Mapping the Brain's Neural Network

Ponca City, We Love You writes "New technologies could soon allow scientists to generate a complete wiring diagram of a piece of brain. With an estimated 100 billion neurons and 100 trillion synapses in the human brain, creating an all-encompassing map of even a small chunk is a daunting task. Only one organism's complete wiring diagram now exists: that of the microscopic worm C. elegans, which contains a mere 302 neurons. The C. elegans mapping effort took more than a decade to complete. Research teams at MIT and at Heidelberg in Germany are experimenting with different approaches to speed up the process of mapping neural connections. The Germans start with a small block of brain tissue and bounce electrons off the top of the block to generate a cross-sectional picture of the nerve fibers. They then take a very thin slice, 30 nanometers, off the top of the block. 'Repeat this [process] thousands of times, and you can make your way through maybe the whole fly brain,' says the lead researcher. They are training an artificial neural network to emulate the human process of tracing neural connections to speed the process about 100- to 1000-fold. They estimate that they need a further factor of a million to analyze useful chunks of the human brain in reasonable times."

Comments

Did I get that right?

[UbuntuDupe]

They are training an artificial neural network to emulate the human process of tracing neural connections to speed the process about 100- to 1000-fold.

So, they're training a neural network to automate the process of mapping a neural network, in the hopes of creating an intelligence that they can train to automate other processes?

My brain hurts...

What constitutes a "map" here differs from elegans

[jpfed]

One reason we could do the aforementioned mapping with C. elegans is that the worm's neurons are always laid out the same way from worm to worm. This is not the case for humans, and probably not the case for any vertebrate.

Re:Missing tag

[Feminist-Mom]

Not to mention - and I a sure I'll get modded down here - is that neural networks aren't very effective. They get a lot of hype in the media, and people who don't work in optimization like them. They sound appealing and cool, but there are other methods that are much better. If you look at the liturature, there is not that much hard science behind them. Some statistical mechanics people have some results, but they really are just a fad, like fractals or catastrophe theory. But bear in mind LOTS of people in applied areas immediately jump to the conclusion that neural networks are great, especially computer vision and robotics people (where I used to work.)

Interesting

[rm999]

Sounds interesting, but is a map enough to understand the brain? I know in artificial neural networks, the actual structure isn't as important as the weights on the nodes. Will hitting the brain with electrons be enough to give us an understanding of these "weights", or just the connections between them?

Re:Interesting

[unixfan]

Hell, it will be fun to see what they figure out to be the capacity of the brain, as far as how much information it can store.

I know visually we are looking at least at something around 24 frames per second. The eye is supposed to have a resolution of around 1000 dpi. Not sure how to measure the viewing area. But let's say it is lesser and lesser resolution the higher the angle. Let's say, just to have a number, that we have a 16:9 viewing ratio at two feet distance. Lets say it's three feet wide. That should add up to something around 36"x20". At 1000dpi that would be something like 729,000 dots. Time 24 per second becomes 17,496,000 dots per second.

Though I think people who have dissected eyes and the stem to the brain would have a hard time quite understanding how that dpi reaches the brain.

On a daily basis, if we don't count 8 hours sleeping, we still come up with 280 million pieces of data in 16 hours.

If an average brain is 1400 cubic centimeters or 85 cubic inches, how many cells could it have if we say it is solid. Best case scenario.

I see numbers of 9,350 cells per cubed millimeter which is 93,500 per cube centimeter. With the above brain size we are looking at 130,900,000 cells in a solid brain.

To complicate things further, how many days of memory do you have? Most people have problems remembering all details but then some people have photographic memory. Which as far as I can see means that all of us has the potential to have it.

The running question is how much info is stored in each cell.

Of course that's not including all the other senses and impressions that are stored.

Re:Interesting

[smallfries]

When you pull figures from your arse, you don't actually add anything to the discussion. All that we've learned is that your arse is rather large, and you are used to removing things from it.

Even if your (incorrect assumptions) were correct, 36" x 20" at 1000dpi would be 36000 pixels x 20000 pixels = 720M pixels. Clue: dpi is a scalar measure rather than area.

Of course, the human eye does not work anything like that. Rather than farting numbers I spent 10 seconds on Google to find this which looks into the question of Visual Acuity. The "high-res" part of the eye is a very small circle with about 120 "dots" across its diameter.

As we do not resolve entire "frames" in a single go, the concept of a frame-rate is completely ludicrous. Your argument earlier in the thread about observing skipping when seeing a high speed stimuli doesn't show evidence of a *periodic* frame rate. It just shows that there is a *minimum* temporal resolution. One does not imply the other, especially when the eye is processing asychronous input (from rods and cones).

Although you don't believe that the brain fills in the missing images with educated guesswork, we've already established that what you believe is shit. Most (if not all) neuroscientists have accepted that the high resolution continuous visual imagery that we see is mostly an illusion produced by the mind. There are many well reported experiments that provide evidence of this. You should look for anything on Visual Illusions - there are far too many decent results in peer reviewed journals for me to spend time looking for you. Change Blindness is a related phenomena.

Finally you've cooked up some stupid figures for the number of cells in a brain. Why do you feel the need to demonstrate how stupid you are? The actual numbers (which you get wrong by 3 fucking orders of magnitude) are in the summary of the article! How hard is it to read the 100 billion neurons at the top of the page.

So next time you feel the need to pontificate needless about something that you don't know anything about. Don't. You, sir, are a thief of oxygen and your pointless ramblings have made everyone reading this article collectively dumber.

PS Feel free to mod me flamebait, as I am clearly annoyed. But when you do so remember that the everything the parent poster wrote was incorrect and that I have pointed out to him where he is wrong.

Horse, push cart.

[zippthorne]

Science is all about the baby steps. You can't talk about determining the weights before you know what the connections are.

Re:Computational Complexity

[Fourier404]

If you emulate a human brain in real time, as well as connections to emulated eyes, ears, and mouths, you just have to talk to it for a couple years and it'll learn English. =D

Feedforward NNs versus biological NNs

[benhocking]

Many engineers have studies feed-forward neural networks and found them to be far inferior to other solutions. Of course, our brains use recurrent neural networks, which, unfortunately for engineers, are very difficult to analyze. There are many secrets yet to be teased out of these neural networks, but much progress has already been made. Researchers in our lab, for example, have demonstrated how introducing random synaptic failures improves not only energy efficiencies, but also the cognitive abilities of simulated neural networks. I'm currently researching the effects of variable activity (as measured in a biological neural network by an EEG), and I dare say there's a lot more that we don't know about these networks than we do.

Re:Missing tag

[Space cowboy]

Perhaps you didn't get much out of neural networks, but my PhD thesis was on the similarities between a kohonen network and relaxation-labelling equations. Part of it is up on my blog (I haven't actually got as far as that bit yet, but the groundwork is there).

A neural network (well, anything more complex than the single-layer perceptron anyway) is an arbitrary classifier. I'm curious as to why other methods are "much better". Unless you do an exhaustive search of the feature-space, all classifier methods are subject to the same limitations - local maxima/minima (depending on the algorithm), noise effects, and data dependencies. All of the various algorithms have strengths and weaknesses - in pattern recognition (my field) NN's are pretty darn good actually.

It's also a bit odd to just say 'neural networks' - there are many many variants of network, from Kohonen nets through multi-layer perceptrons, but focussing on the most common (MLP's), there's a huge amount of variation (Radial-basis function networks, real/imaginary space networks, hyperbolic tangent networks, bulk-synchronous parallel error correction networks, error-diffusion networks to name some off the top of my head), and many ways of training all these (back-prop, quick-prop, hyper-prop, batch-error-update, etc. etc.) I guess my point is that you're tarring a large branch of classification science with a very broad brush, at least IMHO.

Not to mention that this is all the single-network stuff. It gets especially interesting when you start modelling networks of networks, and using secondary feature-spaces rather than primary (direct from the image) features. Another part of my thesis was these "context" features - so you can extract a region of interest, determine the features to use to characterise that region, do the same thing for surrounding regions, and present a (primary) network with the primary region features while simultaneously(*) presenting other (secondary) networks with the features for these surrounding regions and feeding the secondary network results in at the same time as the primary network gets its raw feature data. This is a similar concept (if different implementation) to the eye's centre-surround pattern, and works very well.

If you work through the maths, there's no real difference between a large network and a network of networks, but the training-time is significantly less (and the fitness landscape is smoother), so in practice the results are better, even if in theory they ought to be the same. I was using techniques like these almost 20 years ago, and still (very successfully, I might add) use neural networks today. If it's a fad, it's a relatively long-running one.

Simon.

(*) In practice, you time-offset the secondary network processing from the primary network, so the results of the secondary networks are available when the primary network runs. Since we still run primarily-serial computers, the parallelism isn't there to run all of these simultaneously. This is just an implementation detail though...

they got the complete neural map of C.Elegans

[Z80a]

and it's only like 302 neurons,so,it's possible to write a simulator of it?

Re:they got the complete neural map of C.Elegans

[lazybratsche]

Well, that's the hope, and a major source of appeal for the humble nematode. Unfortunately, that's still far beyond what we know right now. The physical map of every neuron and their connections has been complete for decades. Still, despite a whole lot of effort, researchers are still working to piece together small functional circuits for the simplest of behaviors. A lot of complexity arises in neural circuits -- one physical circuit can contain several independent functional circuits, depending on the types of inputs.

The best current knowledge of C. elegans neurophysiology involves qualitative descriptions of small circuits, involving a few dozen neurons. Unfortunately, while you can do a lot of good behavioral studies and other experiments, it's impossible to directly record the activity of specific neurons. Also, it turns out that some "neural" functions are actually performed by other cells. For example, one pattern generator in the digestive tract actually resides in intestinal cells instead of neurons -- my lab is working on the genetics involved.

This shit gets complicated, fast.

IAAUCER
I am an (undergrad) C. Elegans researcher

Re:Missing tag

[mesterha]

A neural network (well, anything more complex than the single-layer perceptron [wikipedia.org] anyway) is an arbitrary classifier. I'm curious as to why other methods are "much better". Unless you do an exhaustive search of the feature-space, all classifier methods are subject to the same limitations - local maxima/minima (depending on the algorithm), noise effects, and data dependencies. All of the various algorithms have strengths and weaknesses - in pattern recognition (my field) NN's are pretty darn good actually.

Actually, more recent methods don't have local maxima/minima. Something like a support vector machine optimizes an objective function. Of course, this is somewhat of a tangent, in that the objective function might not be a useful metric for performance, but people have shown that the minimum objective function value of a SVM does relate to its generalization performance. It's a little disconcerting that a NN has an objective function but that it can find it's minimum or that the minimum doesn't give good performance on test data (over-fitting)...

Of course, part of the NN's problem stems from the fact that it is an arbitrary classifier. It's hard to give generalization results for an algorithm that has an infinite VC dimension. (There are techniques to restrict the size of the weights to give some guarantees.) However, this doesn't mean NNs can't perform well in practice. It probably means that the current theoretical analysis is somewhat flawed in relation to the real world.

So have you ever compared your NN algorithms with the popular algorithms of the day such as SVMs with kernels or boosting algorithms. Also, are your NN algorithms generic or do you heavily customize and tweak to get good performance.