Neuroimaging as alternative/supplement to cryonics?

I think we are in agreement that recognition of important faces is pretty important... but I can imagine losing just that in a stroke and having to re-learn it, and still being me. It would be annoying, and I can imagine that too much change of that sort might cause what's left of me to be incapable or uninterested in recognizing and cherishing things that are important to me. In that case, I think a copy of my current self and the future inhabitant of a body historically connected to this one might agree we weren't really instances of the same person, because they'd diverged too much.

However, it seems like you might be able to do this sort of brain scan using input data with the things you cherished in them, and many raw facts about the valence and elements of your care for aspects of what you care about would be recorded. Your tenderness or inattention or lust or disgust for many specific parts of the different things that you currently care about would be recorded. How your eyes saccade around each image would probably be reconstructable, so it would capture not just that you recognize your mother, but how you recognize your mother.

Starting with a well preserved brain, I can imagine nanoscale structures being "bottom-up" reconstructed in functional form, but when it comes time to do "integration testing" on the resulting brain model, I could imagine the initial draft having seizures, because certain long distance ratios being a bit different might have set up new resonance possibilities or something. I'm not defending this precise neurological fact (I have no high precision model of how siezures work), I'm just trying to provide a simple example from the space of possible bugs to show that the space is probably non-empty. Perfect representation of a frozen brain run on simulated physics would be just as non-functional in the simulation as a the frozen brain is in reality. You'd need to adjust the brain model and/or the simulation process (maybe both) to get something that works at all.

Testing for seizure prone-ness and adaptively adjusting things to fix that seems like the sort of thing that would be a very basic and obvious part of quality assurance processes on reconstructions. Testing for "memories of cherishing X" seems like something that would be much harder to make a part of basic QA processes, absent external data about this fact captured at the rough level of abstraction where it was obvious (ie by measuring actual responses to actual stimulus). If this was one of the first ever reconstructions of a person then this extra layer of data for "emotional recognition level QA" is something I can imagine appreciating a lot, as an engineer.

When I imagine reconstructing an acceptable King Tut, I'm thinking of this as happening deep into the development of revival processes, where so much information from detail-informed reconstruction of other people's brains is known that basic details of functional neurology aren't even science anymore, they're just engineering, or maybe just application of existing tools by far future script kiddies, and if the tools are solid enough and they hook into the far future Everythingpedia, then maybe all you really need is description of who the person was that cuts human personhood at the joints.

Like the big five is an attempt to describe "personality" in a way that was state of the art for the 1940s and is only very slowly filtering out into mainstream cultural awareness even now. What I'm thinking of would be like the "big million" and perhaps be state of the art for the 2140's. In that sort of context, enough QA data might be all that is really required, and (plus or minus) I can imagine enough QA data being available from archaeological artifacts. A reconstructed Tut really might recognize his Aunt because there is surviving data on her. Given genomics data, a statue of a relative, and lots of computer time, perhaps he would also be able to recognize his mother as she once was.

In some sense there is a really extreme inside-view/outside-view issue here. Taking the extreme outside view, with background knowledge about the entire earth at this moment in time counting as an "accessible codebook" for compression, it takes less than 50 yes/no questions to identify any one of us. Taking the extreme inside view, with the precise configuration of every molecule considered as a completely de novo surprise it probably takes something like 10^50 yes/no questions to precisely describe any one of us. Maybe the issue is that I tend to imagine reconstruction happening in an environment richer in know-how, data, and resources, and so I tend to think fewer bits are required than other people?

The physicality of the reconstruction alone would require impossibly good casting. A leg that's actually injured, a body with epigenetics consistent with the inferred diets of ancient Egyptian monarchs, food preferences consistent with dental wear marks... and so on. That leaves aside finding someone with the right physicality who can even act, and learn Egyptian, and so on and so forth. Which still leaves aside that there would be a lot in the actor that was not in Tut. Jim Carrey was not Andy Kaufman.

Sure but these things cost money and we have finite resources. A dedicated fMRI machine would cost somewhere between 2-6 times SIAI's annual expenditures.

So are you saying that the conditions in the womb doesn't have any effect on brain structure?

By the way, try to aim higher than DH3 because it's hard for me to understand what exactly you're disagreeing with if you don't provide me with a counterargument. Sorry for the bother.

This seems to be false. See for example here.

you periodically take neuroimaging scans of your brain and save them to multiple backup locations (1010 bits is only about 1 gigabyte)

I think I understand but I'm lost as to why that 10^10 is showing up here. Wouldn't it be whatever the scan happens to be rather than a reference to the compressed size of a human's unique experiences? We might plausibly have a 10^18 scan that is detailed in the wrong ways (like it carries 1024 bits per voxel of color channel info :p).

eta: In case it's not clear, I can't actually help you answer the question of just how useful a scan is.

It's also not clear that one could tell whether it failed. That is, OK, it processes the scans, interpolates over the gaps, and a person pops out the other end who believes himself to remember being me. Yay? Maybe.

Then again, it's not clear to me that I ought to care about the difference.

I've been thinking about this more and I recall a study that's already been done and which seems to be capturing most of the important data, and which even has built in quality assurance process that provides a measure of assurance that the measurements contain meaningful content... reconstruction of observed images from brain scanning. Once you see the first example it suggests a whole range of iteratively more complicated research projects. One might attempt reconstruction of an audio channel, reconstruction of audio+video, doing structured interview and reconstructing the audio and text transcripts of the subjects vocal production, and perhaps eventually doing video or audio chat of some sort, with an attempt to reconstruct both sides of the conversation from two separate but synchronized brain scans. Neat :-)

ETA: The conversational reconstruction attempt seems like it would have specific cryonics implications, in terms of gaining data on specific social dynamics whose visceral experienced realities are really really important to people when the subject is discussed in near mode.

Also http://www.aleph.se/andart/archives/2012/04/how_many_persons_can_there_be_brain_reconstruction_and_big_numbers.html

Well, if I'm doing morningstar rhetoric, I'd best get my game face on.

First, in the paper below, their estimate of the information density of the brain is somewhat wrong. What you actually need is the number of neurons in the brain (10^11), squared, times two bytes for half-precision floating point storage of the strength of the synaptic connection, plus another two bytes to specify the class of neuron, times two, to add fudge factor for white matter doing whatever it is that white matter does, which all works out to 6.4 * 10^23.

Now that we've actually set up the problem, let's see if we can find a way it might still be possible. First, let's do the obvious thing and forget about the brain stem. Provided you've got enough other human brain templates on ice, that can probably be specified in a negligable number of terrabytes, to close enough accuracy that the cerebral cortex won't miss it. What we really care about are the 2 10^10 neurons in the cerebral cortex. Which brings out overall data usage down to 1 10^22. Not a huge improvement, I grant you, but we're working. Second, remember that our initial estimate was for the ammount of RAM needed, not the entropy. We're storing slots in a two dimensional array for each neuron to connect to every other neuron, which will never happen. Assuming 5000 synapses per neuron, that means that 5000 / ( 2* 10^9) of our dataset is going to be zeroes. Let's apply run-length encoding for zeroes only, and we should see a reduction by a factor of a hundred thousand, conservatively. That brings it down to 10^17 bits, or 11 petabytes.

Now let's consider that the vast majority of connectomes will never occur inside a human brain. If you generate a random connectome from radio noise and simulate it as an upload, even within the constraints already specified, the result will not be able to walk, talk, reason, or breathe. This doesn't happen to neurologically healthy adults, so we can deduce that human upbringings and neural topology tend to guide us into a relatively narrow section of connectome space. In fact, I suspect that there's a good chance that uploading this way would be a process of starting with a generic human template, and tweaking it. Either way, if you took a thousand of those randomly generated minds, I would be very surprised if any of them was anything resembling a human being, so we can probably shave another three orders of magnitude off the number of bits required. That's 10^15 bits of data, or 113 terrabytes. Not too shabby.

Based on this, and assuming that nights are a wash, and we get no data, in order to specify all those bits in ten years, we would need to capture something like 6.4 megabits a second of entropy, or a little less than a megabyte. This seems a little high. However, there are other tricks you could use to boost the gain. For example, if you have a large enough database of human brain images, you could meaningfully fill in gaps using statistics. For example: if, of the ten thousand people with these eight specific synaptic connections, 99% also have a particular ninth one, it'd be foolish not to include it.

In short, it seems somewhat infeasible, but not strictly impossible. You could augment by monitoring spinal activity, implanting electrodes under your scalp to directly record data on brain region activation, and by the future use of statistical analytics.

Now, actually deducing the states of the brain based on its output, as you said, might be difficult or impossible enough to put an end to the whole game before it starts. Still, it might actually work.

Some of them are pretty discrete, apparantly. I looked into it, and you can buy bluetooth headsets with cameras built into them.

I just thought of a good analogy: if you have a hash of random gibberish you don't need recovered (thermal fluctuations amplified by neurons) combined with the data you want to recover (personality), if the random gibberish is larger than hash you'll probably not be able to recover useful data. That leaves open the question how much random noise gets hashed into result.

Nah, I don't think you think theological thoughts, but I do admit I have that opinion of some other people. Theological thoughts are strangely attractive to minds :/ . That's how we got religion in first place. Can't be too careful about recreating religion.

With the hash functions, there seems to be a very strong disparity - a definition of a hash can fit in 1 page, but our entire might can not solve it. And that's with us trying to use fewer iterations.

Here we are speaking of a hash, definition of which is trillions times larger, and which introduces random thermal noise. Complexity theory is not only tool... there's also the Lyapunov's exponent considerations; it may well be that too big range of minds are compatible with the dataset, even though the dataset has enough data - if the thermal noise got hashed in. Then there's can be future ethical considerations against any form of brute force process that creates minds only to destroy them.

On whenever it is worth a try, for me the strategic considerations apply - if I am to deem something like this worth a try, I will end up buying snake oil. Also, the money, for most of us, can be spent on living better life now.

Nothing, I haven't even bothered getting UCSF to send it to me. I haven't yet looked into what I should do once I get around to doing that.