My CS teacher in high school had a lesson with us about horseshoe crab's eye, which apparently does not see anything that is larger than the field of view of the eye, allowing it to consume smaller organisms and omitting larger organisms. We used a neural network model in excel for such purposes.
He was (and hope he still is!) a great person, who taught me being curious and insightful in the computer science field. Taught me about Donald Knuth, TeX and many more things.
Does not see anything that os larger than the field of view?
I can’t conceptualise this. How? Surely this is the same as humans where if you had a big enough uniformly lit object in front of you, you couldn’t “see” it?
He starts his lecture assuming: "suppose we start with an ancestor who didn't really have an eye at all but just a single sheet of light sensitive cells . . ."
This seems like a very basic condition, and that going from 0 to 'just a single sheet of light sensitive cells' is almost nothing. But of course that is not the case. Before that you would need (+++):
1) Photoreceptor Proteins 2) Functional nervous system or signal processing pathways 3) A machinery to translate the absorption of light into an electrical signal 4) A system for coordinating these signal with other parts of the organism . . .
This is probably more a physics/CS question than a biological one, but how far off are we from having physics simulators good enough to try the types of experiments MIT did with the eye but for entire organisms? Presuming that will one-day be possible (i.e. the simulation takes earth's physics as inputs, and spits out organisms vaguely similar to those found on earth), then we could presumably enter the physics of exoplanets and find out what their creatures might be like.
I think we're quite far off if you want to model full organism complexity. But if you want to answer a research question you can model simpler versions today. Like this research team did around how vision evolves.
By the way– As of recently, we were able to model C Elegans (flatworm) in 3D with all neurons and neurotransmitters. It reacted to virtual stimuli just like a real worm (https://www.nature.com/articles/s43588-024-00738-w). So single-organisms is already possible. But the evolution of these entities in 3D will take us a bit more time is my guess:
10 years ago when I was still in academia, modelling more than 8 water molecules solvating an ion started to get computationally very expensive lol
Depends how accurate you want your model. Pie in the sky thinking, we need quantum computing before I can imagine these kinds of simulations making sense
Well, from a philosophical point of view you might say that it's impossible. The idea of laws that can apply to e.g. mathematics might not apply to organisms because organisms are described in terms of teleology. The heart doesn't just happen to circulate blood, it beats in order to circulate blood to oxygenate the body and so on.
And just because, for example, organisms that live in cold climates generally have thick hair, having thick hair doesn't imply cold climates. Some mammals fill niches filled by birds in other ecosystem, or by fish in others. Likewise in New Zealand they have birds filling niches that in other ecosystems are filled by mammals.
I'm not sure biology can be a purely inductive science.
Simulating physics of muscles/skeleton structures has been done many times over, and I feel like the focus on eyes and vision is what makes this one interesting. Anyway it's quite a fun category of youtube video: https://www.google.com/search?q=simulating+evolution+muscles They tend to be two-dimensional or have other big simplifications. I guess trying to evolve a "real" organism explodes in complexity very quickly even if you ignore the "brain".
So I guess it will be very hard, in a pure Darwinian meaning, to evolve an organism from scratch. But relax that meaning a little by fixing some things and letting only some things evolve, and I suspect it's already possible.
Fun fact about eye evolution, the dark spot you see on a dragonfly’s eye is not a pupil following you, or even a change in the eye itself. You’re actually just looking directly down into the columns of the dragonfly’s eye. They capture light so efficiently that they appear to be black. They’re close to the absolute physical limit for efficiency.
Hmmm... From my experience writing Artificial Life simulators in the 90s, there is so much that is dependent on the parameters that the simulation coders write into the models. Yes, if you build a Genetic Algorithm where you start with a photo-sensitive cell, and then you reward agents that are able to navigate a maze, you're going to end up "evolving" agents that use a bunch of photo-sensitive cells like an eye. Has that really told you something about evolution, about life, or about the simulation that you set up?
I do think it's the third, _but_ I do also think it's different than the kitchen-sink organism simulations that really were just watching a lot of shallowly-programmed behaviors interact with each other. I think of it more of a model than a simiulation - a model that tries to explain a specific real-life phenomenon in as focused and parsimonious way as possible.
Here, the takeaway is that the emergence of two different types of eye – compound and camera-like eyes – can be modelled by a set of 3 specific tasks, in combination with a minimal set of anatomical knobs and switches. Then it might actually be _informative_ to compare and contrast the clear evidence from the model, and see how these explanations compare to the less conclusive ones we can draw from the methods of evo-bio.
(A good analogy would be to look at how gates, latches, and clocks can alone account for the "emergence" of modern superscalar microarchitectures, without having to resort to modelling the analog madness of pushing high frequencies through physical circuits, for example.)
About the latter, obviously. You can't just test it as in physics, so that's all you can have. The proper question (I guess) is, is that knowledge completely useless or may it help with something down the line.
I must say that I was expecting that a single light receptor would also have been evolved from something else so that the title would have earned the moniker of 'from scratch'.
> “What’s interesting is that these Müller cells are known to reactivate and regenerate retina in fish,” she said. “But in mammals, including humans, they don’t normally do so, not after injury or disease. And we don’t yet fully understand why.”
In order for this to work in real life, you'd have to prove a lot of other invariants:
- The mechanism to interpret the light data signal has to be in-step with the evolution of the eye. Getting light data without a brain evolving at the same time to interpret it is evolutionary recessive, i.e. a useless function. I.e. a real evolution would be more like "cat /dev/urandom > output.html", not a controlled ecosystem with a clear penalty-reward system.
- In nature, there is no 1:1 "reward / selection function" like in this simulation. In the computer, this "motivation factor" is externally given, so that the next generation is rewarded and selected out, in reality, there is no rule as to what is and isn't "better" or "fitter" or "more attractive to the other gender" (not like CS nerds would know). Sure, an organism can consume food, but beyond a certain point that wouldn't make the organism just "fat", not stronger. So there also need to be environmental mutations happening at the same time, that reinforce "more food = better evolved".
- There has to be a way for the animal to be so dominant, that the connection between light data and food can be genetically passed on and will not be associated with bad artifacts (see ChatGPT hallucinations for examples of "accidental bad artifacts in evolution" - and that "evolution" has millions of man-hours, money and R&D behind it).
- By the rule of "survival of the fittest", the next generation mutation has to be (in one single step) such a significant improvement over the last one that it won't be selected out again by recessive selection or dilution inside of the gene pool.
- The gene has to be active within 150 subsequent generations, without fail, cancer, recession and provide 150 times a dominant advantage, just to get a basic "eye" for 2D navigation with 10 light sensors. The minimum snail eye (pre-Cambrian) has 14.000 cells [1] (and a snail cannot see color).
- The real world is a 3D environment, which adds a monumental amount of complexity. Add to it the complexity of depth, color, shape, ...
- The mutation(s) have to happen either "at once" or be widespread (otherwise it's going to be like an Albino animal, i.e. some rare neutral mutation).
- All of this has to be done in an environment hostile to life in general (i.e. the edge of underwater vulcanoes, some primordial soup burning at several hundred degrees), all elements have to be at the right place, at the same time, etc. And be created out of nothing, of course.
While I do agree that it can be helpful for computer vision, computerized "evolution" is just adaptive statistical pattern matching, but it's absolutely nothing like real biology. It would be more realistic to just output "/dev/random > kernel-gen-xxx.iso" and then run it bare-metal, with no lab environment, no operating system, no programming language, no goal function, no selection / reward process, no debugging, etc.
Even Darwin had his problems with the eye. The reason I believe in God is not necessarily because I want to, but because evolution (not survival-of-the-fittest, but the "mutation creates information" aspect) requires far more faith and far more dogmas, which cannot be questioned for the sake of science. When I was in 8th grade biology, I took a stone from the schoolyard, put it on the teachers desk and said "alright, so this is a human if we wait 4 billion years". The teacher ignored me, but never told me I'm wrong.
This is interesting. If nothing else to just have the visualization of the idea of how eyes evolved from a single light receptor. I've often heard of that in biology but this really makes the idea more tangible.
Eyes are good at generating the signal or we wouldn't have much use for the vision they do provide us.
On a somewhat related note, due to a head injury I suffered now many years ago, I started developing small-ish blind spots "once in a while" that remain anywhere from a few minutes up to a few months.
The spots are very noticeable when they first appear, grabbing the attention all the time. The ones that persist long tend to "disappear" when my brain filters out the broken (?) signal of where the blind spot appeared, and the spot mainly becomes noticeable again if it hides or interrupts a known pattern that I'm looking at.
It's as if the brain fills the spot with the average color around it (blue sky - blue spot, white wall - white spot etc), which works well for single color surfaces and such but not great for repeating and predictable patterns. And the hiding "lags" which means if I quickly shift between colors then the spot will momentarily be visible as the old color shows up on the new color.
So the brain does "imagine" things, but when it does, it isn't perfect.
[+] [-] p0w3n3d|1 year ago|reply
He was (and hope he still is!) a great person, who taught me being curious and insightful in the computer science field. Taught me about Donald Knuth, TeX and many more things.
[+] [-] pinoy420|1 year ago|reply
I can’t conceptualise this. How? Surely this is the same as humans where if you had a big enough uniformly lit object in front of you, you couldn’t “see” it?
[+] [-] bloomingkales|1 year ago|reply
https://www.youtube.com/watch?v=2X1iwLqM2t0
[+] [-] wolfhumble|1 year ago|reply
This seems like a very basic condition, and that going from 0 to 'just a single sheet of light sensitive cells' is almost nothing. But of course that is not the case. Before that you would need (+++):
1) Photoreceptor Proteins 2) Functional nervous system or signal processing pathways 3) A machinery to translate the absorption of light into an electrical signal 4) A system for coordinating these signal with other parts of the organism . . .
[+] [-] nomilk|1 year ago|reply
[+] [-] MVissers|1 year ago|reply
I think we're quite far off if you want to model full organism complexity. But if you want to answer a research question you can model simpler versions today. Like this research team did around how vision evolves.
By the way– As of recently, we were able to model C Elegans (flatworm) in 3D with all neurons and neurotransmitters. It reacted to virtual stimuli just like a real worm (https://www.nature.com/articles/s43588-024-00738-w). So single-organisms is already possible. But the evolution of these entities in 3D will take us a bit more time is my guess:
Also I'm not a CS person, just an enthusiast.
[+] [-] adrianN|1 year ago|reply
[+] [-] djtango|1 year ago|reply
Depends how accurate you want your model. Pie in the sky thinking, we need quantum computing before I can imagine these kinds of simulations making sense
[+] [-] mathieuh|1 year ago|reply
And just because, for example, organisms that live in cold climates generally have thick hair, having thick hair doesn't imply cold climates. Some mammals fill niches filled by birds in other ecosystem, or by fish in others. Likewise in New Zealand they have birds filling niches that in other ecosystems are filled by mammals.
I'm not sure biology can be a purely inductive science.
[+] [-] harry-wood|1 year ago|reply
[+] [-] dsign|1 year ago|reply
[+] [-] teekert|1 year ago|reply
[+] [-] T-A|1 year ago|reply
https://www.gregegan.net/MISC/CRYSTAL/Crystal.html
[+] [-] thrance|1 year ago|reply
[+] [-] bglazer|1 year ago|reply
[+] [-] hammock|1 year ago|reply
[+] [-] tonetegeatinst|1 year ago|reply
[+] [-] SamBam|1 year ago|reply
[+] [-] nxobject|1 year ago|reply
Here, the takeaway is that the emergence of two different types of eye – compound and camera-like eyes – can be modelled by a set of 3 specific tasks, in combination with a minimal set of anatomical knobs and switches. Then it might actually be _informative_ to compare and contrast the clear evidence from the model, and see how these explanations compare to the less conclusive ones we can draw from the methods of evo-bio.
(A good analogy would be to look at how gates, latches, and clocks can alone account for the "emergence" of modern superscalar microarchitectures, without having to resort to modelling the analog madness of pushing high frequencies through physical circuits, for example.)
[+] [-] wruza|1 year ago|reply
[+] [-] unknown|1 year ago|reply
[deleted]
[+] [-] utkarsh858|1 year ago|reply
[+] [-] fastball|1 year ago|reply
---
Website seems broken (repo deleted?), had to go here[2] instead.
And here is the paper[3] the website is about.
[1] https://cambrian.pages.dev/
[2] https://eyes.mit.edu/ACI/
[3] https://arxiv.org/pdf/2501.15001
[+] [-] kpush|1 year ago|reply
[+] [-] unknown|1 year ago|reply
[deleted]
[+] [-] aryoung1|1 year ago|reply
[+] [-] westurner|1 year ago|reply
> [ mTOR, Muller glia in Zebrafish, ]
From "Reactivating Dormant Cells in the Retina Brings New Hope for Vision Regeneration" (2023) https://neurosciencenews.com/vision-restoration-genetic-2318... :
> “What’s interesting is that these Müller cells are known to reactivate and regenerate retina in fish,” she said. “But in mammals, including humans, they don’t normally do so, not after injury or disease. And we don’t yet fully understand why.”
[+] [-] smusamashah|1 year ago|reply
[+] [-] mgnn|1 year ago|reply
[+] [-] jaetee|1 year ago|reply
[+] [-] dangoodmanUT|1 year ago|reply
[+] [-] d0100|1 year ago|reply
[+] [-] jer0me|1 year ago|reply
[+] [-] fschuett|1 year ago|reply
- The mechanism to interpret the light data signal has to be in-step with the evolution of the eye. Getting light data without a brain evolving at the same time to interpret it is evolutionary recessive, i.e. a useless function. I.e. a real evolution would be more like "cat /dev/urandom > output.html", not a controlled ecosystem with a clear penalty-reward system.
- In nature, there is no 1:1 "reward / selection function" like in this simulation. In the computer, this "motivation factor" is externally given, so that the next generation is rewarded and selected out, in reality, there is no rule as to what is and isn't "better" or "fitter" or "more attractive to the other gender" (not like CS nerds would know). Sure, an organism can consume food, but beyond a certain point that wouldn't make the organism just "fat", not stronger. So there also need to be environmental mutations happening at the same time, that reinforce "more food = better evolved".
- There has to be a way for the animal to be so dominant, that the connection between light data and food can be genetically passed on and will not be associated with bad artifacts (see ChatGPT hallucinations for examples of "accidental bad artifacts in evolution" - and that "evolution" has millions of man-hours, money and R&D behind it).
- By the rule of "survival of the fittest", the next generation mutation has to be (in one single step) such a significant improvement over the last one that it won't be selected out again by recessive selection or dilution inside of the gene pool.
- The gene has to be active within 150 subsequent generations, without fail, cancer, recession and provide 150 times a dominant advantage, just to get a basic "eye" for 2D navigation with 10 light sensors. The minimum snail eye (pre-Cambrian) has 14.000 cells [1] (and a snail cannot see color).
- The real world is a 3D environment, which adds a monumental amount of complexity. Add to it the complexity of depth, color, shape, ...
- The mutation(s) have to happen either "at once" or be widespread (otherwise it's going to be like an Albino animal, i.e. some rare neutral mutation).
- All of this has to be done in an environment hostile to life in general (i.e. the edge of underwater vulcanoes, some primordial soup burning at several hundred degrees), all elements have to be at the right place, at the same time, etc. And be created out of nothing, of course.
While I do agree that it can be helpful for computer vision, computerized "evolution" is just adaptive statistical pattern matching, but it's absolutely nothing like real biology. It would be more realistic to just output "/dev/random > kernel-gen-xxx.iso" and then run it bare-metal, with no lab environment, no operating system, no programming language, no goal function, no selection / reward process, no debugging, etc.
Even Darwin had his problems with the eye. The reason I believe in God is not necessarily because I want to, but because evolution (not survival-of-the-fittest, but the "mutation creates information" aspect) requires far more faith and far more dogmas, which cannot be questioned for the sake of science. When I was in 8th grade biology, I took a stone from the schoolyard, put it on the teachers desk and said "alright, so this is a human if we wait 4 billion years". The teacher ignored me, but never told me I'm wrong.
[1] https://link.springer.com/article/10.1007/BF00606433
[+] [-] boxed|1 year ago|reply
Spoiler warning: Darwin didn't really have any problems with the eye. That's just something creationists say.
[+] [-] IOUnix|1 year ago|reply
[+] [-] whatever1|1 year ago|reply
[+] [-] ZaoLahma|1 year ago|reply
On a somewhat related note, due to a head injury I suffered now many years ago, I started developing small-ish blind spots "once in a while" that remain anywhere from a few minutes up to a few months.
The spots are very noticeable when they first appear, grabbing the attention all the time. The ones that persist long tend to "disappear" when my brain filters out the broken (?) signal of where the blind spot appeared, and the spot mainly becomes noticeable again if it hides or interrupts a known pattern that I'm looking at.
It's as if the brain fills the spot with the average color around it (blue sky - blue spot, white wall - white spot etc), which works well for single color surfaces and such but not great for repeating and predictable patterns. And the hiding "lags" which means if I quickly shift between colors then the spot will momentarily be visible as the old color shows up on the new color.
So the brain does "imagine" things, but when it does, it isn't perfect.
[+] [-] bongodongobob|1 year ago|reply
[+] [-] dod25|1 year ago|reply
[+] [-] unknown|1 year ago|reply
[deleted]
[+] [-] cozzyd|1 year ago|reply
[deleted]