How tiny wasps cope with being smaller than amoebas(blogs.discovermagazine.com)
blogs.discovermagazine.com
How tiny wasps cope with being smaller than amoebas
http://blogs.discovermagazine.com/notrocketscience/2011/11/30/how-fairy-wasps-cope-with-being-smaller-than-amoebas/
12 comments
Wow: with only 7,400 neurons (compared to 340,000 for the common housefly and 850,000 for honeybees), this wasp can somehow fly, search for food, find the right places to lay its eggs, etc.
That ridiculously tiny neural network is one freakingly efficient computing device!
That ridiculously tiny neural network is one freakingly efficient computing device!
7,400 seems very capable of being simulated. In fact, that's a small enough number that I'll bet you could map those connections out by hand with some type of microscope.
To someone more knowledgeable in this subset of biology, is this possible?
To someone more knowledgeable in this subset of biology, is this possible?
It is possible, but the neurons are actually complex computing units that take a plethora of signals into account: subtle temporal behavior (relative timings of post and pre-synaptic activations), complex chemistry in the cell as well as the in the synaptic cleft, and many more less understood things.
Secondly, as you can imagine, the connectivity is far from random. Like in larger nervous systems (or even more than in large pools or neurons), these computing units take precise roles, part due to the developmental process, part due to later stage learning. But in the end, the machinery is precise, and simulating it requires understanding all of it.
I believe that this understanding should not come though imagery alone, but also through studies of the development of these organisms' brains. Like we are now forming artificial neural maps through "machine" learning techniques that are accelerated versions of their biological counterparts, I believe that we should develop developmental algorithms into large-scale simulations. The goals being to first understand how it works, second to see if the models are able to replicate the end result, a good indication that they are useful models.
(I do research in computational neuroscience.)
I believe that this understanding should not come though imagery alone, but also through studies of the development of these organisms' brains. Like we are now forming artificial neural maps through "machine" learning techniques that are accelerated versions of their biological counterparts, I believe that we should develop developmental algorithms into large-scale simulations. The goals being to first understand how it works, second to see if the models are able to replicate the end result, a good indication that they are useful models.
(I do research in computational neuroscience.)
Jefe: "It is possible, but the neurons are actually complex computing units that take a plethora of signals into account: subtle temporal behavior, complex chemistry in the cell as well as in the synaptic cleft, and many more less understood things. Secondly, as you can imagine, the connectivity is far from random. Like in larger nervous systems, these computing units take precise roles, part due to the developmental process, part due to later stage learning. "
El Guapo: "Would you say, senor, that I have a plethora of signals in each computing unit? And I just would like to know if you know what a plethora is. I would not like to think that a person would tell someone he has a plethora, and then find out that that person has no idea what it means to have a plethora."
Jefe: "Forgive me, El Guapo. I know that I, Jefe, do not have your superior intellect and education. But could it be that once again, you are angry at something else, and are looking to take it out on me?"
http://www.youtube.com/watch?v=-mTUmczVdik
"But in the end, the machinery is precise, and simulating it requires understanding all of it."
Sorry to pick at you for this, but how can you be certain of the many claims you've made here? How do you know that intelligence cannot be implemented in a mechanism simpler than the biological hardware implementation or simulations thereof?
El Guapo: "Would you say, senor, that I have a plethora of signals in each computing unit? And I just would like to know if you know what a plethora is. I would not like to think that a person would tell someone he has a plethora, and then find out that that person has no idea what it means to have a plethora."
Jefe: "Forgive me, El Guapo. I know that I, Jefe, do not have your superior intellect and education. But could it be that once again, you are angry at something else, and are looking to take it out on me?"
http://www.youtube.com/watch?v=-mTUmczVdik
"But in the end, the machinery is precise, and simulating it requires understanding all of it."
Sorry to pick at you for this, but how can you be certain of the many claims you've made here? How do you know that intelligence cannot be implemented in a mechanism simpler than the biological hardware implementation or simulations thereof?
(Did I misuse "plethora"? English is not my first language, sorry if I shouldn't use plethora)
> How do you know that intelligence cannot be implemented in a mechanism simpler than the biological hardware implementation or simulations thereof?
I don't know if intelligence cannot be implemented in a simpler way (I certainly hope it can), I am concerned here about understanding the biological implementation of intelligence. I say that simulations are useful because the models that we may make of the emergence of the mechanisms of intelligence in networks of neurons would be very hard to understand without being implemented and simulated.
However, if you are interested in the artificial implementation of the intelligent behavior of some organisms, you could maybe still benefit from understanding how this intelligent behavior arises in these organisms, and take inspiration from these mechanisms.
> Sorry to pick at you for this, but how can you be certain of the many claims you've made here?
I didn't make revolutionary claims regarding the functioning of biological neural networks. The fact that neurons are sensitive to relative spike timing is evident in mechanisms like spike timing dependent plasticity (http://www.scholarpedia.org/article/Spike-timing_dependent_p...). The "complex chemistry" that I mention is behind cognition is evident if you open any neurology book, for instance Principles of Neural Science by Kandel.
Then, I say that simulation (and in particular simulation of development as well as learning) is the right road to understanding biological neural systems, but I took care to mention that it is what I believe, not what I know.
> How do you know that intelligence cannot be implemented in a mechanism simpler than the biological hardware implementation or simulations thereof?
I don't know if intelligence cannot be implemented in a simpler way (I certainly hope it can), I am concerned here about understanding the biological implementation of intelligence. I say that simulations are useful because the models that we may make of the emergence of the mechanisms of intelligence in networks of neurons would be very hard to understand without being implemented and simulated.
However, if you are interested in the artificial implementation of the intelligent behavior of some organisms, you could maybe still benefit from understanding how this intelligent behavior arises in these organisms, and take inspiration from these mechanisms.
> Sorry to pick at you for this, but how can you be certain of the many claims you've made here?
I didn't make revolutionary claims regarding the functioning of biological neural networks. The fact that neurons are sensitive to relative spike timing is evident in mechanisms like spike timing dependent plasticity (http://www.scholarpedia.org/article/Spike-timing_dependent_p...). The "complex chemistry" that I mention is behind cognition is evident if you open any neurology book, for instance Principles of Neural Science by Kandel.
Then, I say that simulation (and in particular simulation of development as well as learning) is the right road to understanding biological neural systems, but I took care to mention that it is what I believe, not what I know.
Your use of plethora was completely right. For some reason giardini thinks it is a very obscure and "intellectual" word but I disagree. In Google's English corpus it is used about 1/3 as often as the word "neuron". http://books.google.com/ngrams/graph?content=plethora%2Cneur...
I see, thanks. And that's a really nice tool, this n-gram viewer. Thanks for the link!
how can you be certain of the many claims you've made here?
It says most of this in the article. e.g. "It turns out that social behavior in the worm is controlled by a pair of neurons called RMG. The two RMG neurons receive input from various sensory neurons that detect the several environmental cues that make worms aggregate. RMG integrates this information and sends signals to the worm’s muscles." You have to understand genetics, smells, sensors, neurons, and muscles before you can explain why worms in nature tend to be together, but worms in the lab stay by themselves.
It says most of this in the article. e.g. "It turns out that social behavior in the worm is controlled by a pair of neurons called RMG. The two RMG neurons receive input from various sensory neurons that detect the several environmental cues that make worms aggregate. RMG integrates this information and sends signals to the worm’s muscles." You have to understand genetics, smells, sensors, neurons, and muscles before you can explain why worms in nature tend to be together, but worms in the lab stay by themselves.
C. elegans (a type of nematode) is even simpler, with exactly 302 neurons and ~15k synapses. But despite having the complete connectome, there has to date been only limited success at simulating the whole worm. Although see: https://code.google.com/p/openworm/
Edit: Also see the NYT article below.
Edit: Also see the NYT article below.
How do you know when you've simulated a worm's brain correctly? I mean, they don't actually do much, do they?
Worms (C. elegans specifically) have specific well-documented responses to certain stimuli. You can apply those same stimuli to your simulated worm and see if it responds like a typical real worm.
Sort of like a wormy unit test. Strange thought!
Actually that's pretty much what they are. There us a whole battery of stimulus stimulus-response tests that are essentially used as a regression test suite to figure out what deleterious (or even beneficial) effect a given mutation has. Of course these "tests" must be manually carried out by researchers on live worms.
See this link posted by "zb": http://www.nytimes.com/2011/06/21/science/21brain.html?pagew...
The problem is that real neurons are way more complex than those simple functions AI guys build their networks of.
"this wasp can somehow [...] find the right places to lay its eggs"
I bet this wasp is playing the "you do not need eyes to hit bullseye if you throw a million darts" game. It is sufficien t if some of these wasps, almost by accident, happen to lay their eggs in the right place.
I bet this wasp is playing the "you do not need eyes to hit bullseye if you throw a million darts" game. It is sufficien t if some of these wasps, almost by accident, happen to lay their eggs in the right place.
I'd love to see this flight in action. I would expect that at their size, they are pretty much limited to going where the primary air current brings them. With perhaps a tiny amount of individual influence.
I was going to say the same thing, coding for such behaviors has to either be pretty simple, or there is more going on here than just neurons.
While I cannot say for certain that neurons are not the only thing responsible for these behaviors, I'm fairly confident in saying that we don't know nearly enough about neurons to assume that 7,400 equates to "pretty simple".
It's probably "pretty simple" compared to 340,000.
That said, I wonder whether some of the complexity was effectively pushed down into complicating behavior of the individual neurons. If there were strong evolutionary pressures pushing down the number of neurons (as seems likely, for so extreme a reduction) it'd be interesting to see what kinds of hacks were included to get it there.
That said, I wonder whether some of the complexity was effectively pushed down into complicating behavior of the individual neurons. If there were strong evolutionary pressures pushing down the number of neurons (as seems likely, for so extreme a reduction) it'd be interesting to see what kinds of hacks were included to get it there.
I find the implied range of cell sizes to be amazing. (Informally, it's tempting to view all microscopic biological entities as being similarly small; they're not.)
Here's a cool visual of relative cell sizes and scale:
http://learn.genetics.utah.edu/content/begin/cells/scale/
Here's a cool visual of relative cell sizes and scale:
http://learn.genetics.utah.edu/content/begin/cells/scale/
The amoeba is as big as a grain of salt? It must be visible with the naked eye, then, no? I had no idea it was that big...
What are the major technical barriers before we can identify the input and output channels to this insect's brain and start iterating through all possible input values, recording the corresponding output values? And once we can do that, could we use that data to fly a virtual insect around a virtual world?
http://www.nytimes.com/2011/06/21/science/21brain.html?pagew...
This article suggests that it's computationally infeasible even for an organism with only 302 neurons, all of which have already been completely mapped.
This article suggests that it's computationally infeasible even for an organism with only 302 neurons, all of which have already been completely mapped.
Oddly enough, there's another story on the front page that address this guy:
http://news.ycombinator.com/item?id=4208454
http://news.ycombinator.com/item?id=4208454
Honest newbie question: Why would we even want to do that? Exclusively for academic medical purposes? (ie learning how our body works)
It seems to me, that since evolution is highly imperfect, that trying to mimic living beings, might not be the best idea. While we do mimic them a lot, mostly it seems to be for learning until we can do better. But in this case. Since it's so expensive and not viable to mimic them properly. Shouldn't we just try to come up with our own algorithms instead of trying to copy nature's historically bloated algorithms?
That seems to be a lesson we learned from machine learning. I remember when neural networks were first being talked about seriously a decades or so ago. The goal seemed to be to try to mimic neurons just for mimicing neurons sake. Many critics would say we should probably try other learning algorithms that were obviously more efficient. And nowadays we barely see actual neural networks being used seriously in commercial production simply because we have better algorithms that are not trying to emulate nature, just because.
Wouldn't trying to emulate a virtual insect be repeating the same mistake? If all we want is to design a virtual robot that can look for food and control its wings. I sure as hell don't need thousand something emulated neurons to build that.
It seems to me, that since evolution is highly imperfect, that trying to mimic living beings, might not be the best idea. While we do mimic them a lot, mostly it seems to be for learning until we can do better. But in this case. Since it's so expensive and not viable to mimic them properly. Shouldn't we just try to come up with our own algorithms instead of trying to copy nature's historically bloated algorithms?
That seems to be a lesson we learned from machine learning. I remember when neural networks were first being talked about seriously a decades or so ago. The goal seemed to be to try to mimic neurons just for mimicing neurons sake. Many critics would say we should probably try other learning algorithms that were obviously more efficient. And nowadays we barely see actual neural networks being used seriously in commercial production simply because we have better algorithms that are not trying to emulate nature, just because.
Wouldn't trying to emulate a virtual insect be repeating the same mistake? If all we want is to design a virtual robot that can look for food and control its wings. I sure as hell don't need thousand something emulated neurons to build that.
>Shouldn't we just try to come up with our own algorithms instead of trying to copy nature's historically bloated algorithms?
People are working on this problem from both sides. AI on one and biological simulation on the other. We don't know which will realize its goals first.
People are working on this problem from both sides. AI on one and biological simulation on the other. We don't know which will realize its goals first.
Because Science, man.
You don't do things in science because they are practical (you just write that on the grant applications), in fact you do many things largely because they are impractical.
It's about wonder and lust for knowledge and all that. It just so happens that the theoretical ground work for all technology piggy backed on this motivation.
You don't do things in science because they are practical (you just write that on the grant applications), in fact you do many things largely because they are impractical.
It's about wonder and lust for knowledge and all that. It just so happens that the theoretical ground work for all technology piggy backed on this motivation.
You might find this project at Caltech interesting. They are trying to model a worm:
http://caltech.wormbase.org/virtualworm/
I imagine one can eventully extend it from simply representing anatomy to "activating it".
http://caltech.wormbase.org/virtualworm/
I imagine one can eventully extend it from simply representing anatomy to "activating it".
I was interested in this too.
Shallow googling found, that there are problems with emulating even more simple organisms: http://lesswrong.com/lw/88g/whole_brain_emulation_looking_at...
I wonder why the Blue Brain guys don't work on c. elegans? They claim they can simulate full neocortical columns, a c. elegans should be no problem and they could compare the behavior.
That's assuming they are stateless (i.e. that they can't learn), so that the ordering of input triggering has no effect on outputs.
BTW: some small spiders press their brains not only into their abdomens, but into their legs. e.g. http://news.nationalgeographic.com/news/2011/12/111219-spide... Spiders gotta spin.
BTW: some small spiders press their brains not only into their abdomens, but into their legs. e.g. http://news.nationalgeographic.com/news/2011/12/111219-spide... Spiders gotta spin.
Arguably, even human brains work that way. You can't really draw a line across part of the CNS and say "This is where the brain stops." The retina, for instance, is almost as much a part of your brain as it is your eye.
By the same token, it's not always easy to classify complex movements as reflexes or thought-guided actions. Cut a chicken's head off while it's walking, and it will keep going for a while. My understanding (IANABiologist) is that the same is true for a human.
By the same token, it's not always easy to classify complex movements as reflexes or thought-guided actions. Cut a chicken's head off while it's walking, and it will keep going for a while. My understanding (IANABiologist) is that the same is true for a human.
Research has also shown that the neurons in the digestive system can control it autonomously, even when disconnected from the brain:
http://en.wikipedia.org/wiki/Enteric_nervous_system
http://en.wikipedia.org/wiki/Enteric_nervous_system
This was one of my first thoughts as well, sort of. Alternatively, is it possible to model each neuron when there are only around 8k? Maybe estimate the visual, audio, and tactile bandwidth of the wasp and feed the simulated neurons a stream of simulated environmental data and just let the connections evolve.
There are not many neurons, but there are many (too many) possible connections. To find the right ones, nature took several billion years. Even with the fastest computers, it would take a considerable amount of time (note that the lifetime of these wasps is a few weeks at most, so evolution happens very fast).
I think we've yet to match the (information processing ability)/(energy consumption x size) ratio of wetware.
Sure, but we can fire up a room full of servers instead.
Short and consistent feedback latency is crucial for movement coordination. Think adapting to wind conditions and landing/liftoff in rough conditions.
At small enough scales -- and this is one of them -- air is not just a fluid, it is a viscous fluid. Movement will be more like swimming than flying; there won't be any landing or liftoff, so much as grabbing on and pushing off.
It's a virtual environment; presumably, one could set the "wall clock" rate of the sim to account for inter-room, or even inter-continental comms latencies.
So, this is really just an allegory about Minimum Viable Products, right?
The headline is a bit misleading, though. Amoebas seem to be just used as a reference point, where I kept expecting more salience - some adaptation of the wasp to deal specifically with the fact that it was smaller than an amoeba in particular, rather than just with the fact that it was small. Nevertheless, confusion aside, it's fascinating stuff!
The wings are amazing. I'd really like to see a video of one of these in flight, although I imagine that would be a difficult thing to capture.
this is rather cool but just to nitpick "one single cell" does not always imply "small" there are a few which are visible to the naked eye (think eggs)
Do you have any references for the egg thing? I have a hard time picturing that? Aren't there individual cells inside a chicken egg (before it starts developing?)
No, an ostrich cell really is a single cell.
http://en.wikipedia.org/wiki/Egg_yolk
http://en.wikipedia.org/wiki/Egg_yolk
Does anyone know how the nucleus destruction happens? Is there any analogy to regularization/sparsification in machine learning? Is there some kind of process that destroys the nucleus of the least useful neurons?
The hypothesis advanced by the article is that there is no loss of function. The neurons are packed with enough proteins to function for five days. The nuclei are just dead weight.
In mammals, red blood cells don't have nuclei either and can live up to 90 days in physiologic conditions.
Educated guess resp. nuclei destruction mechanism: probably through partial apoptosis (nucleus fragmentation), autophagia, and/or expulsion and phagocytosis by other cells.
In mammals, red blood cells don't have nuclei either and can live up to 90 days in physiologic conditions.
Educated guess resp. nuclei destruction mechanism: probably through partial apoptosis (nucleus fragmentation), autophagia, and/or expulsion and phagocytosis by other cells.
Wow, these little guys are incredible! I had no idea such complexity could evolve at that scale. I think these species deserve a mention in science classrooms.
Well as long as you make it clear that they were created that size because God likes a challenge - rather than any sort of adaptation to their environment - that should be OK
Hmmm, downvotes. Hey Fred! Get in here, and bring the spare sensor filaments. I think we have some sarcasm detectors on the fritz.
https://en.wikipedia.org/wiki/Poes_law
https://en.wikipedia.org/wiki/Poes_law
Even so, it's just flame-bait.
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Is that wasp truly really small? Or is that amoeba just really big?
http://en.wikipedia.org/wiki/Xenophyophore http://en.wikipedia.org/wiki/Caulerpa https://en.wikipedia.org/wiki/Valonia_ventricosa