Wow this is great, I am once again incredibly frustrated by my seeming inability to find relevant research without asking people already in the know. If you can think of anything else that would be interesting, please let me know. If there is a single site anywhere that has links and lists of relevant research that would be great. Even OpenWorm and CarbonCopies seem a little scattered but this might be a reading comprehension issue on my part. 

I am curious what you think about optical techniques for connectome tracing. Personally, I really like the idea especially as optical microscopy will allow for lots of stains to be used and will hopefully make inferring electrical properties from dead cells easier. So far though, there doesn't seem to be a large effort of connectome tracing from expansion microscopy (although a lot of research into the various microscopes and robots to automate everything) and even less on getting cellular properties post mortem. If you have thoughts I would like to hear, if you have research you think is relevant that would be great too. 

Ignoring methodology issues for a moment, it is impossible to tell if women have inability to flirt or men have an inability to tell if a woman is flirting. To disentangle this, I propose an experiment with men/men and women/women pairings. In a perfect world, you would also have every combination of (binary) gender and sexual orientation. 

Also, sitting in my basement staring at ChatGPT.com is not (yet) the best way to maintain friendships and find a new boyfriend.

Are you stepping away as to not be dependent, to not lose some part of the human experience no matter how reliable these tools become, or simple for practical reasons?

Also, do you find LLMs to be an effective tool in interpersonal relationships? You call it a cheat code and that sounds much better than my personal experience so far although I have mostly been using them for debugging code rather than social situations.  

To my knowledge, the most recent c. elegans model was all the way back in 2009 

it is this PhD thesis which I admit I have not read in its entirety. 

I found on the OpenWorm history page which is looking rather sparse unfortunately. 

I was trying to go through everything they have, but again, was very disillusioned after trying to fully replicate + expand this paper on chemotaxis. You can read more about what I did here on my personal site. It's pretty lengthy so the TL;DR is that I tried to convert his highly idealized model back into explicit neuron models and it just didn't really work. Explicitly modeling c elegans in any capacity would be a great project because there is so much published, you can copy others and fill in details or abstract as you wish. There is even an OpenWorm slack but I don't remember how to join + it's relatively inactive. 

That is more than enough stuff to keep you busy but if you want to hear me complain about learning rules read on.

I am really frustrated with learning rules for a couple reasons. The biggest one is that researchers just don't seem to have very much follow through on the obvious next steps. either that or I'm really bad at finding/reading papers. In any case, what I would love to work on/read about a learning algorithm that

1. Uses only local information + maybe some global reward function (as in, it can't be some complicated error minimizer like backpropagation, people generally call this biologically plausible)
2. Has experimental evidence that real neurons really learn like this
3. Can do well on one shot learning tasks (fear/avoidance can be learned from single negative stimuli even in really simple animals)
4. Performs well on general learning, as an example, I tried to recreate tuning curves with LIF neurons using the BCM rule + homeostasis, it was really easy to get a population of neurons to respond differently to horizontal vs vertical sine waves but if those sine waves had a phase shift it basically completely failed.
5. Work with deep/complex recurrent architecture 

From what I can tell, many papers address one or two of these but fail to capture everything. Maybe I'm being too greedy, but I feel like this list is pretty sensible for a minimum of whatever learning algorithms are at play in the brain. 
 

I am going to work on the project I outline here but I would genuinely love to help you even if it's just bouncing ideas off me. Be warned, I also am not formally trained in a lot of neuroscience so take everything I say with a heap of salt. 

Based on this and you other comment you seem to be pro GEVI instead of patch clamp, am I correct? Assuming GEVIs were used (or some other, better technology) to find all electrophysiology, why would that be a waste of time? Even if we can get by with a few thousand template neurons and individual tuning is not necessary (which seems to be the view of Steven Byrne and maybe you) how should we go about getting those template neurons without a lot of research into correlating morphology, genetic expression, and electrophysiology? If we don't need them, why would we not? My primary goal is not to defend my plan, I just care about making progress on WBE generally and I would like to hear specific plans if others have them. Studying single cell function just seemed to be the most natural to me. Without that, studying how multiple neurons signal each other or change over time or encode information in spike trains seems like putting the cart before the horse as it were. Again, very glad to be wrong, it just still seems to me that some version of this research has to be done eventually, we haven't done it yet AFAIK, so I should start on what little part I can. 

Yes, I am familiar with the sleep = death argument. I really don't have any counter, at some point though I think we all just kind of arbitrarily draw a line. I could be a solipsist, I could believe in last thursdayism, I could believe some people are p-zombies, I could believe in the multiverse. I don't believe in any of these but I don't have any real arguments for them and I don't think anyone has any knockdown arguments one way or the other. All I know is that I fear soma style brain upload, I fear star trek style teleportation, but I don't fear gradual replacement nor do I fear falling asleep. 

As for wrapping up our more scientific disagreement, I don't have much to say other than it was very thought provoking and I'm still going to try what I said in my post. Even if it doesn't come to complete fruition I hope it will be relevant experience for when I apply to grad school. 

I think this is general admirable in theory, at least broad strokes, but way way harder than you anticipate. The last project I worked on alone I was trying to copy c elegans chemotaxis with a biological neuron model and then have it remember where food was from a previous run and steer in that direction even if there was no food anywhere in its virtual arena, something real c elegans has been observed doing. Even the first part was not a huge success and because of that I put an indefinte pause on the second part. I would love to see you carry on the project or something similar, maybe you will have more success especially if you abstract more. I'm happy to share code and talk more if you're interested. But at this time, it is my impression that we just don't understand individual neurons, synaptic weight, or learning rules well enough to take a good pass at it.

First of all, I hate analogies in general but that's a pet peeve, they are useful. But going with your shaken up circuit as an analogy to brain organoids and assuming it is true, I think it is more useful than you give it credit. If you have a good theory of what all those components are individually you would still be able to predict something like voltage between two arbitrary points. If you model resistors as some weird non ohmic entity you'll probably get the wrong answer because you missed the fact that they behave ohmic in many situations. If you never explicitly write down Ohm's law but you empirically measure current at a whole bunch of different voltages (analogous to patch clamps but far far from a perfect analogy) you can probably get the right answer. So yeah an organoid would not be perfect but I would be surprised if being able to fully emulate one would be useless. Personally I think it would be quite useful but I am actively tempering my expectations. 

But my meta point of 

1. look at small system
2. try to emulate
3. cross off obvious things (electrophysiology should be simple for only a few neurons) that could cause it to not be working
4. repeat and use data to develop overall theory

stands even if organoids in particular are useless. The theory developed with this kind of research loop might be useless for your very abstract representation of the brain's algorithm but I think it would be just fine, in principle, for the traditional, bottom up approach. 

As for the philosophical objections, it is more that whatever wakes up won't be me if we do it your way. It might act like me and know everything I know but it seems like I would be dead and something else would exist. Gallons of ink have been spilled over this so suffice it to say, I think the only thing with any hope of preserving my consciousness (or at least a conscious mind that still holds the belief that it was at one point the person writing this) is gradual replacement of my neurons while my current neurons are still firing. I know that is far and away the least likely path of WBE because it requires solving everything else + nanotechnology but hey I dream big. 

To  be clear, I think your proposed WBE plan has a lot of merit, but it would still result in me experiencing death and then nothing else so I'm not especially interested. Yes, that probably makes me quite selfish. 

Not my claim so I'm not defending this too hard but from my lab experience relatively few genes seem to control bulk properties and then there are a whole bunch of higher order corrections. Literally one or two genes being on/off can determine if a neuron is excitatory or inhibitory. If you subscribe to Izhikevich's classification of bistable/monostable and integrator/resonator you would only need 3 genes with binary expression. After that you get a few more to determine time constants and stuff. I still think whole transcriptome would be helpful, especially as we don't know what each gene does yet, but I am not 100% against the idea that only ~20 really matter with a few thousand template neurons and after that you run into a practical limit of noise being present.