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While interesting (especially from a funding perspective!), most of these approaches aren't quite as ground-breaking as one might think. Neuroscientists in acad

by apl 13y ago

While interesting (especially from a funding perspective!), most of these approaches aren't quite as ground-breaking as one might think. Neuroscientists in academia are doing most of these things. For optogenetics, see Deisseroth's work; for in vivo Ca2+ imaging in nematodes, refer to the work of Bargmann as well as the various Witesides collaborations involving microfluidics; pan-neuronal in vivo imaging is currently being pioneered by Engert at Harvard and a couple of Janelia Farm labs. These are massive efforts, and involve horrendous heaps of diligent busywork. This makes me the boring naysayer, but please don't be distracted by the startup-like appearance and the peculiar financing situation. It's possible but unlikely that the major obstacle here is simply the combination of available techniques! Honestly, what I'm most curious about are his thoughts on model-driven interrogation of an in vivo system -- biologists, and even computational neuroscientists, are a bit too hesitant when it comes to letting computers find and test hypotheses. In the age of highly advanced genetic techniques (e.g., binary expression systems in Drosophila or zebrafish) and 2-photon imaging, the process of actually evaluating hypotheses has become a bit old-fashioned...

DaniFong 13y ago
I think it's fair to say that the hope is to combine the state of the art technology to discover more scientifically than has been before known. Additionally, this pushes technology. The hope is to push the technology far enough a system such that not only do the components work, but you can build actual useful engineering systems with them.
Hitchhiker 13y ago
David's start is at 14 and not post 20 as most grad students. At minimum, he will end up making something like Mathematica like Wolfram and at max, he will change the world. The Big and Little Oh of this story are both extreme events.
- lmm 13y ago
  That's an awfully high "minimum". I knew several "geniuses" at that age (might even qualify myself, though admittedly I was only three years ahead of par at that age); most of them have gone on to fairly normal (though by no means unsuccessful) careers, and a couple burned out quite spectacularly. I don't think putting that weight of expectation on is helpful.
  - Hitchhiker 13y ago
    In deed, you remind me of Solon's warning. Perhaps the better way to put it is he's attacking one heck of a problem - and with the right detachment to both industry and academia - so the minimum is based more on the quality of problems than the individual ( as unique as that is in this particular case ). But yes, being an entrepreneur is an added layer, as you've to learn to arrange people to attain a larger goal than just research. And he shows signs of that even on the jobs page while avoiding the broken method of interviewing that is oft practiced, he's almost defining a boundary for relatively high signal from the applicants. His fluid approach is reminiscent of the caper that the Google guys pulled at Stanford.. and as it turns out, I think he also got funded by Larry.
davidad_ 13y ago
I like to describe my techniques as "not novel, just new." I'm absolutely building on the work of many others in academia. I've spent time with Engert at Harvard, Rex Kerr at Janelia, and some of Deisseroth's students, among others (including Boyden, Ramanathan, Manuel Zimmer in Vienna, Aravi Samuel, George Church, and more). I've only briefly met Bargmann and I loved the advice she gave me: "Hmm. That's probably not going to work, but...it's really worth trying." Unfortunately, most academic scientists are not in a position to risk their careers on such a bold and encompassing proposal. However, I benefit from this in some ways, because academics who are interested in this problem often work on a small piece of it instead of going for the whole thing, and are then incentivized to share that piece with me to integrate with all the other pieces, so they can see their contribution realize its full potential. I wouldn't underestimate the technical demands of the project - it certainly would have been unthinkable 10 years ago. Sydney Brenner once famously wrote: "Progress in science depends on new techniques, new discoveries, and new ideas, probably in that order." However, you're right to point out that it's really the abstract methodology of delegating experimentation to a machine which truly distinguishes my technical proposal from related work. I have to admit that I haven't worked out in detail what the math will look like, though <http://arxiv.org/abs/1103.5708&#62 http://arxiv.org/abs/1103.5708> is a pretty good start. The tricky bit is defining the probability space (that is, the family of models under consideration). As a probability space, it must have a measurable structure. But for efficient and effective inference, it should also have additional structure, like a vector space. Yet any particular choice of vector space representation will trade off dimensionality against non-convexity, so it's desirable to have multiple representations of the same space. I've been taking a "cross that bridge when I come to it" approach, as I'm occasionally reminded by academics that if all I manage to do is collect a bunch of time series data about hundreds of neurons simultaneously, that would still probably be scientifically interesting and novel, even with ordinary, human-driven analyses.

by apl 13y ago

DaniFong 13y ago

I think it's fair to say that the hope is to combine the state of the art technology to discover more scientifically than has been before known. Additionally, this pushes technology. The hope is to push the technology far enough a system such that not only do the components work, but you can build actual useful engineering systems with them.

Hitchhiker 13y ago

David's start is at 14 and not post 20 as most grad students. At minimum, he will end up making something like Mathematica like Wolfram and at max, he will change the world. The Big and Little Oh of this story are both extreme events.

lmm 13y ago
That's an awfully high "minimum". I knew several "geniuses" at that age (might even qualify myself, though admittedly I was only three years ahead of par at that age); most of them have gone on to fairly normal (though by no means unsuccessful) careers, and a couple burned out quite spectacularly. I don't think putting that weight of expectation on is helpful.
- Hitchhiker 13y ago
  In deed, you remind me of Solon's warning. Perhaps the better way to put it is he's attacking one heck of a problem - and with the right detachment to both industry and academia - so the minimum is based more on the quality of problems than the individual ( as unique as that is in this particular case ). But yes, being an entrepreneur is an added layer, as you've to learn to arrange people to attain a larger goal than just research. And he shows signs of that even on the jobs page while avoiding the broken method of interviewing that is oft practiced, he's almost defining a boundary for relatively high signal from the applicants. His fluid approach is reminiscent of the caper that the Google guys pulled at Stanford.. and as it turns out, I think he also got funded by Larry.

davidad_ 13y ago

I like to describe my techniques as "not novel, just new." I'm absolutely building on the work of many others in academia. I've spent time with Engert at Harvard, Rex Kerr at Janelia, and some of Deisseroth's students, among others (including Boyden, Ramanathan, Manuel Zimmer in Vienna, Aravi Samuel, George Church, and more). I've only briefly met Bargmann and I loved the advice she gave me: "Hmm. That's probably not going to work, but...it's really worth trying." Unfortunately, most academic scientists are not in a position to risk their careers on such a bold and encompassing proposal. However, I benefit from this in some ways, because academics who are interested in this problem often work on a small piece of it instead of going for the whole thing, and are then incentivized to share that piece with me to integrate with all the other pieces, so they can see their contribution realize its full potential. I wouldn't underestimate the technical demands of the project - it certainly would have been unthinkable 10 years ago. Sydney Brenner once famously wrote: "Progress in science depends on new techniques, new discoveries, and new ideas, probably in that order." However, you're right to point out that it's really the abstract methodology of delegating experimentation to a machine which truly distinguishes my technical proposal from related work. I have to admit that I haven't worked out in detail what the math will look like, though <http://arxiv.org/abs/1103.5708&#62 http://arxiv.org/abs/1103.5708> is a pretty good start. The tricky bit is defining the probability space (that is, the family of models under consideration). As a probability space, it must have a measurable structure. But for efficient and effective inference, it should also have additional structure, like a vector space. Yet any particular choice of vector space representation will trade off dimensionality against non-convexity, so it's desirable to have multiple representations of the same space. I've been taking a "cross that bridge when I come to it" approach, as I'm occasionally reminded by academics that if all I manage to do is collect a bunch of time series data about hundreds of neurons simultaneously, that would still probably be scientifically interesting and novel, even with ordinary, human-driven analyses.