

How tiny wasps cope with being smaller than amoebas - mike_esspe
http://blogs.discovermagazine.com/notrocketscience/2011/11/30/how-fairy-wasps-cope-with-being-smaller-than-amoebas/

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colanderman
On the opposite side of things, here are some of the largest single-celled
organisms:

<http://en.wikipedia.org/wiki/Xenophyophore>
<http://en.wikipedia.org/wiki/Caulerpa>
<https://en.wikipedia.org/wiki/Valonia_ventricosa>

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cs702
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!

~~~
Xcelerate
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?

~~~
agravier
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.)

~~~
giardini
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?

~~~
agravier
(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...](http://www.scholarpedia.org/article/Spike-
timing_dependent_plasticity)). 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.

~~~
sp332
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...](http://books.google.com/ngrams/graph?content=plethora%2Cneuron%2Csynaptic%2Cjefe%2C+guapo&year_start=1800&year_end=2000&corpus=0&smoothing=3)

~~~
agravier
I see, thanks. And that's a really nice tool, this n-gram viewer. Thanks for
the link!

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kjhughes
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/>

~~~
StavrosK
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...

~~~
jacobolus
Yes, many kinds of amoebas are visible to the naked eye. Some grow to several
millimeters long.

~~~
einhverfr
I used to have a culture of paramecia and noticed I could see a paramecium
with the naked eye.

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raldi
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?

~~~
vibrunazo
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.

~~~
Bjartr
>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.

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knowtheory
So, this is really just an allegory about Minimum Viable Products, right?

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dllthomas
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!

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patdennis
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.

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riffraff
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)

~~~
tocomment
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?)

~~~
wazoox
No, an ostrich cell really is a single cell.

<http://en.wikipedia.org/wiki/Egg_yolk>

~~~
tocomment
So are you saying the whole yolk divides in half continually to form the
embryo? Is there a cell membrane around the yolk?

~~~
masklinn
> So are you saying the whole yolk divides in half continually to form the
> embryo?

No, the yolk does not "divide", the developing embryo feeds on its content.

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robrenaud
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?

~~~
pygy_
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.

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nemo1618
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.

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excuse-me
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

~~~
raganwald
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>

~~~
excuse-me
Doesn't HN let you see a poster's other posts/comments? My background should
be pretty obvious!

ps How many christian fundies post on HN anyway?

~~~
vacri
HN is a fairly humourless place independent of religious leanings.

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Apocryphon
Is that wasp truly really small? Or is that amoeba just really big?

