I love it when anything Caenorhabditis elegans (C. Elegans) related pops up because this little biological organism sits at this beautiful intersection between technology and biology and philosophy. The successful emulation of C. Elegans would represent a concrete step towards whole brain emulation and all the transhuman and ethical and moral quandaries that would bring. The general idea is that the human brain has billions of neurons, Elegans has hundreds (and we've had them mapped since 1986). If one can successfully "upload" Elegans, then humans are just a matter of scale.
However, it should be noted that the field, and specifically this line of research, hasn't produced much in the way of results in 10+ years. University of Oregon planned (though I can't tell if they ever developed) NemaSys[0] ~1997. OpenWorm has been exploring this since 2011. Project Nemaload explored it a bit from 2011-2013.[1] But each project ran into three problems:
- 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.[2]
- C. elegans is able to learn by changing the weights. We don't know how weights and thresholds are changed in a living worm.[2]
- Funding [3]
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. Relevant neural recording technologies are needed to collect data from living worms, but they remain undeveloped (but in progress?[4][5][6]), and the funding simply isn't there.
OpenWorm got the idea to plug their connectome into a Lego robot[7] and got it to exhibit the tap-withdrawal behavior of the nematode, but it had technical limitations preventing easy modification of the connectome or introduction of new models of neural dynamics. JHU Applied Physics Lab extended the work by using a basic integrate and fire model to simulate the neurons and assigned weights by determining the proportion to the total number of synapses the two neurons on either side of the synapses shared and in the end got the simulated worm to reverse direction when bumping into walls.[8] At this point, humanity seems to have abandoned emulated worm driven mechanisms which is honestly kind of a loss.
There's no real ending to this comment. Love this project, loves what it stands for, looking forward to seeing progress in this field. And a lot of this information was pulled from this blog post[9] which was also mentioned in the comments somewhere.
There are a bunch of labs that are making progress in reading the full nervous system (so to speak) realtime in live worms. I like especially the outputs from Andrew Leifer lab in Princeton. I anticipate some very interesting results to come from these labs in the next decade.
I briefly considered doing a postdoc in one of these labs, because I love working worms and agree with your proposition that next logical step in neuroscience is modeling and fully understanding an entire organism. The late Sydney Brenner asked for the same in 2011 [1].
But most academic labs doing well won’t even consider a postdoc application from a student who didn’t work in the exact same field. Solidified my decision to never be part of the Ponzi scheme that is academic research.
The beauty of C. Elegans is that you actually need very little to start working with them. All the strains are available for 10 bucks a pop, you can do most work at room temperature. I only need to invest on a very custom (but not necessarily outrageously expensive) microscope to start working on this topic in my garage. Which I absolutely plan to start in the next few years. I’ve done the math and it’ll cost me less than owning a cheap boat lol. If anyone wants to fund me I’ll be open to it too :)
1. Sydney, B. & Sejnowski, T. J. Understanding the human brain. Science 334, 567 (2011).
I actually take this as a positive, they have uploaded a shell of a 'brain', and have identified the next problem, 'weights'. Why isn't what they have done so far be amazing enough to start tackling the next thing. I am surprised that with Neural Nets being such a hot bed, the amount of money pouring into AI, and NeuralLink type research, that they would have a hard time with funding.
Watching that lego worm really zapped my brain, seemed like we were on the cusp of something. Maybe we still are, and just misjudged the time-scale on progress.
What would a "real world" application of this be? Could I for example chuck a bunch of simulated worms onto a map and have it solve a route? "Given enough worms, all Travelling Salesman problems are shallow?"
However, it should be noted that the field, and specifically this line of research, hasn't produced much in the way of results in 10+ years. University of Oregon planned (though I can't tell if they ever developed) NemaSys[0] ~1997. OpenWorm has been exploring this since 2011. Project Nemaload explored it a bit from 2011-2013.[1] But each project ran into three problems:
- 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.[2]
- C. elegans is able to learn by changing the weights. We don't know how weights and thresholds are changed in a living worm.[2]
- Funding [3]
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. Relevant neural recording technologies are needed to collect data from living worms, but they remain undeveloped (but in progress?[4][5][6]), and the funding simply isn't there.
OpenWorm got the idea to plug their connectome into a Lego robot[7] and got it to exhibit the tap-withdrawal behavior of the nematode, but it had technical limitations preventing easy modification of the connectome or introduction of new models of neural dynamics. JHU Applied Physics Lab extended the work by using a basic integrate and fire model to simulate the neurons and assigned weights by determining the proportion to the total number of synapses the two neurons on either side of the synapses shared and in the end got the simulated worm to reverse direction when bumping into walls.[8] At this point, humanity seems to have abandoned emulated worm driven mechanisms which is honestly kind of a loss.
There's no real ending to this comment. Love this project, loves what it stands for, looking forward to seeing progress in this field. And a lot of this information was pulled from this blog post[9] which was also mentioned in the comments somewhere.
[0] https://web.archive.org/web/20030115124331/http://www.csi.uo...
[1] https://github.com/nemaload
[2] https://www.jefftk.com/p/we-havent-uploaded-worms
[3] https://www.quora.com/Is-Larry-Page-funding-any-neuroscience...
[4] https://arxiv.org/pdf/2109.10474.pdf
[5] https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.24483
[6] https://www.sciencedirect.com/science/article/pii/S095943882...
[7] https://www.cnn.com/2015/01/21/tech/mci-lego-worm/
[8] https://ccneuro.org/2018/proceedings/1149.pdf
[9] https://www.lesswrong.com/posts/mHqQxwKuzZS69CXX5/whole-brai...