Slime molds’ complex ability to detect mass from a distance(harvardmagazine.com)
harvardmagazine.com
Slime molds’ complex ability to detect mass from a distance
https://www.harvardmagazine.com/2021/11/right-now-can-slime-molds-think
46 comments
Also it is really cool that it prefers to move towards objects with shapes, it prefers moving towards them over the clean walls and it prefers them more when there are more of those shapes. And the movement is very deliberate, it doesn't just meander in that direction but instead just heads straight to it.
Oh yeah, it definitely picked a direction and committed.
Slime molds have three distinct phases. One phase is basically a distributed network of cells that communicate chemically. These signals can indicate when it is time to change form (transform!). Sounds like this is an extension of that in-between form.
There is a fantastic book that talks about this kind of emergent intelligence in slime molds, called--ironically--"Emergence: The Connected Lives of Ants, Brains, Cities, and Software".
The first 1/4 of the book is about the freakiness of slime mold. I thoroughly enjoyed this book, very well written and accessible and covers multiple domains (a lot like G.E.B.); Link to non-amazon:
https://www.barnesandnoble.com/w/emergence-steven-johnson/11...
There is a fantastic book that talks about this kind of emergent intelligence in slime molds, called--ironically--"Emergence: The Connected Lives of Ants, Brains, Cities, and Software".
The first 1/4 of the book is about the freakiness of slime mold. I thoroughly enjoyed this book, very well written and accessible and covers multiple domains (a lot like G.E.B.); Link to non-amazon:
https://www.barnesandnoble.com/w/emergence-steven-johnson/11...
> In much the same way that a blindfolded person on a trampoline can sense if other people are on it, TRP channels help the slime mold detect faraway objects.
This is as detailed a description as the article gives in how they detect objects? This explanation does not seem very helpful since I assume the glass disks were not jumping up and down. Is there any more information available on how they sense?
This is as detailed a description as the article gives in how they detect objects? This explanation does not seem very helpful since I assume the glass disks were not jumping up and down. Is there any more information available on how they sense?
The original paper seems to be at https://onlinelibrary.wiley.com/doi/10.1002/adma.202008161.
I only glanced at it, but noticed that they hypothesized that the organism uses mechanosensation involving rhythmic pulling on the substrate and interpretation of changes in things like tension, compression or mechanical strain.
I only glanced at it, but noticed that they hypothesized that the organism uses mechanosensation involving rhythmic pulling on the substrate and interpretation of changes in things like tension, compression or mechanical strain.
Free access to the (preprint) paper appears to be at:
https://www.researchgate.net/publication/340090261_Mechanose...
https://www.researchgate.net/publication/340090261_Mechanose...
Ok this is their theory for how it senses:
> Physarum is widely known to grow in a pulsatile manner, which consists of a forward growth phase and a reverse streaming phase during which the cytoplasm is retracted away from the growth regions (Figure 8A). We have observed that this oscillation is critical for Physarum mechanosensation and that interrupting it by changing the substrate stiffness (Figure 4) or interfering with the oscillations (Figure 6) prevents accurate decision making. Thus, we propose a theoretical model of Physarum navigation where this oscillatory behavior acts as a sample-and- integrate function (Figure 8B): the growth regions sample the environment during the growth phase, optimize the direction of the network tubes during the reverse streaming phase by inducing internal tension in the Physarum network which then aligns future growth of the growth regions.
So basically the slime mold pulls back on the substrate and feels for strain gradients.
> Physarum is widely known to grow in a pulsatile manner, which consists of a forward growth phase and a reverse streaming phase during which the cytoplasm is retracted away from the growth regions (Figure 8A). We have observed that this oscillation is critical for Physarum mechanosensation and that interrupting it by changing the substrate stiffness (Figure 4) or interfering with the oscillations (Figure 6) prevents accurate decision making. Thus, we propose a theoretical model of Physarum navigation where this oscillatory behavior acts as a sample-and- integrate function (Figure 8B): the growth regions sample the environment during the growth phase, optimize the direction of the network tubes during the reverse streaming phase by inducing internal tension in the Physarum network which then aligns future growth of the growth regions.
So basically the slime mold pulls back on the substrate and feels for strain gradients.
The organism didn't evolve to live in a petri dish. I wonder how it uses this ability in the wild.
Is there any reason one should not just go to Sci-Hub when hitting a paywall?
I think Alexandra has temporarily stopped adding new papers, hasn't she?
She's resumed updating Sci-Hub:
Today is Sci-Hub anniversary the project is 10 years old!
I'm going to publish 2,337,229 new articles to celebrate the date. They will be available on the website in a few hours (how about the lawsuit in India you may ask: our lawyers say that restriction is expired already)
https://nitter.kavin.rocks/ringo_ring/status/143435621720862...
Today is Sci-Hub anniversary the project is 10 years old!
I'm going to publish 2,337,229 new articles to celebrate the date. They will be available on the website in a few hours (how about the lawsuit in India you may ask: our lawyers say that restriction is expired already)
https://nitter.kavin.rocks/ringo_ring/status/143435621720862...
I found this one on Sci-Hub by plugging in the DOI before I saw the comment above.
An interesting related concept is Mogees [0] that converts any surface to a musical instrument using a contact microphone. The initial underlying theory is described in the thesis of Bruno Zamberlin [1], where he describes in chapter 4 a work with Carmine Emanuele Cella where a piezo sensor is used to convert the vibrations on any physical object into musical sound.
[0]: https://youtu.be/xdo8N30qb2A
[1]: http://research.gold.ac.uk/id/eprint/12312/
[0]: https://youtu.be/xdo8N30qb2A
[1]: http://research.gold.ac.uk/id/eprint/12312/
There’s an iOS app called Impaktor that gives similar results with the phone’s mic instead of a contact mic. So not as nuanced, but great for $5. (You will need headphones.)
https://www.beepstreet.com/ios/impaktor
https://www.beepstreet.com/ios/impaktor
The experiment happened over a day, so lots of time for environmental vibrations (sound, seismic activity) to paint a subtle picture of the immediate surroundings.
Sensation of mechanical stimulation. How we sense touch, vibrations, sound.
> They keep time: if blown with cold air on the first of every hour, by the third hour, they’ll retract just before the cold snap.
That is super quick learning! And for a formless blob!
Imagine if we knew how to train ML models that could reliably perform feats like this (learn after training, not train to withdraw every hour), while still managing all other parts of being a slime mold like hunting for food, distributing nutrients etc.
Imagine if we knew how to train ML models that could reliably perform feats like this (learn after training, not train to withdraw every hour), while still managing all other parts of being a slime mold like hunting for food, distributing nutrients etc.
That won't happen for a long time. First we'd need to let go of the idea that "intelligence" means "neural network", and there's a huge amount of ideological resistance to the idea that intelligence isn't made with composeable atomic computation blocks.
Physical slime molds have already been used in an experimental model to optimize road networks:
https://www.sciencedirect.com/science/article/abs/pii/S03032...
https://www.sciencedirect.com/science/article/abs/pii/S03032...
> Imagine if we knew how to train ML models that could reliably perform feats like this
I imagined it and I don’t find it that special.
Picking up a strong and regular periodic signal and predicting the next iteration is not a hard problem.
> while still managing all other parts of being a slime mold like hunting for food, distributing nutrients etc.
ML models don’t do those things because ML models are abstract constructs without appendages or any other physical embodiment.
We cannot manufacture artifical slime molds because we cannot manufacture artifical cells. This doesn’t seems to be an ML problem. (Or if it is, then everything become an ML problem too and the term lost its meaning.)
I imagined it and I don’t find it that special.
Picking up a strong and regular periodic signal and predicting the next iteration is not a hard problem.
> while still managing all other parts of being a slime mold like hunting for food, distributing nutrients etc.
ML models don’t do those things because ML models are abstract constructs without appendages or any other physical embodiment.
We cannot manufacture artifical slime molds because we cannot manufacture artifical cells. This doesn’t seems to be an ML problem. (Or if it is, then everything become an ML problem too and the term lost its meaning.)
> ML models don’t do those things because ML models are abstract constructs without appendages or any other physical embodiment.
I am talking about it doing this in a simulation. We don't have AI that are as smart as a slime mold at the moment, it will be an achievement when we get there. Also we can't discount the processing power required to process raw signals detected, our AI models needs to also learn the signal processing as well. The raw data a slime mold has to work with is extremely bare, if you get that input do you really think an AI could do the same things as it? And this is a single cell organism, this is the capability of a single biological cell.
> Picking up a strong and regular periodic signal and predicting the next iteration is not a hard problem.
Not if that is the only thing you do, no, but if you also do a lot of other things at the same time then it gets extremely hard. Edit: For example, how does the mold know that it just didn't move into a sunny spot? Or maybe this is just a cloud and it retracted unnecessarily? There are a lot of things to account for in a real world scenario that the slime mold has to understand that your naive signal processor can just ignore.
I am talking about it doing this in a simulation. We don't have AI that are as smart as a slime mold at the moment, it will be an achievement when we get there. Also we can't discount the processing power required to process raw signals detected, our AI models needs to also learn the signal processing as well. The raw data a slime mold has to work with is extremely bare, if you get that input do you really think an AI could do the same things as it? And this is a single cell organism, this is the capability of a single biological cell.
> Picking up a strong and regular periodic signal and predicting the next iteration is not a hard problem.
Not if that is the only thing you do, no, but if you also do a lot of other things at the same time then it gets extremely hard. Edit: For example, how does the mold know that it just didn't move into a sunny spot? Or maybe this is just a cloud and it retracted unnecessarily? There are a lot of things to account for in a real world scenario that the slime mold has to understand that your naive signal processor can just ignore.
> For example, how does the mold know that it just didn't move into a sunny spot?
Why do you think that it does know that?
> Or maybe this is just a cloud and it retracted unnecessarily?
I bet that it does retract sometimes unnecessarily. Every organism has these kind of false positives.
> We don't have AI that are as smart as a slime mold at the moment, it will be an achievement when we get there.
When you say we don’t have this AI do you mean you can’t download it from Github right now, or do you mean it is something humanity couldn’t build if they tried? I assume you mean it in the second sense. Why do you think it is so hard? Let’s say someone, a research project or an alien inteligence gives you such an AI on a thumbstick. How would you evaluate it to test if it is indeed a simulation of a slime mold? In other words what do you expect from the simulation?
Why do you think that it does know that?
> Or maybe this is just a cloud and it retracted unnecessarily?
I bet that it does retract sometimes unnecessarily. Every organism has these kind of false positives.
> We don't have AI that are as smart as a slime mold at the moment, it will be an achievement when we get there.
When you say we don’t have this AI do you mean you can’t download it from Github right now, or do you mean it is something humanity couldn’t build if they tried? I assume you mean it in the second sense. Why do you think it is so hard? Let’s say someone, a research project or an alien inteligence gives you such an AI on a thumbstick. How would you evaluate it to test if it is indeed a simulation of a slime mold? In other words what do you expect from the simulation?
> When you say we don’t have this AI do you mean you can’t download it from Github right now, or do you mean it is something humanity couldn’t build if they tried?
"Could build if they tried" is a strange description, we haven't made an AI with similar capabilities as a slime mold so we don't know how to do it. Maybe a research team could do it if they tried, and we would learn things from it, but before we have done it I'll say that we can't do it. Similarly 5 years ago I'd say that we don't know how to make a super human GO AI at the time. There was a team in the process of making a GO AI in the works, but we still didn't know how to build it since we didn't know if their tech would be good enough to do it at the time.
> I bet that it does retract sometimes unnecessarily. Every organism has these kind of false positives.
Sure, but it can't get into an infinite loop or hang, or it would die a lot. It needs to be smart enough to survive in most real world scenarios. That is the hard part. It can't just do something like "if I get cold two times in a row I'll retract and stop moving in preparation for the third time", that would probably make it stop way too much. And we do know that slime molds remembers locations of food, so it has a proper memory, likely it has some sort of reasoning about temperatures and locations as well to avoid going to its death. This just has evidence that it even reasons about time and temperature as well, and even detects recurring time series events in addition to everything else it does, that is a lot of features that needs to interact well.
Edit:
> How would you evaluate it to test if it is indeed a simulation of a slime mold?
Simple, you make another team set up some tests for it, and see if it behaves similar to a real world slime mold in those scenarios. Slime mold environments are very quick and cheap to set up and run, so it would be neither hard nor expensive to do.
"Could build if they tried" is a strange description, we haven't made an AI with similar capabilities as a slime mold so we don't know how to do it. Maybe a research team could do it if they tried, and we would learn things from it, but before we have done it I'll say that we can't do it. Similarly 5 years ago I'd say that we don't know how to make a super human GO AI at the time. There was a team in the process of making a GO AI in the works, but we still didn't know how to build it since we didn't know if their tech would be good enough to do it at the time.
> I bet that it does retract sometimes unnecessarily. Every organism has these kind of false positives.
Sure, but it can't get into an infinite loop or hang, or it would die a lot. It needs to be smart enough to survive in most real world scenarios. That is the hard part. It can't just do something like "if I get cold two times in a row I'll retract and stop moving in preparation for the third time", that would probably make it stop way too much. And we do know that slime molds remembers locations of food, so it has a proper memory, likely it has some sort of reasoning about temperatures and locations as well to avoid going to its death. This just has evidence that it even reasons about time and temperature as well, and even detects recurring time series events in addition to everything else it does, that is a lot of features that needs to interact well.
Edit:
> How would you evaluate it to test if it is indeed a simulation of a slime mold?
Simple, you make another team set up some tests for it, and see if it behaves similar to a real world slime mold in those scenarios. Slime mold environments are very quick and cheap to set up and run, so it would be neither hard nor expensive to do.
Reminds me of the theory that many plants have "eyes".
https://www.scientificamerican.com/article/veggies-with-visi...
https://www.nationalgeographic.com/science/article/the-sneak... (for this there is the claim that it copies plastic plants, too. couldn't find anything factual though)
https://www.scientificamerican.com/article/veggies-with-visi...
https://www.nationalgeographic.com/science/article/the-sneak... (for this there is the claim that it copies plastic plants, too. couldn't find anything factual though)
I’m curious whether this result contradicts prior findings by making the experimental setups untrustworthy.
If Slime can detect a mass horizon gradient, maybe experiments need to be redesigned to account for it?
If Slime can detect a mass horizon gradient, maybe experiments need to be redesigned to account for it?
Surely mass is a red herring in all this. The masses involved must be waaaaaay too small to detect, especially in the presence of earth's gravity.
Why would a slime mold even evolve this capability of detecting tiny mass differences?
None of it makes sense. It's much more likely there's an ultra-thin tendril they haven't noticed.
Why would a slime mold even evolve this capability of detecting tiny mass differences?
None of it makes sense. It's much more likely there's an ultra-thin tendril they haven't noticed.
Nova has a show on this too:
https://www.kpbs.org/news/arts-culture/2020/09/15/nova-secre...
There’s a lot of conjecture in this thread so I might as well throw in a couple cents.
Mold is saprophytic so it’s more often than not advantageous to grow toward larger stationary objects in its environment to eat.
The stack of discs deformed the agar gel, the mechanoreceptors on the periphery hyphae detected the tiny deformation and set off a chemical cascade for growth in that direction.
What if the substrate is lumpy and harder than agar? The researchers disabled the mechanoreceptors and there was no “mass detection”. If there’s no deformation in the substrate by discs on a hard bumpy surface then I’m guessing those mechanoreceptors won’t detect the mass.
So mold uses gradient deformation of mechanoreceptors to move toward possible food sources.
Mold is saprophytic so it’s more often than not advantageous to grow toward larger stationary objects in its environment to eat.
The stack of discs deformed the agar gel, the mechanoreceptors on the periphery hyphae detected the tiny deformation and set off a chemical cascade for growth in that direction.
What if the substrate is lumpy and harder than agar? The researchers disabled the mechanoreceptors and there was no “mass detection”. If there’s no deformation in the substrate by discs on a hard bumpy surface then I’m guessing those mechanoreceptors won’t detect the mass.
So mold uses gradient deformation of mechanoreceptors to move toward possible food sources.
>So mold uses gradient deformation of mechanoreceptors to move toward possible food sources.
This explains why they compared the ability to a blind folded person on a trampoline. I wish they had explained it this way.
This explains why they compared the ability to a blind folded person on a trampoline. I wish they had explained it this way.
It’s just my 2 cents.
The mold may not even be moving toward a food source - it could be moving toward the deformation because larger objects cast bigger shadows and it needs to prevent desiccation.
The mold may not even be moving toward a food source - it could be moving toward the deformation because larger objects cast bigger shadows and it needs to prevent desiccation.
Could it be because the three glass disks cause a slope in the agar gel? And the slime mold prefers to grow downhill?
> They found that, even more than the total mass of the objects, the slime mold reacted to how much of the horizon the objects occupied. It repeatedly preferred objects spread along the horizon as opposed to those at a single, stacked point
Could the slime grow thin extremely small exploratory network first and then decide how to grow its main body?
Or could evaporation happen faster on the one-disk side so that the slime mold prefers to move towards the more humid/moist side?
I wonder if it has something to do with surface tension
How many times was this repeated? How many trials did they have to do before they got this result? How many other objects did they try to see if its something related to the glass?
> How many times was this repeated?
A 100 times for each variant.
> How many trials did they have to do before they got this result?
Looks like it picked the side with 3 disks 70% of the time and never picked the side with 1 disk. They tested a ton of different scenarios so the results seems really robust.
"When presented with the 3-discs versus 1-disc choice, the Physarum grew toward the 3 disc regions 70% of the time, while never choosing the 1 disc region alone."
A 100 times for each variant.
> How many trials did they have to do before they got this result?
Looks like it picked the side with 3 disks 70% of the time and never picked the side with 1 disk. They tested a ton of different scenarios so the results seems really robust.
"When presented with the 3-discs versus 1-disc choice, the Physarum grew toward the 3 disc regions 70% of the time, while never choosing the 1 disc region alone."
Glass disks could reflect light towards the mold. Otherwise this experiment results seem contradictory to physics.
> Then, the scientists turned out the lights and watched
It's not clear to me if they are using 'mass' as we understand it from physics, or just the general notion of size of an object.
To me the stacking experiment seems to invalidate that its the former. It would probably be useful to do some analysis on airflow inside the petri dish, there could be a very subtle convection and/or radiant heating from the white disks. There's also potential phototropic effects like we would see with plants bending towards light. Both of these inputs would actually be affected by the stacking, where mass is clearly not. (Yes they 'turn off the lights', but you can't take a time lapse movie in absolute darkness)
Regardless of the cause, the effects are going to be extremely subtle, and the fact that the slime mold seems to select a direction based on this incredibly subtle effect is still extremely cool.