How tiny wasps cope with being smaller than amoebas
blogs.discovermagazine.com
blogs.discovermagazine.com
http://en.wikipedia.org/wiki/Xenophyophore http://en.wikipedia.org/wiki/Caulerpa https://en.wikipedia.org/wiki/Valonia_ventricosa
That ridiculously tiny neural network is one freakingly efficient computing device!
To someone more knowledgeable in this subset of biology, is this possible?
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.)
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?
> 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.
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.
Edit: Also see the NYT article below.
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 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.
Here's a cool visual of relative cell sizes and scale:
This article suggests that it's computationally infeasible even for an organism with only 302 neurons, all of which have already been completely mapped.
http://caltech.wormbase.org/virtualworm/
I imagine one can eventully extend it from simply representing anatomy to "activating it".
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.
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.
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.
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.
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.
No, the yolk does not "divide", the developing embryo feeds on its content.
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.
ps How many christian fundies post on HN anyway?
(reference to "Evolution from Space: A Theory of Cosmic Creationism" by Fred Hoyle)