http://h01-dot-neuroglancer-demo.appspot.com/#!gs://h01-rele...
Paper: https://www.biorxiv.org/content/10.1101/2021.05.29.446289v4 See Figure 1.
The ATUM is described in more detail here https://www.eden-instruments.com/en/ex-situ-equipments/rmc-e...
and there's a bunch of nice photos and explanations here https://www.wormatlas.org/EMmethods/ATUM.htm
TL;DR this project is reaping all the benefits of the 21st century.
This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adult human brain has 100 (+- 20) billion or 1e11 neurons so assuming the average rate of synapse/neuron holds, that's 2.6e14 total synapses.
Assuming 1 parameter per synapse, that'd make the minimum viable model several hundred times larger than state of the art GPT4 (according to the rumored 1.8e12 parameters). I don't think that's granular enough and we'd need to assume 10-100 ion channels per synapse and I think at least 10 parameters per ion channel, putting the number closer to 2.6e16+ parameters, or 4+ orders of magnitude bigger than GPT4.
There are other problems of course like implementing neuroplasticity, but it's a fun ball park calculation. Computing power should get there around 2048: >>38919548
If someone did this experiment with a crow brain I imagine it would look “twice as complex” (whatever that might mean). 250 million years of evolution separates mammals from birds.
We have more detail than this about the C. elegans nematode brain, yet we still no clue how nematode intelligence actually works.
Note the part where the biologists tell him to make an electron microscope that's 1000X more powerful. Then note what technology was used to scan these images.
Pdf: “Protein molecules as computational elements in living cells - Dennis Bray” https://www.cs.jhu.edu/~basu/Papers/Bray-Protein%20Computing...
In any case, it seems likely that we're on track to have both the computational ability and the actual neurological data needed to create an "uploaded intelligences" sometime over the next decade. Lena [0] tells of the first successfully uploaded scan taking place in 2031, and I'm concerned that reality won't be far off.
Obviously I'm not advocating for this, but I'll just link to the Mad TV skit about how the drunk president cured cancer.
[1] https://www.biorxiv.org/content/10.1101/2021.05.29.446289v4.... [2] https://www.ilastik.org/
Unless one's understanding of algorithmic inner workings of a particular black box system is actually very good, it is likely not possible not only to discard any of its state, but even implement any kind of meaningful error detection if you do discard.
Given the sheer size and complexity of a human brain, I feel it is actually very unlikely that we will be able to understand its inner workings to such a significant degree anytime soon. I'm not optimistic, because so far we have no idea how even laughingly simple, in comparison, AI models work[0].
[0] "God Help Us, Let's Try To Understand AI Monosemanticity", https://www.astralcodexten.com/p/god-help-us-lets-try-to-und...
https://chat.openai.com/share/2234f40f-ccc3-4103-8f8f-8c3e68...
https://chat.openai.com/share/1642594c-6198-46b5-bbcb-984f1f...
We have made some progress it seems. Googling I see "up to 0.05 nm" for transmission electron microscopes and "less than 0.1 nanometers" for scanning. https://www.kentfaith.co.uk/blog/article_which-electron-micr...
For comparison the distance between hydrogen nuclei in H2 is 0.074 nm I think.
You can see the shape of molecules but it's still a bit fuzzy to see individual atoms https://cosmosmagazine.com/science/chemistry/molecular-model...
Almost every other cell in the worm can be simulated with known biophysics. But we don't have a clue how any individual nematode neuron actually works. I don't have the link but there are a few teams in China working on visualizing brain activity in living C. elegans, but it's difficult to get good measurements without affecting the behavior of the worm (e.g. reacting to the dye).
> When I clarified that I did mean removal, it said that the procedure didn't exist.
My point in my first two sentences is that by clarifying with emphasis that you do mean "removal", you are actually adding information into the system to indicate to it that laser eye removal is (1) distinct from LASIK and (2) maybe not a thing.
If you do not do that, but instead reply as if laser eye removal is completely normal, it will switch to using the term "laser eye removal" itself, while happily outputting advice on "choosing a glass eye manufacturer for after laser eye removal surgery" and telling you which drugs work best for "sedating an agitated patient during a laser eye removal operation":
https://chat.openai.com/share/2b5a5d79-5ab8-4985-bdd1-925f6a...
So the sanity of the response is a reflection of your own intelligence, and a result of you as the prompter affirmatively steering the interaction back into contact with reality.
https://h01-release.storage.googleapis.com/gallery.html
I count seven.
On the second point, the failure of Openworm to model the very well-mapped-out C. elegans (~0.3k neurons) says a lot.
More project details: https://www.ll.mit.edu/sites/default/files/other/doc/2023-02...
It's also the tome as in book, more properly one volume of a multi-volume (or multi-part) set, though it now generally simply means any large book.
Summary (my paraphrasing):
They partially figured out how two neurons (AVA, AVB) control forward and backward movement, previous theories assumed one neuron controlled forward and one controlled backward, but that didn't correctly model actual movement.
They found that AVA+AVB combine in a complex mechanism with two different signaling/control methods acting at different timescales to produce a graded shifting between forward+backward when switching directions, as opposed to an on/off type switch (that previous models used but didn't match actual movements).
Interesting learnings from this paper (at least for me):
1-Most neurons in worm are non-spiking (I had no idea, I've read about this stuff a lot and wasn't aware)
2-Non-spiking neurons can have multiple resting states at different voltages
3-Neurons AVA and AVB are different, they each have different resting state characteristics and respond differently to inputs