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AFAIK, Markram (leader of Blue Brain) isn't officially connected to SyNAPSE. He's one of the external advisors for a closely related project housed out of my department, though, so you could say he has an informal link. (it's a small field)
Well, it depends. :-)
Drop me an email with some information on where you're coming from and what kinds of roles you're looking for and I may be able to point you to the right people. (bchandle at gmail)
The BBC article left out a critical constraint from DARPA. The final deliverable (with the "complexity of a cat's brain") isn't just a model or a simulation. It has to be a physical artifact which requires no more than two liters of volume and consumes no more than two kilowatts of power (this information comes from the DARPA BAA). So yes, prototyping will be done in software, but to reach that kind of efficiency, drastically new hardware is called for.
I'm not intimately familiar with IBM's plans, but this is one of the applications HP has lined up for the memristor technology they've been working on (HP is another one of the three prime contractors on the original DARPA grant). The benefit to the HP approach is that data and computation are both local to the applicable memristor, which is much closer to a neural system. That means no time or energy is wasted shuttling data around and the entire system state can be updated in parallel.
For an idea of why this is so exciting, keep in mind that HP plans build memristors at about a density of a trillion per square centimeter, clocked at about a kilohertz. You get the rough equivalent of one floating point operation per memristor per cycle. At this estimated manufacturing density, the expected performance of these things is on the order of a petaflop per square centimeter, drawing on the order of tens of watts. It isn't really fair to make a comparison to Von Neumann machines since the architecture is so dramatically different and so application-specific, but for certain kinds of computations these new chips will be vastly faster and more efficient.
(for the sake of disclosure, I'm working on the DARPA SyNAPSE project, but not with IBM)
It doesn't necessarily matter that he wasn't working with highly dangerous or toxic chemicals. My roommate works in quality at a medical devices company, and many of the regulations they deal with have to do with things like proper labeling, storage, disposal and the like. And their product is chemically inert.
While I'd hope that chem regulations aren't quite as stringent as pharma or biomedical, it isn't unfathomable that the guy broke some or many of them. Unless someone here is knowledgeable of the relevant regulatory environment (which I'm certainly not), it's unfair to make any accusations based on a blog post and a terse newspaper story.
Read the original article: http://www.telegram.com/article/20080809/NEWS/808090323/1008...
Neither source is all that complete, but the original article indicates this guy was violating zoning law, as well as safe handling and disposal regulations. I'm a scientist, and I sympathize, but it's unfair to claim "home science is under attack" from the available evidence.
I strongly agree. The PhD is an apprenticeship, so the people you'd be working for (and with) are the most significant factor. Make sure you fit with the research and social philosophies of a program before applying if you can, and certainly before accepting an offer.
As you leave school and prepare to enter industry or academia, my experience has generally been that the strength of the recommendations backing you matters far more than whatever rank your institution may have.
As you mentioned AI/machine learning, I believe these factors are even more important. There are some very distinct schools of thought when it comes to those things, so make sure you look for philosophical compatibility when picking programs. AI has a few deep schisms and widely separated sub-fields, so tread carefully.
It may be worth it to consider some of these: What's your philosophy of mind? Symbolic/statistical/neural? How important is biology/neuroscience when looking at artificial intelligence? What general approach to AI/machine learning most interests you?
Try taking a look at part II of this: http://chimprawk.blogspot.com/2008/05/twitter-imagined-ident...
(complements of HN a day or two ago)
Personally, I'm still not a user, but the conceptualization of Twitter as more than a simple messaging service significantly raises its value in my eyes.