The Map of Science: a directed citation graph showing the interdependence of fields | Hacker News Reader

The Map of Science: a directed citation graph showing the interdependence of fields

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23 pointsDaniFong22 comments

If neuroscience is ever going to truly succeed, that link with mathematics better get more firmly established (even as it's surely underestimated based on the journals sampled). With the number of neurons and possible synapses, there needs to be advanced computational work to tame the complexity. The NIPS conference, for instance, is already decently big. But we need many more folks from that physics and engineering cluster.

Hah, nice catch!

It seems to me (though I am very biased!) that most of the landmark papers in the aggregate behavior of neural networks are mostly in physics (eg: Hopfield networks) and in computer science (eg: Perceptrons, Minsky).

You're right, but it quickly moved away from physics. McClelland and Rumelhart also did the seminal work in cognitive science in the 80s.

Here's an upcoming review: http://www-psych.stanford.edu/~jlm/papers/McClellandIPTOPiCS...

Then there are folks like Sejnowski (http://www.salk.edu/faculty/sejnowski.html; http://en.wikipedia.org/wiki/Terry_Sejnowski) who pushed the field more into biology.

Still, I'm really surprised by the current chasm, if that map is reflective of the state of the field.

EDIT: For spelling.

Sejnowski pushed it back into physics (sort of) with his information maximization work, very interesting...

http://papers.cnl.salk.edu/PDFs/An%20Information-Maximizatio...

Thanks for the links.

Happy to help. I learn so much around here, it's rare I can return the favor.

Those are artificial neural networks -- not necessarily anything like the real ones.

Some are. Some aren't. It depends on the instantiation details. Folks though seem to be much more ready to implement at a biologically-plausible level these days than they were even a few years ago.

My point is that we learn much more about neurobiology from a biological viewpoint than by studying artificial networks from a computer science perspective. If anything, the biology informs the computer science as you suggested.

It's more bidirectional than that. Higher-order cognition (language, memory, even perception and attention) isn't so easily reducible. Take for instance the hippocampus. Sure, we can simulate circuitry with precision but that doesn't explain memory formation and retrieval. More "artificial" approaches can help to explain systems from the top down even as biological constraints are more rigid from the bottom up. Both modeling approaches are likely to meet somewhere in the middle. The computational shortcuts in the more abstract models (e.g. backprop) are really just a shorthand to allow investigators to focus on less biologically-driven details or those that are not now understood in biological terms.

For instance, I know of one group using analytic techniques from social networks to correlate brain regions in fMRI data. Is the brain a massive social network? I don't think any one would say that literally. But right now, that approach is as good as any other to examine n-dimensional relationships in highly complex data.

This discussion reminds me of Paul Krugman's argument for cartoon models. I personally think that we can isolate, and therefore explain, simple parts of aggregate neural behavior by artificial construction more easily than we can by doing careful biology.

Incidentally, you might be interested to know that restricted boltzmann machines are much more biologically plausible than backprop, and seem to work faster and better.

I think you're misunderstanding the role of "neural networks" in academia. NIPS has a lot of value to the machine learning and AI communities but, beyond vague inspiration, it has almost no connections to neuroscience. There is a substantial body of work in computational modeling of neuronal behavior, but this stuff is much messier (PDEs with biologically determined constants) and limited in scope than the papers that appear at NIPS.

edit: Relevant conferences in computational neuroscience -

* http://cosyne.org/c/index.php?title=Cosyne_09

* http://www.cnsorg.org/2009/

* http://icms.org.uk/workshops/mathneuro2009

Sorry, but the CS approaches have much to share with neuroscience even in these very early (and messy) days.

Machine learning is now used to analyze neuroimaging data and predict behavioral responses.

http://polyn.com/struct/NormEtal06_TICS.pdf

I also know of one group merging their intelligent tutors with fMRI data to predict types of confusion and offer better suggestions.

I don't really know much about neuroscience proper. I tend to look at the simplified artificial models and synthesize what might be possible, rather than look at all at the biology.

I'm surprised the link between neuroscience and economics has not shown up yet.

We're working on it ;)

Also, something interesting I found using the main site http://eigenfactor.org/map/ was how music was linked neuroscience. What a cool site!