<p>A (probably incomplete) list of ongoing whole brain simulation projects can be found at <a href="http://www.artificialbrains.com/" rel="nofollow">http://www.artificialbrains.com/</a>. However, based on the information reported on this site, it is sometimes hard do distinguish what already has been achieved and what is still in the planning stage. Nevertheless it gives a good overview to start with.</p>

CC BY-SA 3.0
2012-04-13T14:30:23.257Z
2012-04-14T20:09:57.030Z
2012-04-14T20:09:57.030Z
H.Muster
<p>A few years ago I wrote a research paper for college on neural networks.  At the time IBM's <a href="http://en.wikipedia.org/wiki/Blue_Brain_Project">Blue Brain</a> was the clear winner. Some rumor went around that they were close to emulating a brain the complexity of a cat; <a href="http://news.discovery.com/tech/cat-brain-computer-hype.html">this has since been debunked</a>. </p>

<p>At the time their research was groundbreaking and previously even emulating the brain of a C. Elegans <a href="http://www.skeptic.com/reading_room/artificial-intelligence-gone-awry/">seemed far-fetched</a>.</p>

<p>Describing the complexity of neural networks is <a href="http://math.bu.edu/people/mkon/pppp11.pdf">complex</a> but I'll define <strong>number of synapses</strong> or neurons as an easily measurable scale of complexity.</p>

<p>What is the most complex working artificial neural network? What is the relative complexity of this network compared to an animal's brain?</p>

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2012-02-02T23:42:05.173Z
define complex...
Jeff
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2012-02-03T00:31:37.450Z
@ArtemKaznatcheev what does neurobiology have to do directly with either in this context? They're both different concepts but neural-network is usually used to refer to artificial ones these days so just that one of the two does make sense.
Zelda
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2012-02-03T00:34:36.853Z
@Jeff for the purposes of this question number of synapses should be sufficient as that (and count of neurons, which correlates with it) is how artificial ones are generally gauged. I've edited the question. Some better reading on neural net complexity is here: http://math.bu.edu/people/mkon/pppp11.pdf
Zelda
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2012-02-03T01:20:54.877Z
@Ben I don't think number of synapses or neurons (termed *neural complexity* in the paper you cite) is an accurate way to measure complexity in the way you are thinking about it. I can build an ANN with a billion hidden units that does nothing but compute a XOR function. The paper's other form of complexity (*informational complexity*) is very specific to the function you are  trying to implement. Perhaps you are after the question of biological realism? That seems to be one of Blue Brain's primary objectives, though admittedly I don't know a lot about the project.
Jeff
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2012-02-03T01:58:30.267Z
@Jeff practical ANNs like Blue Brain always go for biological realism, obviously an ANN that isn't representative of an NN isn't acceptable but that's not a good usable metric. The metrics I always hear to compare ANNs is always either neuron/synapse count or complexity compared to an animal (simple = C. elegans, complex = cat, Holy Grail = Human). If there's a better measure I'm not aware of it but would love to know of it
Zelda
CC BY-SA 3.0
2012-02-03T02:13:40.760Z
@Jeff I think perhaps now this is the more useful question: http://cogsci.stackexchange.com/questions/252/what-is-an-effective-metric-of-complexity-for-an-artificial-neural-network
Zelda
CC BY-SA 3.0
2012-02-03T02:44:27.807Z
@Ben good question! hehe
Jeff
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2012-02-06T22:10:44.037Z
@BenBrocka you might be interested in [this article](http://www.huffingtonpost.com/mark-changizi-phd/artificial-brains-not-in-_b_1248948.html) about what-if-anything we should expect from building bigger and bigger models. It came to me via this [G+ page](https://plus.google.com/u/0/105594787035213157161/posts) which might also interest you.
Artem Kaznatcheev
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2012-02-06T22:31:46.303Z
@ArtemKaznatcheev sounds very interesting, added to my reading list :)
Zelda
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2014-04-08T17:33:20.620Z
heres a [survey late 2013](http://vzn1.wordpress.com/2013/10/28/deep-learning-ai-artificialreal-brain-research-leadscompendiumcritical-masssingularity/). note google is very far ahead with "deep learning" visual images.
vzn
<neural-network><artificial-intelligence>
What is the most complex artificial neural network created to date?
Zelda
# What is the most complex artificial neural network created to date?

(Asked by: username_0 on 2012-02-02)

A few years ago I wrote a research paper for college on neural networks. At the time IBM's [Blue Brain](http://en.wikipedia.org/wiki/Blue_Brain_Project) was the clear winner. Some rumor went around that they were close to emulating a brain the complexity of a cat; [this has since been debunked](http://news.discovery.com/tech/cat-brain-computer-hype.html).

At the time their research was groundbreaking and previously even emulating the brain of a C. Elegans [seemed far-fetched](http://www.skeptic.com/reading_room/artificial-intelligence-gone-awry/).

Describing the complexity of neural networks is [complex](http://math.bu.edu/people/mkon/pppp11.pdf) but I'll define **number of synapses** or neurons as an easily measurable scale of complexity.

What is the most complex working artificial neural network? What is the relative complexity of this network compared to an animal's brain?

**Tags:** neural-network, artificial-intelligence

> **Comments:**
> **username_1**: define complex...  
> **username_0**: @ArtemKaznatcheev what does neurobiology have to do directly with either in this context? They're both different concepts but neural-network is usually used to refer to artificial ones these days so just that one of the two does make sense.  
> **username_0**: @username_1 for the purposes of this question number of synapses should be sufficient as that (and count of neurons, which correlates with it) is how artificial ones are generally gauged. I've edited the question. Some better reading on neural net complexity is here: http://math.bu.edu/people/mkon/pppp11.pdf  
> **username_1**: @Ben I don't think number of synapses or neurons (termed *neural complexity* in the paper you cite) is an accurate way to measure complexity in the way you are thinking about it. I can build an ANN with a billion hidden units that does nothing but compute a XOR function. The paper's other form of complexity (*informational complexity*) is very specific to the function you are  trying to implement. Perhaps you are after the question of biological realism? That seems to be one of Blue Brain's primary objectives, though admittedly I don't know a lot about the project.  
> **username_0**: @username_1 practical ANNs like Blue Brain always go for biological realism, obviously an ANN that isn't representative of an NN isn't acceptable but that's not a good usable metric. The metrics I always hear to compare ANNs is always either neuron/synapse count or complexity compared to an animal (simple = C. elegans, complex = cat, Holy Grail = Human). If there's a better measure I'm not aware of it but would love to know of it  
> **username_0**: @username_1 I think perhaps now this is the more useful question: http://cogsci.stackexchange.com/questions/252/what-is-an-effective-metric-of-complexity-for-an-artificial-neural-network  
> **username_1**: @Ben good question! hehe  
> **username_2**: @BenBrocka you might be interested in [this article](http://www.huffingtonpost.com/mark-changizi-phd/artificial-brains-not-in-_b_1248948.html) about what-if-anything we should expect from building bigger and bigger models. It came to me via this [G+ page](https://plus.google.com/u/0/105594787035213157161/posts) which might also interest you.  
> **username_0**: @ArtemKaznatcheev sounds very interesting, added to my reading list :)  
> **username_3**: heres a [survey late 2013](http://username_31.wordpress.com/2013/10/28/deep-learning-ai-artificialreal-brain-research-leadscompendiumcritical-masssingularity/). note google is very far ahead with "deep learning" visual images.  

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## 1 answers

### Answer by username_4 on 2012-04-13. Score: 8

A (probably incomplete) list of ongoing whole brain simulation projects can be found at <http://www.artificialbrains.com/>. However, based on the information reported on this site, it is sometimes hard do distinguish what already has been achieved and what is still in the planning stage. Nevertheless it gives a good overview to start with.


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