in various industries: brain scanning, self-driving cars, clean energy, interstellar travel, AI development. The list goes on.

Laymen see a point B in the distance, where we have self-driving cars run on green energy powered by AGI's. They see where we are now. They figure they can estimate how long it'll take to get to that point B, slap on a factor of safety, and make a prediction. 

But the real world of problem solving is akin to a maze. And there's no way to know the shape or complexity of that maze until you actually start along the path. You can think you know the theoretical complexity of the maze you'll encounter, but you can't. 

It seems to me that this task has an unclear goal. Imagine I linked you a github repo and said "this is a 100% accurate and working simulation of the worm." How would you verify that? If we had a WBE of Ray Kurzweil, we could at least say "this emulated brain does/doesn't produce speech that resembles Kurzweil's speech." What can you say about the emulated worm? Does it wiggle in some recognizable way? Does it move towards the scent of food?

As someone interested in seeing WBE become a reality, I have also been disappointed by the lack of progress. I would like to understand the reasons for this better. So I was interested to read this post, but you seem to be conflating two different things. The difficulty of simulating a worm and the difficulty of uploading a worm. There are a few sentences that hint both are unsolved, but they should be clearly separated.

Uploading a worm requires being able to read the synaptic weights, thresholds, and possibly other details from an individual worm. Note that it isn't accurate to say it must be alive. It would be sufficient to freeze an individual worm and then spend extensive time and effort reading that information. Nevertheless, I can imagine that might be very difficult to do. According to wormbook.org, C. elegans has on the order of 7,000 synapses. I am not sure we know how to read the weight and threshold of a synapse. This strikes me as a task requiring significant technological development that isn't in line with existing research programs. That is, most research is not attempting to develop the technology to read specific weights and thresholds. So it would require a significant well-funded effort focused specifically on it. I am not surprised this has not been achieved given reports of lack of funding. Furthermore, I am not surprised there is a lack of funding for this.

Simulating a worm should only require an accurate model of the behavior of the worm nervous system and a simulation environment. Given that all C. elegans have the same 302 neurons this seems like it should be feasible. Furthermore, the learning mechanism of individual neurons, operation of synapses, etc. should all be things researchers outside of the worm emulation efforts should be interested in studying. Were I wanting to advance the state of the art, I would focus on making an accurate simulation of a generic worm that was capable of learning. Then simulate it in an environment similar to its native environment and try to demonstrate that it eventually learned behavior matching real C. elegans including under conditions which C. elegans would learn. That is why I was very disappointed to learn that the "simulations are far from realistic because they are not capable of learning." It seems to me this is where the research effort should focus and I would like to hear more about why this is challenging and hasn't already been done.

I believe that worm uploading is not needed to make significant steps toward showing the feasibility of WBE. The kind of worm simulation I describe would be more than sufficient. At that point, reading the weights and thresholds of an individual worm becomes only an engineering problem that should be solvable given a sufficient investment or level of technological advancement.

This post was a great dive into two topics:

• How an object-level research field has gone, and what are the challenges it faces.
• Forming a model about how technologically optimistic projects go.

I think this post was good on it's first edition, but became great after the author displayed admirable ability to update their mind and willingness to update their post in light of new information.

Overall I must reluctantly only give this post a +1 vote for inclusion, as I think the books are better served by more general rationality content, but I'm terms of what I would like to see more of on this site, +9. Maybe I'll compromise and give +4.

One complication glossed over in the discussion (both above and below) is that a single synapse, even at a single point in time, may not be well characterized as a simple "weight". Even without what we might call learning per se, the synaptic efficacy seems, upon closer examination, to be a complicated function of the recent history, as well as the modulatory chemical environment. Characterizing and measuring this is very difficult. It may be more complicated in a C. elegans than in a mammal, since it's such a small but highly optimized hunk of circuitry.

There’s a scan of 1 mm^3 of a human brain, 1.4 petabytes with hundred(s?) of millions of synapses

‪https://ai.googleblog.com/2021/06/a-browsable-petascale-reconstruction-of.html‬

Do we at least have some idea of what kind of technology would be needed for reading out connection weights?

Two years later, there are now whole brain wide recordings on C. Elegans via calcium imaging. This includes models apparently at least partially predictive of behavior and analysis of individual neuron contributions to behavior. 

If you want the "brain-wide recordings and accompanying behavioral data" you can apparently download them here!

It is very exciting to finally have measurements for this. I still need to do more than skim the paper though. While reading it, here are the questions on my mind:
* What are the simplest individual neuron models that properly replicates each measured neuron-activation? (There are different cell types so take that into account too)
* If you run those individually measurement-validated neuron models forward in time, do they collectively produce the large scale behavior seen? 
    * If not, why not? What's necessary?
* Are these calcium imaging measurements sufficient to construct the above? (Assume individualized connectomes per-worm are gathered prior instead of using averages across population)
    * If not, what else is necessary? 
    * And if it is sufficient, how do you construct the model parameters from the measurements?         
* Can we now measure and falsify our models of individual neuron learning? 
    * If we need something else, what is that something?

Edit: apparently Gwern is slightly ahead [LW(p) · GW(p)] of me and pointed at Andrew Leifer whose group an entire year ago who produced a functional atlas of C Elegans that also included calcium imaging. Which I'd just totally missed. One missing element is extrasynaptic signaling, which apparently has a large impact on C Elegans behavior. So in order to predict neuron behavior you need to attend to those as well.
 

One review criticized my post for being inadequate at world modeling - readers who wish to learn more about predictions are better served by other books and posts (but also praised me for being willing to update its content after new information arrived). I don't disagree, but I felt it was necessary to clarify my background of writing it.

First and foremost, this post was meant specifically as (1) a review of the research progress on Whole Brain Emulation of C. elegans, and (2) a request for more information from the community. I became aware of this research project since 10 years ago on Wikipedia, and like everyone else, I thought it would be a milestone of transhumanism. At the beginning of 2020 I remembered it again - it seemed to stuck in development hell forever, without clear reasons. Frustrated, I decided to find out why.

I knew transhumanism has always been an active topic on LessWrong so naturally I came here and searched the posts. It was both disappointing and encouraging. There was no up-to-date post beyond the original one in 2010, but I found some researchers are LW members, and it was likely that I would be able to learn more by asking it here, perhaps with an "insider" answer.

Thus, I made the original post. It was not, at all, intended as an exercise of forecasting or world modeling. Then, I was completely surprised by the post's reception. Despite being the first post I've ever made, it received a hundred upvotes within days, and later, it was selected by the editors as a "Curated" homepage post. It even became the reading material at offline meetups! It was completely unexpected. As a result, jefftk - the lead researcher of one project, personally answered my questions. I updated my post to incorporate new information as they arrived.

In conclusion, this post completely served its purpose of gathering new information about the research progress in this field. However, the research I did in the original post (before update) was incomplete. I totally missed the 2014 review and a 2020 mention, both were literally on LW. If I knew the post would be selected as Curated and widely read by a large audience as an exercise of world modeling (and winning a prize), I would have used more patience during my initial research before sending the original version.

This is a total nitpick but there is a typo in the title of both this post and the one from jefftk referenced by it. It's "C. elegans", not "C. elgans".

There's a fundamental difficulty with these sorts of attempts to emulate entire nervous systems (which gets exponentially worse as you scale up) that I don't think gets enough attention:  failure of averaging. See this paper on simulating single neurons: https://pubmed.ncbi.nlm.nih.gov/11826077/#:~:text=Averaging%20fails%20because%20the%20maximal,does%20not%20contain%20its%20mean.

The abstract:

"Parameters for models of biological systems are often obtained by averaging over experimental results from a number of different preparations. To explore the validity of this procedure, we studied the behavior of a conductance-based model neuron with five voltage-dependent conductances. We randomly varied the maximal conductance of each of the active currents in the model and identified sets of maximal conductances that generate bursting neurons that fire a single action potential at the peak of a slow membrane potential depolarization. A model constructed using the means of the maximal conductances of this population is not itself a one-spike burster, but rather fires three action potentials per burst. Averaging fails because the maximal conductances of the population of one-spike bursters lie in a highly concave region of parameter space that does not contain its mean. This demonstrates that averages over multiple samples can fail to characterize a system whose behavior depends on interactions involving a number of highly variable components."

Historically, a similar problem was discovered by the US air force when trying to design the cockpits of fighter jets. They took anatomical measurements from hundred of pilots and designed a cockpit based on the average values, under the assumption that it would fit most pilots reasonably well. In actuality, it didn't fit anyone (they eventually solved the problem by making everything adjustable): https://www.thestar.com/news/insight/2016/01/16/when-us-air-force-discovered-the-flaw-of-averages.html

Curated. The topic of uploads and whole-brain emulation is a frequent one, and one whose feasibility is always assumed to be true. While this post doesn't argue otherwise, it's fascinating to hear where we're with the technology for this.

Hey, 

TL;DR I know a researcher who's going to start studying C. elegans worms in a way that seems interesting as far as I can tell. Should I do something about that?

I'm trying to understand if this is interesting for our community, specifically as a path to brain emulation, which I wonder if could be used to (A) prevent people from dying, and/or (B) creating a relatively-aligned AGI.

This is the most relevant post I found on LW/EA (so far).

I'm hoping someone with more domain expertise can say something like:

• "OMG we should totally extra fund this researcher and send developers to help with the software and data science and everything!"
• "This sounds pretty close to something useful but there are changes I'd really like to see in that research"
• "Whole brain emulation is science fiction, we'll obviously destroy the world or something before we can implement it"
• "There is a debate on whether this is useful, the main positions are [link] and [link], also totally talk to [person]"

Any chance someone can give me direction?

Thx!

(My background is in software, not biology or neurology)

OpenWorm seems to be a project with realistic goals but unrealistic funding in contrast to the EU's Human Brain Project (HBP): a project with an absurd amount of funding, with absurdly unrealistic goals. Even ignoring the absurd endpoint, any 1billion Euro project should be split up into multiple smaller ones with time to take stock of things in between.

What could the EU have achieved by giving $50million to OpenWorm to spend in 3 years (before getting more ambitious)? 

Would it not have done so in the first place because of hubris? The worm is somehow too simple to be worthy of investigation? The complexity of 300 C. Elegans neurons is way way more than the superficial perception of 300 times the complexity of 1 artificial neuron. 300 real neurons provide way more degrees of freedom than any scientist would like to be dealing with. 

$50million on OpenWorm surely would have made significant progress on the methodologies required, without being distracted by the supercomputing sideshow.

What would you have done with the $50million?

My impression of OpenWorm was that it was not focused on WBE. It tried to be a more general-purpose platform for biological studies. It attracted more attention than a pure WBE project would, by raising vague hopes of also being relevant to goals such as studying Alzheimer's.

My guess is that the breadth of their goals led them to become overwhelmed by complexity.

It's possible to donate to Openworm to accelerate the development, via their website

Maybe the problem is figuring out how to realistically simulate a SINGLE neuron, which could then be extended 302 or 100,000,000,000 times. Also due to shorter generation times any random c.elegans has 50 times more ancestors than any human, so evolution may have had time to make their neurons more complex.