Slashdot Mirror
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."
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...
Apology to Ubuntu forum.
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.
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?
Science is all about the baby steps. You can't talk about determining the weights before you know what the connections are.
Can you be Even More Awesome?!
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...
Physicists get Hadrons!
and it's only like 302 neurons,so,it's possible to write a simulator of it?