An adult fruit fly brain has been mapped
economist.com
economist.com
> There's lots of very exciting work going on around the fully mapped fruit fly connectome. For example, I'm a CTO of a stealth startup that aims to do for utilitarianism what carbon credits did for environmentalism. We are selling 'utility credits' which translates directly into us simulating trillions and trillions of fruit fly brains in a state of constant orgasmic bliss, which you can then buy to offset any actions your company has undertaken that damage global happiness or well-being. We've seen a lot of interest from some pretty large industry players.
Karma Kredits
Perhaps a co-branding opportunity with Krispy Kreme donuts?
Buy a donut, and experience some Joy, which handily comes with Karma Kredits to offset that Joy.
Have some KK with the KK!
So this kind of scenario has to be carefully taken into account in favor of scenarios which lead to actual emission reductions. It's not a simple fungible asset or commodity as some (naively) assume.
These unregulated credits get bought by heavy consumers in greenwashing
The vast majority of carbon credits issued are fraudulent:
> Revealed: more than 90% of rainforest carbon offsets by biggest certifier are worthless, analysis shows
https://www.theguardian.com/environment/2023/jan/18/revealed...
> A Shell-operated plant reported millions of carbon credits tied to CO₂ removal that never took place but were used by Canada’s largest oil sands companies
https://www.ft.com/content/93938a1b-dc36-4ea6-9308-170189be0...
I do think, in some cases, there is such a thing as thinking being equivalent to simulating (e.g. a calculator) but this isn't one of those.
connectome isn't a dead end but it doesn't solve all known problems. It's like making a static map which you can then use to inspect all those cars driving around (the dynamics) and crashing (the interactions).
[edit: I forgot to mention that neuron growth in adults (across many species) is still a controversial topic; see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7554932/ for some commentary on the challenge in fly; https://en.wikipedia.org/wiki/Adult_neurogenesis for commentary on the larger problem ]
There are systems at play that form the brain into what it is and we don’t know much about them. The individual neurons — we have a better understanding of, but not the emergent systems. Now that many more scientists will know what the target for these systems is — what is the brain they shape, we can start to understand the control and feedback loops that result in this snapshot state of the brain.
And that’s why it’s not a dead end. Just because it doesn’t immediately give some sort of a consumer product, doesn’t mean it’s not a step forward.
> So, to run the same [fMRI, NIRS,] stimulus response activation observation/burn-in again weeks or months later with the same subjects is likely necessary given Representational drift
And isn't there n-ary entanglement?
It is like getting a static map of the country's roads with no cars on it.
You can not make it come alive with cars (activity), but you can infer where people need to drive but you don't know when and why they drive or what they are doing, but it is a major clue.
I was thinking it was more like giving somebody iPhone schematics and die shots of all the chips and then asking them to figure out how Portrait Mode works in the Camera app.
It's unlikely that brains have an abstraction layer like that, so work like this is a necessary precondition to understanding the rest of how it works. That actual understanding may be elusive for quite some time to come, but without a connectome, forget it, no change.
Luckily this is science so we can actually find out.
And maybe there’s some data or concept that will one day be discovered that will be the key to unlocking how brains work.
For my analogy, I was thinking more of how the connectome is, like schematics, static and the dynamic part is probably more interesting.
One of the main open questions in neuroscience right now is how network structure, dynamics, and function are related in the brain. Connectomes provide tremendous insight into structure, but as mentioned this does not generically solve either the dynamics or function problem. For example, for many of these neurons we don't have a good understanding of their input-output relationship, and the nature of this relationship can strongly affect the dynamics that emerge in a highly connected network. Individual variability across connectomes, and how connectomes change over development are also a significant issue, but at least for the fly it's thought that many of the basic structures are pretty conserved across adult animals, even if many of the details could differ.
Modulo these caveats, knowing the physical network structure of the brain does still impose huge constraints on what kinds of models we should be using for gaining insight into dynamics and function. For example, there are well known areas (the "mushroom bodies") with specific feed-forward connectivity patterns that are very different from a random recurrent network. Further, there are at least some areas in the fly brain where we think there are indeed quite clean structure-function relationships, e.g. in the central complex of the fly brain, which contains a physical ring of neurons and is thought to support a "bump" of activity that acts as a sort of compass that helps flies navigate via a ring-attractor-like dynamical system. Thus, even though it has many missing pieces, a wiring diagram like this can be tremendously useful for generating hypotheses to guide more targeted experiments and theoretical studies.
[1] https://openworm.org/assets/OpenWormPoster_Celegans_Glasgow_...
No need to treat research like a business.
Never heard a single story like this
"A Turning Point in Cancer Research: Sequencing the Human Genome" - https://www.science.org/doi/10.1126/science.3945817
Even without that I'm not sure why you think that's a good point — it's very easy to find serendipitous examples in medicine in general, e.g. viragra which was initially a heart treatment, or even thalidomide whose anti-cancer uses were suggested by the very birth defects that made it infamous.
Specifically cancer research finding other things by accident:
"Cancer researchers accidentally discover ‘cure’ for baldness, gray hair" - https://technology.inquirer.net/62453/cancer-researchers-acc...
"Cancer Researchers Accidentally Discover New Nylon Process" - https://www.popularmechanics.com/science/health/a8135/cancer...
[1] https://theonion.com/scientists-don-t-get-mad-but-we-acciden...
Tho tbf when I've joked about that with str8 friends they get really upsetti spaghetti, yet somehow still can't link their spaghettiness to why I was offended when they said "just don't act gay" when I said I would love to see Egypt but didn't want to travel there.
in the meantime, here's a simple tool paper we wrote explaining how you can treat this like a cool graph database challenge [1] and a preprint showing how you could approach that question when your number of samples per animal is close to N=1 [2]. basically..... it's hard! but also.... it's cool!
[1]: https://www.nature.com/articles/s41598-021-91025-5 [2]: https://www.biorxiv.org/content/10.1101/2023.10.16.562590v1....
There's obviously something to it or implementing what we map in software wouldn't give results as accurately as they do.
But it's the flow of information as signals pass through nodes where everything actually happens.
A human brain has about 86 billion neurons and quite likely many trillions of synapses and that is likely an underestimate. That 3 million edits will turn into 3 million * 10^6 at least manual edits, that doesn't seem feasible. The error rate on the fruit flu would have to come down into the single digits to be usable to map a human brain. So an improvement from about 6% of synapses to 0.000006%. That is one heck of a jump in improvement for an AI.
There's easily a century between the earliest accurate map of Edinburgh and the earliest accurate map of the world. And even at present, the accuracy of maps of Edinburgh is much greater than the accuracy of maps of the world.
So yeah, the whole world could be next. But the person you're replying to has a point when they say significant improvements are needed.
I don't understand all this rushing and skepticism when such amazing science is being done. It's not like some AI company marking claims to sell a product, it's some researchers trying to accomplish something. Yes, they should (and probably will) do it better but that's not the goal here.
If 3 million manual edits are still doable then it's ok. And when the manual step is not feasible, a jump in the tech will be required.
To put a fine point on the difference in scale:
Edinburgh[0]: 264 square km
Earth[1]: 510,000,000 square km
0 - https://www.britannica.com/place/Edinburgh-Scotland
1 - https://www.universetoday.com/25756/surface-area-of-the-eart...
A toy model compared to real neurons but a good starting place with nice results. We could identify the solution that most robustly reproduced the firing patterns even in the presence of noise.
I would be curious how well the connectome documents connection and dendrite/axon geometry, beyond connection paths. For shedding light on behavior related to connection strengths, timing, neuron firing sensitivities, etc. For the stable non-learning model as captured at scanning time.
To investigate adaptation purpose & behavior, it helps to understand what operational behavior has been learned.
Whole-brain connectome of the fruit fly (2023) https://news.ycombinator.com/item?id=36568609
Whole-brain connectome of the fruit fly - https://news.ycombinator.com/item?id=36568609 - July 2023 (94 comments)
The connectome of an insect brain by Winding et al. - https://news.ycombinator.com/item?id=35112234 - March 2023 (1 comment)
Map of an Insect’s Brain - https://news.ycombinator.com/item?id=35111371 - March 2023 (119 comments)
The Connectome of an Insect Brain - https://news.ycombinator.com/item?id=35094565 - March 2023 (1 comment)
The first wiring map of an insect's brain hints at incredible complexity - https://news.ycombinator.com/item?id=35089298 - March 2023 (5 comments)
Fruit Fly Brain Map - https://news.ycombinator.com/item?id=29672565 - Dec 2021 (1 comment)
Structure of Fruit Fly Brain (2018) - https://news.ycombinator.com/item?id=26474430 - March 2021 (7 comments)
Google publishes largest ever high-resolution map of brain connectivity - https://news.ycombinator.com/item?id=22124888 - Jan 2020 (1 comment)
Explore the the adult fruit fly brain - https://news.ycombinator.com/item?id=20015218 - May 2019 (1 comment)
To detect new odors, fruit fly brains improve on a well-known computer algorithm - https://news.ycombinator.com/item?id=18656016 - Dec 2018 (1 comment)
A Complete Electron Microscopy Volume of the Brain of Adult Fruit Fly - https://news.ycombinator.com/item?id=17590910 - July 2018 (50 comments)
Fruit Fly Brain Hackathon 2017 – Brain Circuit, Memory and Computation - https://news.ycombinator.com/item?id=13692166 - Feb 2017 (13 comments)
Neurokernel: Emulating the Fruit Fly Brain - https://news.ycombinator.com/item?id=9284802 - March 2015 (8 comments)
An open source platform for emulating the fruit fly brain - https://news.ycombinator.com/item?id=8377600 - Sept 2014 (17 comments)
Maybe also throw in:
Six Nobel prizes – what’s the fascination with the fruit fly? - https://news.ycombinator.com/item?id=15463522 - Oct 2017 (16 comments)
Fruit fly nervous system: new solution to fundamental computer network problem - https://news.ycombinator.com/item?id=2103668 - Jan 2011 (13 comments)
Map of an Insect’s Brain - https://news.ycombinator.com/item?id=35111371 - March 2023 (119 comments)
Figuring out the behaviour of the neurons could take decades, although I have no doubt that people will eventually. And simulating a whole fruit fly body seems like it’s going to be out of reach for a very long time.
It has approximate weights. Neuron connection strength is determined by the number of synapses (1-100s, sometimes 1000s), the type of synapse neurotransmitter, and the number of receptors. The connectome has 1 and 2 and is only missing 3. The number of receptors may not even be that important- the fact that the number of synapses is important may well mean the number of receptors is unreliable.
Neurons also don't transmit scalars to each other. The synapse is stimulate by frequency of action potentials much more than strength.
> And it doesn’t have a body.
It does have nervous connections outside the brain. That behavior is not as complex.
> Figuring out the behaviour of the neurons could take decades
Neurons are not that complex in terms of matching in->out behavior. Since spiking is frequency-based, you can verify it quite well by ensuring the frequency of spikes in->out matches; you can even measure single neurons with implanted electrodes. You don't need so much precision to see individual spikes, since the size of the spikes does not matter much at all.
Long term potentiation also makes measuring individual neuron strength even less important- if you model potentiation correctly, then over time you'll converge accurately as understimulated connections weaken and vice versa.
The real issue is we have barely any clue how potentiation works and can't model it well at all. It's very important to brain behavior and most of the interesting things brains do. Its kind of an issue.
But the astrocytes are dynamically modulating the signal at the synapse, it doesn't seem like we really know "the" weight.
It's also not just frequency, but "shape" (for lack of a better word) of incoming inputs that matters, as such there is a very wide variety of spiking patterns that certain cells exhibit, like chopper cells.
As such, unlike the vast majority of emulated humans, the emulated Miguel Acevedo boots with an excited, pleasant demeanour. He is eager to understand how much time has passed since his uploading, what context he is being emulated in, and what task or experiment he is to participate in.
...
MMAcevedo's demeanour and attitude contrast starkly with those of nearly all other uploads taken of modern adult humans, most of which boot into a state of disorientation which is quickly replaced by terror and extreme panic. Standard procedures for securing the upload's cooperation such as red-washing, blue-washing, and use of the Objective Statement Protocols are unnecessary. This reduces the necessary computational load required in fast-forwarding the upload through a cooperation protocol, with the result that the MMAcevedo duty cycle is typically 99.4% on suitable workloads, a mark unmatched by all but a few other known uploads. However, MMAcevedo's innate skills and personality make it fundamentally unsuitable for many workloads.
The large-scale architecture will be roughly the same between any two individuals. You would likely need some sort of mapping (like an embedding) to generalize. It's definitely an active area of research.
Then you analyze the slice images and determine the neurons and their connection. This is the hard part, and the breakthrough is an AI based method.
Pretty sure they've only mapped one brain so far.
In practice, I suspect there's a fair bit of grad student manual labor that keeps the pipeline flowing...
Happened to go for a walk with the corresponding author and made her repeat this fact for me.
Looking in the paper more closely they say: """After matching, Schlegel et al.12 also compared our wiring diagram with the hemibrain where they overlap and showed that cell-type counts and strong connections were largely in agreement. This means that the combined effects of natural variability across individuals and ‘noise’ due to imperfect reconstruction tend to be modest, so our wiring diagram of a single brain should be useful for studying any wild-type Drosophila melanogaster individual. However, there are known differences between the brains of male and female flies46. In addition, principal neurons of the mushroom body, a brain structure required for olfactory learning and memory, show high variability12. Some mushroom body connectivity patterns have even been found to be near random47, although deviations from randomness have since been identified48. In short, Drosophila wiring diagrams are useful because of their stereotypy, yet also open the door to studies of connectome variation."""
i woudl expect the overall architecture to be the same, but not the cell identities or the connections. But as always, I'm happy to be shown wrong with facts.
You'd need to inspect the paper, the supplementals, and the website closely to determine exactly which files are interesting.
i'm not hosting this dataset specifically, but check out https://bossdb.org/. my disclaimer and also my brag is that this is my job and research area :) if you're looking for a copy, let's talk! there are easy ways and hard ways :)
They have a cool website that lets you browse the data: https://codex.flywire.ai/
For example, after the connectome of the worm were finished, despite it being quite small, for many years it proved to be impossible to simulate the dynamics, because of so many unknown parameters.
This was one of the criticisms that the opponents of connectomics have always brought up. "You spend a lot of money that could have been used for other research, but in the end you do not get a true insight into how the brain really works." For the researchers who thought that knowing all the connections was important, it was an uphill battle to overcome such attitudes.
But one has to start somewhere -- like a genome, the connectome is not the whole story, but it is a very important part of it, on which many other advances can be built up.
Apparently they have been able to simulate dynamics with the fruit fly connectome(?) [0]:
> researchers used the connectome to create a computer model of the entire fruit-> fly brain, including all the connections between neurons. They tested it by activating neurons that they knew either sense sweet or bitter tastes. These neurons then launched a cascade of signals through the virtual fly’s brain, ultimately triggering motor neurons tied to the fly’s proboscis — the equivalent of the mammalian tongue. When the sweet circuit was activated, a signal for extending the proboscis was transmitted, as if the insect was preparing to feed; when the bitter circuit was activated, this signal was inhibited. To validate these findings, the team activated the same neurons in a real fruit fly.
This is certainly very cool. But as the authors themselves point out [1], much more work remains to be done to reproduce more subtle features of the dynamics of the system.
The connectome for the C. elegans nematode was mapped in the 1980s and the OpenWorm project has successfully simulated all non-neuronal cells. But they are very far from simulating the brain abd it will take decades of experimental work to understand C. elegans's brain - it's very difficult to observe a living brain in the required molecular detail.
There's plenty of value to knowing where the datacentres are and which regions are active under which circumstances, but none of that is telling you what the internet is thinking...
But the challenges are substantial, and these imaging techniques (mostly optical, not MRI/etc) depend on the simplicity of C. elegans: its brain is essentially a thin disk, with only 300 neurons in its entire nervous system, and it is surrounded by a transparent membrane. I am not sure how these techniques could possibly extend to something with a thick exoskeleton like Drosophila. And there are great difficulties keeping track of just the 300 neurons in a moving nematode with its own unique brain; it seems completely intractable with current tools to extend the complexity 50x, especially since fruit flies move far more rapidly and have far more individual variation.
It this like knowing only this:
which neuron is connected to which neuron
But you don't know:
the values of the weights (the value of the neuron, or the parameters)
the activation functions
what circuit do neurons implement (fully connected? CNN?)
However the real challenge would be:
1. bring this mapping into a AI framework for inferencing 2. We don't know the "OS" on how it runs. Just randomly triggering a neuron probably wouldn't work as there is a lot of other factors that trigger neurons.
yes. and you can get VERY roughly connection strengths by synapse count but that's as far as you can go
Also, “activation function” isn’t exactly the right thing for real biological neurons. They aren’t just functions of the current input or the like. Their behavior depends on their recent history. Some will like, by themselves iirc, periodically fire. Others will fire if enough input is sent within some amount of time (in some models of some of them there’s like, some accumulations of signal when receiving inputs, which gradually decays/leaks, and it fires (and depletes) if enough is accumulated).
But yes, the idea is that “what is connected to what” is obtained, but not more specific things about how the ones that are connected are connected (how the behavior of one relates to the behavior of the ones it is connected to).
Curious what a 'fly brain map' looks like - iss the download a 3D model, or a matrix with values for attributes?
Sure, the connectome isn't the whole story, but I think it possible that a few hundred years from now we might understand the brain sufficiently to simulate it, and then by inputting this connectome, together with guesses/approximations for the information not captured, that person is effectively time-travelled.
Could be amazing for archaeologists - rather than looking at broken bits of pottery and guessing what they're used for, you could literally just ask someone (a brain) from that time.
With a connectome, you can make hundreds of copies of the data round the world. With pickled-brain-in-a-jar you better make sure that jar is well hidden in a dusty basement for long enough to not get chucked out, but not so well hidden future generations never find it.
(eg. the Kurzweils in 2016 https://www.pcmag.com/articles/how-ray-kurzweil-and-his-daug...)
I don't mean to be facetious - I'm struggling to to see what other consideration this helps with.
The amount of data may mean we have to wait for Moore's Law to keep improving things for a while though.
In this context, what matters is "how many operations can I get done for a dollar?", and that's still very much improving very fast, albeit not quite as fast as before.
https://cap.csail.mit.edu/death-moores-law-what-it-means-and....
I’m not making this stuff up.
Dennard scaling is long dead, as is the clock frequency race; but features are still slowly getting smaller (your own link says so), as is energy consumption per operation. The latter, J/op, is the critical issue for big data centres. Brains are obviously better than transistors at this, and IIRC by that measure transistors are still getting roughly twice as good every 2.6 years.
From the link: "Although miniaturization is still happening, the Moore’s Law standard of doubling the components on a semiconductor chip every two years has been broken"
I'm not saying things aren't getting smaller. I'm saying moores law is broken.
And I'm saying that Moore's original statement was already broken by 1975.
And that the whole phrase means loads of different things that Moore never actually said, and the one of those which matters here is still true.
That’s from the link. I largely agree. Colloquially it’s over after 2016. Any other interpretation is too pedantic imo.
(Well, dollars per operation, but assuming energy costs are fixed…)
Surely a daunting task, but depending on the tools used to create the smaller map, possibly a realistic one. Maybe with a bit of a less precision.
There's something called Connectome Annotation Versioning Engine (CAVE). Which appears to be software(?) which allows researchers to examine a dataset and annotate it in some way. Presumably the dataset consists of images of the neurons themselves and the job is to map which neurons touch which other neurons? That's the thing I am not understanding. How do they get such images in the first place?
CAVE is mentioned along with electron microscopy... but I don't understand how an electron microscope can be useful here. Obviously, it's not TEM (which required a very flat specimen). Then, there's SEM, but doesn't that require a conductive sample? In both cases, any electron microscope requires a vacuum to even work, right? How can this be done with something so wet, fragile and 3 dimensional like the brain of a fruit fly? Even worse, the connections are stacked on top of each other. How can an electron microscope image below the surface?
TLDR; How is it possible to even image the way the neurons are connected in the first place? ELI5?
The sections can be imaged with TEM or SEM in high vacuum, the block face can be imaged with SEM.
The resulting 3d volume can be segmented with neural networks.
CAVE is for manual editing / correction on top of the automatically generated segmentation.
No.
Many unknowns and even more being discovered regularly (e.g. tunneling nanotubes connecting neurons dynamically)
Inference from structure to dynamics in a brain is several orders of magnitude less plausible than inferring from a record of local weather reports to simulating actual weather patterns.
Maybe a better analogy would be inferring from Grey's Anatomy to the regulatory dynamics of proteins at the cellular level in vivo (although I think that might actually be easier?)
Openworm still hasn't succeeded.
Get there and its full of flies.
Keeps your virtual landlord happy... Landlord of the Flies, if you will.
We're here at "Systems level sketch of a fruit fly brain". It's incredible work! But as other comments detail, there is far more to the function of a fly brain than this "map". It's quite a long way from "Deep understanding of a Human Brain, to the point where we can begin engineering a replica".
Maybe we'll get lucky, and find that "Neural Network" techniques really are a pathway to Intelligence in a broad sense. But without some mechanistic understanding of Biological Intelligence, it seems no better than betting on the Numbers in roulette.