[HN Gopher] An adult fruit fly brain has been mapped
___________________________________________________________________
An adult fruit fly brain has been mapped
Author : teleforce
Score : 557 points
Date : 2024-10-02 16:16 UTC (1 days ago)
(HTM) web link (www.economist.com)
(TXT) w3m dump (www.economist.com)
| tonetegeatinst wrote:
| Datahoarder question but can I download the map of the fly?
| ijxjdffnkkpp wrote:
| I think that releasing the map on torrent would be a useful
| idea as well. This fly could end up like the lobsters in
| Accelerando. In that book the mapped animal is lobsters and
| they get first mover advantage on some post-scarcity type
| things. Getting them to the Internet would be a good first
| step, IMO.
| rbanffy wrote:
| I, for one, welcome our new fruit fly overlords.
| dekhn wrote:
| See the FAQ: https://codex.flywire.ai/faq which leads to the
| API for access: https://codex.flywire.ai/api/download
|
| You'd need to inspect the paper, the supplementals, and the
| website closely to determine exactly which files are
| interesting.
| andbberger wrote:
| the raw data will be on the order of PBs
| rmorey wrote:
| the EM dataset for this connectome, FAFB, is only a few
| hundred TB. as a rule of thumb volume electron microscopy
| datasets are on the order of 1 PB / cubic millimeter, and the
| fly brain is much smaller than 1 mm3
| andbberger wrote:
| a few hundred TB is on the order PBs
| j6m8 wrote:
| raw data is O(petabytes) (single-digit); synapse-neuron graph
| will be probably order 100GB. But you also want morphology and
| locations, since it's not enough to just say "X connects to Y"
| if you want to know about dynamics!
|
| 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 :)
| mjburgess wrote:
| It was my understanding that all this connectome-based research
| was largely a deadend, because it doesnt capture dynamics, nor a
| vast array of interactions. if you've ever seen neurones being
| grown (go search YT), you'll see it's a massive gelatinous
| structure which is highly plastic and highly dynamic. Even in the
| simplest brains (eg., of elgans), you get 10^x exponential growth
| in number of neurones and their connections as it grows.
| dekhn wrote:
| This is done agaist an adult so all the neurons have already
| grown.
|
| 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 ]
| caseyy wrote:
| Giving scientists access to the connectome snapshot alone is
| very exciting. The first step to understanding _why_ something
| is and _how_ it came to be is seeing _what_ it is.
|
| 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.
| westurner wrote:
| From https://news.ycombinator.com/item?id=35877402#35886145 :
|
| > _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?
| tgbugs wrote:
| You don't get the dynamics from connectomes, but you absolutely
| need them. So it isn't that they are a dead end, it is that the
| dynamics by themselves are also insufficient and the connectome
| is insufficient, you need both. Further, if you want to
| actually be able to have anything to attach the dynamics to,
| you need the cellular anatomy, so connectomes are absolutely
| necessary. The fact that connectomes are insufficient does not
| mean that such research is a dead end, but rather that the
| prerequisites for understanding the nervous system are vastly
| more complex and demanding than some might have hoped.
| bhouston wrote:
| It is useful.
|
| 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.
| criddell wrote:
| > It is like getting a static map of the country's roads with
| no cars on it.
|
| 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.
| bhouston wrote:
| Yup, it is similar to that as well. It is a part of the
| puzzle definitely, but not at all the whole picture.
| samatman wrote:
| Sort of, but mostly not. The critical distinction is that,
| given better data (the instruction set, the source code or
| binary of the OS and camera app), the schematics and die
| shots aren't necessary or even useful.
|
| 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.
| snorin wrote:
| Why exactly would it be unlikely?
| Retric wrote:
| It would be really inefficient and neurons inherently
| provide a great deal of flexibility. Larger animals might
| use this kind of thing, but insects don't have that many
| neurons to work with.
|
| Luckily this is science so we can actually find out.
| criddell wrote:
| > given better data
|
| 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.
| falcor84 wrote:
| The difference is that in the brain there's no real
| separation between hardware and software, so I'm your
| analogy, we also have the equivalent of the source code,
| but just maybe not the environment configuration needed to
| get it to run (nor would we at this stage have sufficient
| compute to fully run it).
| v-erne wrote:
| Any man made hardware is rather too organized to be good
| analogy here. But we have better alternatives than came
| along recently - LLMs or any kind of AI models as a
| matter of fact. Personally I would use analogy of "try
| running a prompt locally and then explain what really
| happened inside in terms of CPU operations" :)
| coldpie wrote:
| Analogies are like banana peels. Rarely useful and they
| break down pretty quickly.
| pazimzadeh wrote:
| the metaphor I've heard is it's like getting a map of the
| country's roads, but none of the signs are labelled.
| rkp8000 wrote:
| Connectome-adjacent neuroscientist here. Definitely not a dead
| end! But also definitely not the whole picture.
|
| 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.
| Animats wrote:
| How's Open Worm coming along? The connectome of C. Elegans
| has been known for years, and Open Worm tries to simulate it.
| [1] Not with enormous success.
|
| [1] https://openworm.org/assets/OpenWormPoster_Celegans_Glasg
| ow_...
| xandrius wrote:
| Like everything in science: we don't know until we know.
|
| No need to treat research like a business.
| cafebeen wrote:
| Budgets are finite, and most science funding involves
| some decision making about how to allocate resources.
| bboygravity wrote:
| And you can't know where to allocate resources best until
| after the science is done (unless a field/group is known
| to scam).
| xandrius wrote:
| Although for what we know now, we definitely can't
| understand the territory without a map.
| consf wrote:
| Funding agencies often have to prioritize projects that
| show potential
| mr_toad wrote:
| It's a non-profit volunteer run project. People spend
| more money on stamp collections.
| beefnugs wrote:
| You know you would have thought all the years and years
| of "donations" to "cancer research" there would be
| constant news stories about how we accidentally cured a
| bunch of ancillary medical problems, and wow its all free
| to everyone because it was from donations!
|
| Never heard a single story like this
| ben_w wrote:
| Human Genome Project and everything derived from it.
| IIRC, that was originally proposed as a cancer research
| project:
|
| "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/healt
| h/a8135/cancer...
| dkarbayev wrote:
| Was there any "productionization" of the "cure" for
| baldness & gray hair, after it was discovered 7 years
| ago? I reckon, there's a huge market for that cure.
| hwillis wrote:
| https://perfecthairhealth.com/why-krox20-isnt-a-miracle-
| hair...
| Animats wrote:
| "We were just doing cancer research and ... we
| accidentally found the cure for homosexuality" (The
| Onion)[1]
|
| [1] https://theonion.com/scientists-don-t-get-mad-but-we-
| acciden...
| consf wrote:
| Research thrives on curiosity
| SubiculumCode wrote:
| Very Nice. --from a Connectome-Centric neuroimager :) One
| technique that I am pursuing right now is information
| decomposition of timeseries to separate the mutual
| information of two timeseries into redundant and synergistic
| informational atoms (synnergystic here means the degree to
| which knowing both timeseries gives you more information than
| the individual parts give (more than sum of parts). The big
| limitation of the method is the geometric explosion in
| complexity of the decomposition as the number of time series
| grow, with most analyses being limited to two or three times
| series at a time. However, the scale of the data on which it
| is applied is not requisite, meaning the approach can equally
| be used on the mutual information between two regions of
| interest in rsfMRI , or two spiking timeseries from
| individual neurons. https://en.wikipedia.org/wiki/Partial_inf
| ormation_decomposit...
| taneq wrote:
| Thanks for your insight! How repeatable are these structures
| between individual animals? Are they very similar or is it
| more like "here's a feed forward kinda bit, here's a toroidal
| bit, and over here it's just a mess"?
| j6m8 wrote:
| preprint coming out soon about this specifically :)
|
| 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/10.1101/2023.10.16.562
| 590v1....
| consf wrote:
| It helps inform models for further exploration, if I
| understand correctly
| mjfl wrote:
| Connectome is a necessary component to understanding dynamics.
| dboreham wrote:
| You just need to supply your own training data.
| ruthmarx wrote:
| > It was my understanding that all this connectome-based
| research was largely a deadend,
|
| There's obviously something to it or implementing what we map
| in software wouldn't give results as accurately as they do.
| lawrenceyan wrote:
| Connectomes are like a static graph of a neural network.
|
| But it's the flow of information as signals pass through nodes
| where everything actually happens.
| andbberger wrote:
| it's a tool in the toolbox. useful for mapping things out when
| doing functional experiments
| Nahtnah wrote:
| https://www.sciencedirect.com/science/article/pii/S089662732...
|
| https://www.biorxiv.org/content/10.1101/2020.12.22.423967v1
| AlexCoventry wrote:
| That's a bit like saying that sequencing the genome was a dead
| end, because it doesn't capture the molecular biology of the
| encoded proteins. Assuming the connectome is accurate, it's a
| major advance in our knowledge of neuroanatomy.
| fabiensanglard wrote:
| What does it mean "mapped". Does it mean we know what each
| nerve/axon does?
| CobrastanJorji wrote:
| It's my (layman) understanding that it's more or less a wiring
| diagram. Synapse #8217492 connects neuron #27472 and neuron
| #27865. It's a graph with 140,000 nodes (neurons) and 54.5
| million edges (synapses). And then some labels for them like
| neurotransmitter type, which class of brain operations they're
| associated with, its size and position in 3D, etc.
|
| They have a cool website that lets you browse the data:
| https://codex.flywire.ai/
| mont_tag wrote:
| Is the data such that it can be modeled in software?
| dekhn wrote:
| Yes. One example:
| https://www.biorxiv.org/content/10.1101/2024.03.11.584515v1
| GoblinSlayer wrote:
| https://github.com/Flowx08/Celegans-simulation
| tsimionescu wrote:
| Depends what you mean by "modeled". You can probably create
| a visualization of it, but the data doesn't include any
| information about the dynamics of the system, how the
| neurons behave. So, you can't "simulate a brain" to any
| extent with this data, if that's what you were thinking.
| generuso wrote:
| Unfortunately, not. We get the graph of the connections, but
| there are tons of essential parameters that are not captured.
| Such as the synaptic weights, the complex non-linear dynamics
| of the real neurons, their intricate modulation by various
| chemicals, etc.
|
| 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.
| bryan0 wrote:
| > 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.
|
| 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.
|
| [0]: https://www.nature.com/articles/d41586-024-03190-y
| generuso wrote:
| The researchers have taken a very simple idealized
| mathematical model of a neuron, assumed that all synaptic
| weights were the same, ignored modulation, ignored base
| level inhibitory inputs, and have shown that even in such a
| crude setting, for some important inputs (especially for a
| taste of sugar) the "logic" of how the inputs result in the
| activation of certain outputs still works, based on the
| connectome information alone.
|
| 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.
|
| [1] https://www.nature.com/articles/s41586-024-07763-9
| ChumpGPT wrote:
| http://archive.today/vBUjt
| purplejacket wrote:
| Thanks
| droideqa wrote:
| There is this interesting past post:
|
| Whole-brain connectome of the fruit fly (2023)
| https://news.ycombinator.com/item?id=36568609
| dang wrote:
| Thanks! Macroexpanded:
|
| _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)
| idrios wrote:
| Another for you:
|
| Map of an Insect's Brain -
| https://news.ycombinator.com/item?id=35111371 - March 2023
| (119 comments)
| dang wrote:
| Inserted. Thank you!
| mrguyorama wrote:
| Out there question: Do you have a hand crafted database of
| these setup or some sort of macro to take the output of the
| search api and form it like this, or are you hand editing
| these lists?
| pvg wrote:
| That question is so in there, there is also a list of many
| of the answers
|
| https://news.ycombinator.com/item?id=35668525
| qafy wrote:
| "human brains could follow" feels like a few jumps ahead? a fruit
| fly has on the order of 100k neurons, a human brain has on the
| order of 100 billion neurons. that's 6 orders of magnitude
| larger. that's like saying "A map of San Francisco has been
| completed, the entire solar system could follow!"
| IshKebab wrote:
| Well assuming the same density it's "only" 100 times bigger in
| linear dimensions. Doesn't sound quite as crazy...
| twojacobtwo wrote:
| Isn't that just saying "if you take the cube root of the
| number, it's a smaller number"?
|
| I don't mean to be facetious - I'm struggling to to see what
| other consideration this helps with.
| svara wrote:
| The physical process of cutting. We're physically
| sectioning 3 dimensional blocks of tissue.
| cloudripper wrote:
| I thought it was intended as more of a pun on questionable
| displays of human intelligence.
| BurningFrog wrote:
| The method used seems like it would work as well on bigger
| brains.
|
| The amount of data may mean we have to wait for Moore's Law to
| keep improving things for a while though.
| tsimionescu wrote:
| The method used required 3 million manual human corrections.
| Even if Moore's Law actually still meant anything for compute
| power, this is still many orders of magnitude from scaling to
| a human brain.
| ninetyninenine wrote:
| Moores law ended.
| ben_w wrote:
| Depends which of the many similar but subtly different
| things with that name was meant.
|
| 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.
| ninetyninenine wrote:
| It applied to transistor density and it's over. Its
| completely and utterly true and it's agreed upon by
| experts.
|
| https://cap.csail.mit.edu/death-moores-law-what-it-means-
| and....
|
| I'm not making this stuff up.
| ben_w wrote:
| The original formulation was "The complexity for minimum
| component costs has increased at a rate of roughly a
| factor of two per year", which stopped being true almost
| immediately and very soon got mixed up with "performance
| doubles every 18 months".
|
| 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.
| ninetyninenine wrote:
| > but features are still slowly getting smaller (your own
| link says so)
|
| 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.
| ben_w wrote:
| > 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_.
| inglor_cz wrote:
| Given that for a map, it is the sqkm which matters, 6 orders of
| magnitude from the map of San Francisco is a jump from 121 sqkm
| to 121 000 000 sqkm ... which is not even all dry land on
| Earth, much less in the Solar System.
|
| 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.
| baanist wrote:
| Are all fruit fry brains the same? Does anyone know what has
| actually been mapped and why it would generalize from one fruit
| fly to the next?
| dekhn wrote:
| I don't think that drosophila are eutelic
| (https://en.wikipedia.org/wiki/Eutely) so no two flies have
| precisely the same cells at precisely the same locations
| (that's true for c. elegans, whose connectome is probably the
| best studied).
|
| 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.
| BurningFrog wrote:
| The article describes it as slicing the fly brain into very
| thin slices, which are imaged by an electron microscope.
|
| 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.
| LeifCarrotson wrote:
| Fortunately, the whole chain of slicing, imaging, and
| analysis are now at least partially automated, so in theory
| you can repeat the process with nothing more than some time
| on the equipment and a bit of compute.
|
| In practice, I suspect there's a fair bit of grad student
| manual labor that keeps the pipeline flowing...
| roywiggins wrote:
| They crowdsourced three million manual corrections to the
| AI output, yeah.
| int_19h wrote:
| That sounds like a great training set then.
| twarge wrote:
| Yes, they are apparently exactly the same, with exactly the
| same neurons and connections!
|
| Happened to go for a walk with the corresponding author and
| made her repeat this fact for me.
| dekhn wrote:
| I don't think that's correct- the nature article about the
| article says they don't,
| https://www.nature.com/articles/d41586-024-03190-y and
| drosophila are not eutelic (although I see that some insects
| do have "partial constancy"). Could you ask the author to
| clarify?
|
| 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.
| baanist wrote:
| No need to get angry and sarcastic.
| andbberger wrote:
| highly stereotyped, definitely not identical
| swayvil wrote:
| Simulated too? I assume that if you can map it then you can
| simulate it. Am I correct?
| glial wrote:
| I doubt that's been done yet but I'd be surprised if it didn't
| happen soon using something like NEURON [1]. It would be
| telling to see how similar the simulation is to the living
| organism, since there is a lot going on inside the brain in
| addition to the neuron spiking.
|
| [1] https://nrn.readthedocs.io/en/8.2.6/
| meindnoch wrote:
| Simulating it would require many orders of magnitude more
| compute. Biological neurons are not just a sigmoid function.
| roywiggins wrote:
| > In one paper, for example, 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. The researchers learnt that the
| simulation was more than 90% accurate at predicting which
| neurons would respond and therefore how the fly would behave.
|
| https://www.nature.com/articles/d41586-024-03190-y
| jknoepfler wrote:
| If I understand what you're asking for correctly, then no, not
| in any meaningful sense. This is the gross structural anatomy
| of a dead brain, which is a small but important step towards
| understanding dynamics.
|
| 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?)
| worik wrote:
| Quite a leap, fruit fly to human....
| PaulKeeble wrote:
| Going to need a significant improvement in the software to get it
| to map a human. The fruit flu has 140,000 neurons and 54.5
| million synapses and the AI that mapped it required a post
| process with humans checking it all with 3 million edits and they
| still have to identify every neuron type.
|
| 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.
| keybored wrote:
| Cartographers have mapped Scotland. [random scribe muses that]
| The whole world could be next.
| pepve wrote:
| Did a rough calculation, it would be more like Edinburgh.
|
| 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.
| xandrius wrote:
| We did map a handful of brains yet, the more we do the
| better we will get at it.
|
| 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.
| AdieuToLogic wrote:
| > Did a rough calculation, it would be more like Edinburgh.
|
| 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...
| 1-6 wrote:
| This reminds me of the coastline paradox. I wonder if it
| applies to mapping an organism's brain. For example, one can
| say they know the length of Scotland's coastline but as the
| resolution increases, so does the coastline's length. It's
| infinite.
| jcfrei wrote:
| The resolution increases but the information doesn't. Apply
| some compression algorithm on the higher resolution
| coastline and you will find that you can reduce size
| massively. Same with LLMs and same with mapping the brain
| probably.
| perforator wrote:
| Why would the information not increase? If your unit is,
| say, 10 meters, you would only be able to see a straight
| line instead of curve.
| osrec wrote:
| You seem to have called it a fruit flu twice... Was that a typo
| or do you actually mean to call it a flu instead of a fly?!
| houseplant wrote:
| maybe he just has big fingers.
| willcipriano wrote:
| 50 years from now I am dying in a hospital bed, the nurse informs
| me that my consciousness will be uploaded to a computer with all
| the other brains, a digital heaven if you will.
|
| Get there and its full of flies.
| inglor_cz wrote:
| Well, the big question is if a human is "just" a mega-fly when
| it comes to brain structure.
| dekhn wrote:
| No, beetles (JBS Haldane said god has an "inordinate fondness
| for beetles")
| phyzome wrote:
| That may be so, but scientists have an inordinate fondness
| for flies.
| AStonesThrow wrote:
| Yeah but you can earn CPU cycles and egress bandwidth by
| sending bug reports
|
| Keeps your virtual landlord happy... Landlord of the Flies, if
| you will.
| akomtu wrote:
| heaven? not so fast. how about solving captchas at 100x speed
| for 100 years to aid the development of some ai vision project?
| dmitrysergeyev wrote:
| https://archive.is/vBUjt
| _giorgio_ wrote:
| Is this correct?
|
| 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?)
| charlescurt123 wrote:
| I believe they do know this.
|
| 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.
| andbberger wrote:
| > which neuron is connected to which neuron
|
| yes. and you can get VERY roughly connection strengths by
| synapse count but that's as far as you can go
| drdeca wrote:
| I don't think the last one is right. Fully connected and CNN
| are part of "what neuron is connected to what neuron" (though
| in the case of CNN, a number of corresponding "neurons" have
| equal weights going to/from them ).
|
| 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).
| adrian_b wrote:
| The paper published in Nature, which is open access:
| https://www.nature.com/articles/s41586-024-07558-y
| dmvdoug wrote:
| Paging mjg59, Matthew Garrett, Matthew Garrett to the white
| courtesy phone.
| holtkam2 wrote:
| What hard steps exist between mapping the physical structure of
| the brain and simulating a running one via software?
| aithrowawaycomm wrote:
| Knowing what the individual neurons actually do. The connectome
| is like an electrical schematic but you don't even know which
| components are resistors, inductors, etc (let alone the
| resistances and inductances).
|
| 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.
| s1artibartfast wrote:
| I think it's even more complex. The neurons are like
| individual raspberry pi. They have both complex logic and
| physical memory.
| notahacker wrote:
| Yeah, I think it's close to "what steps exist between
| observing the network topology of the internet and being
| able to emulate a Google search query?"
|
| 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...
| nonameiguess wrote:
| I'm frankly not sure it will ever be possible. Forget about
| observing the inside of a running neuron. In spite of how
| confidently people on the Internet will tell you their body
| fat percentage, in reality we can't even accurately measure
| that without killing you first.
| aithrowawaycomm wrote:
| I wouldn't say never, at least for C. elegans: there's been
| quite a bit of progress on imaging its brain, and it's
| plausible we'll have a fairly complete picture in a few
| decades:
| https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801769/
|
| 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.
| flashman wrote:
| go look at a photo of a CPU and imagine what else you'd need to
| run Windows on it, and then imagine it's probably extremely
| exponentially more difficult
| beefman wrote:
| Better source: https://www.nature.com/articles/d41586-024-03190-y
| janalsncm wrote:
| Do we have an accurate model of a single neuron or very small
| group of neurons? I understand the reality may be chaotic, but I
| would hope to have a simulation such that it mirrors the
| evolution of neurons to a reasonable extent.
| RaftPeople wrote:
| > _Do we have an accurate model of a single neuron_
|
| No.
|
| Many unknowns and even more being discovered regularly (e.g.
| tunneling nanotubes connecting neurons dynamically)
| Jun8 wrote:
| My (maybe very ignorant) question is: can this connectome be used
| to "run" simulations of a virtual fruit fly, a la MMAcevedo?
| fhdsgbbcaA wrote:
| Microsoft Fly Simulator (tm).
| andbberger wrote:
| no. turaga and co have some work where they constrain model
| network topologies with the connectome and train on visual
| data. this is imo a very silly line of research and they come
| to some very wrong conclusions about what neurons do what with
| it. but that's the closest to what you're asking for
| mr_toad wrote:
| It's a neural network without weights. And it doesn't have a
| body.
|
| 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.
| hwillis wrote:
| > It's a neural network without weights.
|
| 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.
| RaftPeople wrote:
| > _Neuron connection strength is determined by the number
| of synapses (1-100s, sometimes 1000s), the type of synapse
| neurotransmitter, and the number of receptors._
|
| But the astrocytes are dynamically modulating the signal at
| the synapse, it doesn't seem like we really know "the"
| weight.
| achileas wrote:
| And of course, not just frequency of incoming action
| potentials, but processes within the receiving cell, in the
| cell membrane, at the site of the synapse, and between the
| cell and any supporting cells (astrocytes and glia).
|
| 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.
| codeulike wrote:
| MMAcevedo is a reference to this short story (in the form of a
| future wiki article) which is brilliant, if you havent read it
| do check it out
|
| https://qntm.org/mmacevedo
|
| _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._
| Lonestar1440 wrote:
| A big reason for my imminent-AGI Skepticism is the fact that our
| understanding of the currently existing, Biological intelligence
| is so, so shallow.
|
| 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.
| tomrod wrote:
| I think we've already done this with a certain flatworm.
| perryizgr8 wrote:
| I don't think you need to fully understand how the brain works
| to be able to create AGI. Did the invention of the
| wheel/cart/car require us to fully understand how we walk? Did
| we need to fully understand how fish swim before we could make
| a boat? The only caveat would be that the AGI we build would be
| entirely unlike human minds. In the sane way a car going 100
| kph is different from a running person.
| tim333 wrote:
| It's surprising in a way how similar some generative AI seems
| to be to human parts of human minds like the dream like
| images produced some times and the reasoning in o1 being kind
| of human like.
| israrkhan wrote:
| AGI does not need to be based on biological intelligence. it is
| analogous to human will to fly, and our models were birds, but
| eventually we came up with something else (airplanes), that are
| much better at flying than birds (in some regards), and much
| there is nothing in nature so big, that can fly (nothing that
| we know of). IMO AGI could be similar.. despite its
| dissimilarities with biological brains, if it looks like a
| duck, quacks like a duck, swims like a duck, then it probably
| is duck (and perhaps better than duck in some ways).
| thoi234324234 wrote:
| Does it matter ?
|
| Openworm still hasn't succeeded.
| nomilk wrote:
| > Data available for download, programmatic access and
| interactive browsing and have been made interoperable with other
| fly data resources
|
| Curious what a 'fly brain map' looks like - iss the download a 3D
| model, or a matrix with values for attributes?
| idlewords wrote:
| It thinks a lot about fruit.
| khazhoux wrote:
| Can we please stop perpetuating this racist stereotype?
| philodeon wrote:
| The stereotype sounds way more homophobic to me than
| racist...
| perryizgr8 wrote:
| So can we run it on a computer now? That's the end goal, isn't
| it? Or maybe ask an LLM to look at the upload and figure out what
| makes it tick.
| okdood64 wrote:
| Why would an LLM in particular be good at this?
| yuz wrote:
| So, can it run Doom?
| pugworthy wrote:
| They are currently active in my kitchen. I think those thinking
| that AI and ML need massive compute power and electricity should
| take note that these little bastards can be annoying and
| pervasive as hell with just 100k neurons in that little head. And
| run off ripe bananas.
| dennis_jeeves2 wrote:
| Indeed it is miracle of evolution (or a creator if that suit
| you) to be so efficient.
| londons_explore wrote:
| If this could be done for a human, I think there is a realistic
| prospect of perhaps being the first human "brought back to life
| inside a computer".
|
| 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.
| groestl wrote:
| I fully agree, but at the same time it gives me Black Mirror
| vibes.
| londons_explore wrote:
| On the other hand, I suspect just putting your brain in a jar
| filled with formaldehyde has similar chances of future humans
| managing to re-animate it.
|
| 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.
| codeulike wrote:
| https://qntm.org/mmacevedo
| tim333 wrote:
| Long before bringing people back based on analysis of their
| neurons, there will be many people 'brought back' by something
| like an LLM acting like them similar to how a human actor sort
| of becomes someone. There have already been crude efforts and I
| guess as time goes on they will get better.
|
| (eg. the Kurzweils in 2016 https://www.pcmag.com/articles/how-
| ray-kurzweil-and-his-daug...)
| Nevermark wrote:
| My undergrad research was on identifying synaptic strengths based
| on firing behavior of networks of simple integrate to threshold
| neuron models.
|
| 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.
| DrPimienta wrote:
| What wondrous secrets of knowledge await us in such a mighty
| neurological architecture?
| awestroke wrote:
| Related: my favorite HN comment ever from a similar submission a
| year ago:
|
| > 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.
|
| https://news.ycombinator.com/item?id=36584130
| ash1794 wrote:
| Why!
| neverrroot wrote:
| Feel good, bragging rights, entitlement
| rtkwe wrote:
| It's a joke about utilitarianism which has had it's ups and
| downs but is a philosophy that has some pretty powerful
| adherents in the tech CEO world, notably the whole effective
| altruist crowd that SBF came out of. There's the Parable of
| Felix from SMBC [0] coming at it from the other angle of one
| incredibly happy person skewing the utility equation to do
| awful things.
|
| [0] https://www.smbc-comics.com/comics/20120403.gif
| FrustratedMonky wrote:
| They have a branding opportunity.
|
| 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!
| birracerveza wrote:
| And those who have a lot of Karma Kredits can enter the
| exclusive Karma Kredits Klub!
| rtkwe wrote:
| "We apologize for any negative connotations brought forward
| by our unfortunate naming of the Karma Kredits Klub and
| their ceremonial white peaked hats. As compensation we will
| simulate 50,000 more orgasmic fruit flies. Thank you we
| will not be taking questions."
| bookofjoe wrote:
| https://kk.org/
| FrustratedMonky wrote:
| Wash away the guilt of one-too-many Krispy Kreme with some
| Karma Kredits.
| neverrroot wrote:
| About as useful as the carbon credits themselves
| bumby wrote:
| Curious if you could elaborate? It seems like the issue isn't
| carbon credits, but the lack of a regulated level of carbon
| that would make them effective. Market incentives work best
| with dollar incentives, not virtue points.
| gnramires wrote:
| I am no specialist, but carbon credits seem like they can
| work to me. The problem is they need to be very carefully
| analyzed. Here in Brazil there have been cases of people
| illegally seizing protected forests (far off in Amazon
| regions) and making huge amounts of money selling off
| carbon credits simply for 'not taking the forest down' (it
| could be even worse: taking down the forest for even more
| 'reforestation credits'). Usually most of those 'pay
| someone not to pollute' can be very problematic, because in
| principle anyone can declare (misleading) intent to pollute
| any amount and thus could get infinite credits.
|
| 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.
| physicsguy wrote:
| I interviewed with a company a few years ago that offers
| people the ability to certify their carbon free generation
| in countries where there isn't a regulated market (I.e.
| outside of the US, EU, etc.). I was pretty shocked by the
| low standards of proof required, and it's fairly obvious
| that someone can seek accreditation from multiple competing
| certifying agencies.
|
| These unregulated credits get bought by heavy consumers in
| greenwashing
| spiritplumber wrote:
| I think that was the joke
| LukaD wrote:
| Yes, that was the joke.
| ElevenLathe wrote:
| I think this is probably a riff on something that happens in
| /Venomous Lumpsucker/ by Ned Beauman. I won't give it away but
| do highly recommend the book.
| ucarion wrote:
| For anyone curious about the real critique of utilitarianism
| behind the joke, it's called a utility monster:
|
| https://en.wikipedia.org/wiki/Utility_monster
| eternauta3k wrote:
| Why do you actually need to simulate them? The mapping between
| the state of the computer and the simulated state it represents
| exists only in our heads. You might as well say that any
| reality you can vaguely conceive of, exists and has moral
| weight.
| glenstein wrote:
| Depending on which article you look at, you can see things
| such as the stunning, vivid rotating 3D model showing the
| optic lobes, or elaborations on what the model captures: it
| has mapped 139,000 neurons and 130 million synapses. I don't
| think that quietly picturing a fruit fly in your head
| executes a true simulation of those details.
|
| 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.
| crispyambulance wrote:
| Interesting stuff, but I don't understand HOW they've done it.
|
| 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?
| svara wrote:
| You perform chemical fixation and heavy metal staining, then
| some form of serial sectioning. You can either image the
| sections themselves or serially image the block face.
|
| 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.
| danbruc wrote:
| Do we understand how memory works in general? If you tell me to
| remember the number 71, I can do that instantly with my short
| term memory, so I would guess that works without any changes to
| the brain structure and more like charging or discharging the
| tiny capacity in a DRAM cell or flipping the feedback loop in an
| SRAM cell. For long term memory on the other hand I would assume
| that this involves changes to the brain structure as this seems
| more robust but slower to do, I would have to think of 71 for
| quite some time in order to remember it weeks, months or years
| later. Do we know anything about this in good detail or is this
| still too hard to investigate because the relevant structures or
| processes are hidden in a sea of other things?
| achileas wrote:
| We don't really know much about how memories are formed, the
| short-term and long-term divisions come from cognitive
| psychology and we don't have (from what I remember from my
| neuro days, which may be out of date by now but I am friends
| with several neuroscientists and I'm not sure it's out of date
| yet) a strong idea of what the biological correlates of each
| are, or even if they arise via different processes or what.
| ignoramceisblis wrote:
| A 3D interactive interface, which has been around for several
| years now: https://flycircuit.neuronlp.fruitflybrain.org/
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