14 ms·
Cubic millimetre of brain mapped at nanoscale resolution
- posnet 3y ago1.4 PB/mm^3 (petabytes per millimeter cubed)×1260 cm^3 (cubic centimeters, large human brain) = 1.76×10^21 bytes = 1.76 ZB (zetabytes)
- bahrant 3y agowow
- gary17the 3y ago[AI] "Frontier [supercomputer]: the storage capacity is reported to be up to 700 petabytes (PB)" (0.0007 ZB). [AI] "The installed base of global data storage capacity [is] expected to increase to around 16 zettabytes in 2025". Thus, even the largest supercomputer on Earth cannot store more than 4 percent of state of a single human brain. Even all the servers on the entire Internet could store state of only 9 human brains. Astonishing.
- dekhn 3y agoOne point about storage- it's economically driven. If there was a demand signal (say, the government dedicated a few hundred billion dollars to a single storage systems), hard drive manufacturers could deploy much more storage in a year. I've pointed this out to a number of scientists, but none of them could really think of a way to get the government to spend that much money just to store data without it curing a senator's heart disease.
- falcor84 3y ago> without it curing a senator's heart disease Obviously I'm not advocating for this, but I'll just link to the Mad TV skit about how the drunk president cured cancer. https://www.youtube.com/watch?v=va71a7pLvy8 https://www.youtube.com/watch?v=va71a7pLvy8
- falcor84 3y agoI appreciate you're running the numbers to extrapolate this approach, but just wanted to note that this particular figure isn't an upper bound nor a longer bound for actually storing the "state of a single human brain". Assuming the intent would be to store the amount of information needed to essentially "upload" the mind onto a computer emulation, we might not yet have all the details we need in this kind of scanning, but once we do, we may likely discover that a huge portion of it is redundant. 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. [0] https://qntm.org/mmacevedo https://qntm.org/mmacevedo
- rmorey 3y agowe are nowhere near whole human brain volume EM. the next major milestone in the field is a whole mouse brain in the next 5-10 years, which is possible but ambitious
- falcor84 3y agoWhat am I missing? Assuming exponential growth in capability, that actually sounds very on track. If we can get from 1 cubic millimeter to a whole mouse brain in 5-10 years, why should it take more than a few extra years to scale that to a human brain?
- rmorey 3y agoassuming exponential growth in capacity is a big assumption!
- gary17the 3y ago> we may likely discover that a huge portion of [a human brain] is redundant 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-understand https://www.astralcodexten.com/p/god-help-us-lets-try-to-und...
- treprinum 3y agoAI folks dream about creating superintelligence to guide our lives but all we can do is drosophilla's brain.
- shpx 3y agoIf you can preserve and scan the tissue in a way that lets you scan the same area multiple times you wouldn't need to digitize the whole thing. Put the slices on rotating platters with a microscope for each platter and read parts of the brain on demand. It's a hard drive but instead of magnets storing the bits of an image of the sample, it's the actual physical sample.
- gary17the 3y agoNot if you want to actually execute the state of a human brain in a digital simulation to see how it works and whether it still displays certain abilities such as comprehension and consciousness. Otherwise a digital scan of a brain is just a glorified microscope.
- ibeforee 3y agoWe don’t know to even model that state: do we need the position and velocity and charge of every atom, or can a neuron be approximated by a bfloat?
- userbinator 3y agoIt's very lossy and unreliable storage, however. To use an analogy, it's only a huge amount of ECC that keeps things (just barely) working.
- deleted 3y ago[deleted]
- fractal618 3y agoFascinating! I wonder how different that is from the mind of a man haha
- stefantalpalaru 3y ago[dead]
- teuobk 3y agoThe interactive visualization is pretty great. Try zooming in on the slices and then scrolling up or down through the layers. Also try zooming in on the 3D model. Notice how hovering over any part of a neuron highlights all parts of that neuron: http://h01-dot-neuroglancer-demo.appspot.com/#!gs://h01-release/assets/neuroglancer_states/20210601/c2_library.json http://h01-dot-neuroglancer-demo.appspot.com/#!gs://h01-rele...
- jamiek88 3y agoMy god. That is stunning. To think that’s one single millimeter of our brain and look at all those connections. Now I understand why crows can be so smart walnut sized brain be damned. What an amazing thing brains are. Possibly the most complex things in the universe. Is it complex enough to understand itself though? Is that logically even possible?
- deleted 3y ago[deleted]
- ignoramous 3y agoI wonder if we manage to annotate this much level of detail about our brain, and then let (some variant of the current) models train on it, will those intrinsically end up generalizing a model for intelligence?
- nicklecompte 3y agoI think you would also need the epigenetic side, which is very poorly understood: https://www.universityofcalifornia.edu/news/biologists-transfer-memory-between-snails https://www.universityofcalifornia.edu/news/biologists-trans... We have more detail than this about the C. elegans nematode brain, yet we still no clue how nematode intelligence actually works.
- Animats 3y agoHow's OpenWorm coming along?
- CSSer 3y agoFor some people, this is all you need (sorry, couldn’t resist)!
- eminence32 3y ago> cut the sample into around 5,000 slices — each just 34 nanometres thick — that could be imaged using electron microscopes. Does anyone have any insight into how this is done without damaging the sample?
- talsit 3y agoUsing a Microtome (https://en.m.wikipedia.org/wiki/Microtome https://en.m.wikipedia.org/wiki/Microtome).
- dredmorbius 3y agoNB: tome in Microtome has the same root as the T in CAT scan: computer aided tomography. Which is to say, slimly-sliced cabbage^W X-ray scans. 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. <https://www.etymonline.com/search?q=tome https://www.etymonline.com/search?q=tome>
- themoonisachees 3y agoIncredible, love learning language facts like this.
- dredmorbius 3y agoEtymology is disturbingly addictive.
- habi 3y agohttps://en.wikipedia.org/wiki/Tomography https://en.wikipedia.org/wiki/Tomography: `The word tomography is derived from Ancient Greek τόμος tomos, "slice, section" and γράφω graphō, "to write" or, in this context as well, "to describe."`
- dekhn 3y agoThe sample is stained (to make thigns visible), then embedded in a resin, then cut with a very sharp diamond knife and the slices are captured by the tape reel. Paper: https://www.biorxiv.org/content/10.1101/2021.05.29.446289v4 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-em-sample-prep-solutions/atumtome/ 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 https://www.wormatlas.org/EMmethods/ATUM.htm TL;DR this project is reaping all the benefits of the 21st century.
- g4zj 3y agoIs there a name for the somewhat uncomfortable feeling caused by seeing something like this? I wish I could better describe it. I just somehow feel a bit strange being presented with microscopic images of brain matter. Is that normal?
- ignoramous 3y agoTrypophobia, visceral, uncanny, squeamish?
- greenbit 3y agoIs it the shapes, similar to how patterns of holes can disturb some people? Or is it more abstract, like "unknowable fragments of someone's inner-most reality flowed through there"? Not that I have a name for it either way. The very shape of it (in context) might represent an aspect of memory or personality or who knows what.
- g4zj 3y ago> "unknowable fragments of someone's inner-most reality flowed through there" It's definitely along these lines. Like so much (everything?) that is us happens amongst this tiny little mesh of connections. It's just eerie, isn't it? Sorry for the mundane, slightly off-topic question. This is far outside my areas of knowledge, but I thought I'd ask anyhow. :)
- greenbit 3y agoIt's kind of feeling a bit like an intruder? There probably is a name for that.
- Zenzero 3y agoFor me the disorder of it is stressful to look at. The brain has poor cable management. That said I do get this eerie void feeling from the image. My first thought was to marvel how this is what I am as a conscious being in terms of my "implementation", and it is a mess of fibers locked away in the complete darkness of my skull. There is also the morose feeling from knowing that any image of human brain tissue was once a person with a life and experiences. It is your living brain looking at a dead brain.
- throwup238 3y ago> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. 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: https://news.ycombinator.com/item?id=38919548 https://news.ycombinator.com/item?id=38919548
- gibsonf1 3y agoExcept you’d be missing the part that a neuron is not just a node with a number but a computational system itself.
- bglazer 3y agoComputation is really integrated through every scale of cellular systems. Individual proteins are capable of basic computation which are then integrated into regulatory circuits, epigenetics, and cellular behavior. Pdf: “Protein molecules as computational elements in living cells - Dennis Bray” https://www.cs.jhu.edu/~basu/Papers/Bray-Protein%20Computing.pdf https://www.cs.jhu.edu/~basu/Papers/Bray-Protein%20Computing...
- krisoft 3y agoI think you are missing the point. The calculation is intentionally underestimating the neurons, and even with that the brain ends up having more parameters than the current largest models by orders of magnitude. Yes the estimation is intentionally modelling the neurons simpler than they are likely to be. No, it is not “missing” anything.
- dekhn 3y agoAnnual reminder to re-read "There's plenty of room at the bottom" by Feynman. https://web.pa.msu.edu/people/yang/RFeynman_plentySpace.pdf https://web.pa.msu.edu/people/yang/RFeynman_plentySpace.pdf 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.
- tim333 3y agoI think it's actually "What you should do in order for us to make more rapid progress is to make the electron microscope 100 times better" and the state of art at the time was "it can only resolve about 10 angstroms" or I guess 1nm. So 100x better would be 0.1 angstrom / 0.01 nm. 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-microscope-has-the-highest-resolution_4737 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-models-electron-microscopy/ https://cosmosmagazine.com/science/chemistry/molecular-model...
- theogravity 3y ago> The brain fragment was taken from a 45-year-old woman when she underwent surgery to treat her epilepsy. It came from the cortex, a part of the brain involved in learning, problem-solving and processing sensory signals. Wonder how they figured out which fragment to cut out.
- pfdietz 3y agoI imagine they determined the focus of the seizures by electrical techniques. I worry this might make the sample biased in some way.
- notfed 3y agoImagine all the conclusions being made from a 1cm cube of epileptic neurons.
- creer 3y agoConsidering the success of this work, I doubt this is the last such cubic millimeter to be mapped. Or perhaps the next one at even higher resolution. No worries.
- seesawtron 3y agoA similar dataset already exists in mouse cortex. More are underway in the field. https://www.biorxiv.org/content/10.1101/2024.03.22.586254v1 https://www.biorxiv.org/content/10.1101/2024.03.22.586254v1
- sgt101 3y agoThe manuscript gives some details in the context of the difficulty of obtaining larger useful samples in the future and the difficulty of understanding if a sample is typical or pathological.
- blincoln 3y agoWhy did the researchers use ML models to do the reconstruction and risk getting completely incorrect, hallucinated results when reconstructing a 3D volume accurately using 2D slices is a well-researched field already?
- scotty79 3y agoMaybe it's not about reconstructing a volume but about recognizing neurons within that volume.
- rmorey 3y agoThe methods used here are state of the art. The problem is not just turning 2D slices into a 3D volume, the problem is, given the 3D volume, determining boundaries between (and therefore the 3d shape of) objects (i.e. neurons, glia, etc) and identifying synapses
- VikingCoder 3y agoI'm guessing a registration problem. If all of the layers were guaranteed to be orthographic with no twisting, shearing, scaling, squishing, with a consistent origin... Then yeah, there's a huge number of ways to just render that data. But if you physically slice layers first, and scan them second, there are all manner of physical processes that can make normal image stacking fail miserably.
- momojo 3y agoAlthough the article mentions Artificial Intelligence, their paper[1] never actually mentions that term, and instead talks about their machine learning techniques. AFAIK, ML for things like cell-segmentation are a solved problem [2]. [1] https://www.biorxiv.org/content/10.1101/2021.05.29.446289v4.full.pdf https://www.biorxiv.org/content/10.1101/2021.05.29.446289v4.... [2] https://www.ilastik.org/ https://www.ilastik.org/
- rmorey 3y agoThere are extremely effective techniques, but it is not really solved. The current techniques still require human proofreading to correct errors. Only a fraction of this particular dataset is proofread.
- bugbuddy 3y agoBased on the picture of a single neuron, the brain sim crowd should recalculate their estimates for the needed computing power again.
- brandonmenc 3y agoAnother proof point that AGI is probably not possible. Growing actual bio brains is just way easier. Its never going to happen in silicon. Every machine will just have a cubic centimeter block of neuro meat embedded in it somewhere.
- mr_toad 3y agoYou’d have to train them individually. One advantage of ANNs is that you can train them and then ship the model to anyone with a GPU.
- skulk 3y agoI agree, mostly because it's already being done! https://www.youtube.com/watch?v=V2YDApNRK3g https://www.youtube.com/watch?v=V2YDApNRK3g https://www.youtube.com/watch?v=bEXefdbQDjw https://www.youtube.com/watch?v=bEXefdbQDjw
- myrmidon 3y agoHard disagree on this. I strongly believe that there is a TON of potential for synthetic biology-- but not in computation. People just forget how superior current silicon is for running algorithms; if you consider e.g. a 17 by 17 digit multiplication (double precision), then a current CPU can do that in the time it takes for light to reach your eye from the screen in front of you (!!!). During all the completely unavoidable latency (the time any visual stimulus takes to propagate and reach your consciousness), the CPU does millions more of those operations. Any biocomputer would be limited to low-bandwidth, ultra high latency operations purely by design. If you solely consider AGI as application, where abysmal latency and low input bandwidth might be acceptable, then it still appears to be extremely unlikely that we are going to reach that goal via synthetic biology; our current capabilities are just disappointing and not looking like they are gonna improve quickly. Building artificial neural networks on silicon, on the other hand, capitalises on the almost exponential gains we made during the last decades, and already produces results that compare to say, a schoolchild, quite favorably; I'd argue that current LLM based approaches already eclipse the intellectual capabilities of ANY animal, for example. Artificial bio brains, on the other hand, are basically competing with worms right now... Also consider that even though our brains might look daunting from a pure "upper bound on required complexity/number of connections" point of view, these limits are very unlikely to be applicable, because they confound implementation details, redundancy and irrelevant details. And we have precise bound on other parameters, that our technology already matches easily: 1) Artificial intelligence architecture can be bootstrapped from a CD-ROM worth of data (~700MiB for the whole human genome-- even that is mostly redundant) 2) Bandwidth for training is quite low, even when compressing the ~20year training time for an actual human into a more manageable timeframe 3) Operating power does not require more than ~20W. 4) No understanding was necessary to create human intelligence-- its purely a result of an iterative process (evolution). Also consider human flight as an analogy: we did not achieve that by copying beating wings, powered by dozens of muscle groups and complex control algorithms-- those are just implementation details of existing biological systems. All we needed was the wing-concept itself and a bunch of trial-and-error.
- greentext 3y agoIt looks like spaghetti code.
- idontwantthis 3y ago> Jain’s team then built artificial-intelligence models that were able to stitch the microscope images together to reconstruct the whole sample in 3D How do they know if their AI did it correctly or not?
- sgt101 3y agothey don't and talk about the difficulties in their paper. I found it refreshing to see the standard of frankness and openness in how they address this. But - it's all pretty compelling and will surely prompt and sustain a lot more research investigating these results and data and also creating more in the future.
- dvfjsdhgfv 3y agoWhy do these neurons have flat "heads"?
- ewchris 3y agoEdge of the dataset.
- nakedneuron 3y ago> the model showed neurons with tendrils that formed knots around themselves I wonder if this plays into the mechanism of epilepsy. Self-arousal...? Anybody qualified to comment on?
- seesawtron 3y agoThis tissue was from epileptic patient. So we can not rule out that weird things are related to disease.
- bluenose69 3y agoNeal Stephenson has a good novel that deals with this (https://en.wikipedia.org/wiki/Fall;_or,_Dodge_in_Hell https://en.wikipedia.org/wiki/Fall;_or,_Dodge_in_Hell)
- ein0p 3y agoAs important and impressive a result as this is, I am reminded of the cornerstone problem of neuroscience, which goes something like this: if we knew next to nothing about processors but could attach electrodes to the die, would we be able to figure out how processors execute programs and what those programs do, in detail, just from the measurements alone? And now scale that up several orders of magnitude and introduce sensitivity to timing of arrival for signals, and you got the brain. Likewise ok, you have petabytes of data now, but will we ever get closer to understanding, for example, how cognition works? It was a bit of a shock for me when I found out (while taking an introductory comp neuroscience course) that we simply do not have tractable math to model more than a handful neurons in time domain. And they do actually operate in time domain - timings are important for Hebbian learning, and there’s no global “clock” - all that the brain does is a continuous process.
- golergka 3y agoParticle physics works in a similar way, but instead of attaching electrodes, you shoot at them with guns and then analyze trajectories of the fragments.
- spacetimeuser5 3y agoThe cheap monkey headset works in a similar way: monkeys just essentially continue to analyze trajectories of medieval cannon balls in the LHC and to count potatoes in the form of bytes.
- spacetimeuser5 3y ago>> The sample was immersed in preservatives and stained with heavy metals to make the cells easier to see. Try experimenting with immersing your brain in preservatives and staining with heavy metals to see how would you be able to write the comment similar to the above. No wonder that monkey methods continue to unveil monkey cognition.
- dudeinjapan 3y ago> Try… immersing your brain in preservatives and staining with heavy metals I think we all do every day
- spacetimeuser5 3y agoYea, and at the Plank's scale resolution as a logical extension of the nanoscale with their "modern" measurement methodology this cheap monkey headset just disintegrates, haha.
- NexusMethod 3y agoAfter reading through all comments as of 2024/05/11 I (as a professor at some major university) am quite surprised that not one single comment has asked the obvious question (instead of dishing out loads of (partial) "textbook knowledge" about brain functions, the difference between mammals and birds, AI and LLM etc.), which would be: what do all those strange structures and objects do which we know nothing about whatsoever? Have a look: https://h01-release.storage.googleapis.com/gallery.html https://h01-release.storage.googleapis.com/gallery.html I count seven.
- spacetimeuser5 3y agoThese data do not have a control group of healthy similar tissue samples, thus are not falsifiable, and are not 100% scientifically valid.
- ben_w 3y agoNeat, thanks. As a complete outsider who doesn't know what to look for, the dendrite inside soma (dendrite from one cell tunnelling through the soma of another) was the biggest surprise.
- dmarble 3y agoI'm in awe at the complexity and unknowability of it all, but I also have to chuckle at the thought that some portion may be vestigial. I'm particularly fond of the "Egg shaped object with no associated processes". :)
- deleted 3y ago[deleted]
- Terretta 3y agoSure, if you think of it as an egg, instead of as a galaxy of electrons and atoms so dense as to have structure big enough for us to give it the label "egg shaped object". What if it's a "wireless" device?
- NexusMethod 3y agome too! :-)
- sgt101 3y agoI was really interested to see that a single neuron has 1000's of exciting connections and 1000's of inhibitory connections. I know that this is a gross feature but it's a reminder of just how distant NN models are from the biological reality.
- ninashamsi 3y agoI developed algorithms for a Neuroglancer fork which annotates unlabeled volumetric data via self-supervised learning. I am looking forward to more developments in large scale brain mapping projects :) More project details: https://www.ll.mit.edu/sites/default/files/other/doc/2023-02/TVO_Technology_Highlight_45_NeuroTrALE.pdf https://www.ll.mit.edu/sites/default/files/other/doc/2023-02...