The linear approximation of SfN

I recently came back from the Society for Neuroscience (SfN) mega-conference that draws over 30 kNeuroscientists each year. That’s right, 3×104. Compare this, of course, to the annual conference for the American Association for the Advancement of Science, which draws about 10.000, or 1/3 of the number. Yet neuroscience is a subset of science, so, something funny must be happening. Is there a conference larger than SfN? I’d do well to avoid it, I think.

I looked at SfN this year from the limited but functional perspective of a computational neuroscientist. For the experimental posters and talks I attended, I tried to think of ways in which they could be modeled. On my mind notably is modeling of calcium wave propagation dynamics, which is an active area of imaging and may benefit from computational approaches. Surely there is work being done on this currently.

For the computational presentations, I tried to learn about various techniques that I have not yet been exposed to. There was a lot of basic Hodgkin-Huxley type modeling, along with a bunch of compartmental modeling. But what caught my eye was the use of stochastic processes in creating a framework for studying spiking neurons. At its best it may provide a mathematically rigorous description for certain experimentally observable data.

The most fruitful conversation I had was with a fellow grad student who had access to some very unique electrocorticogram (ECoG) data in auditory cortex of humans and had done some preliminary frequency analyses during natural, meaningful sounds (such as a spoken sentence). There is a songbird analog to this idea using natural, complex sounds that constitutes a very active area of research for auditory physiologists and others.

One interesting (to me) observation about the conference was the inelastic collision between vendors, industry, funding sources, institutions, and scientists. A lot of toys (swag) were doled out. I asked an NIH person about computational neuroscience support, and while they financially support efforts of collaborative science that include computation (a very reasonable approach, especially considering the NIH mission), they do not have but a handful of computational neuroscientists staffed at NIH.

One guy whose job was to connect industries with researchers said, when asked about the perceived value of computational neuroscientists in industry, “None yet. But keep asking.” I don’t know how indicative of greater industry he represents, but this means to me that the value of computational approaches may not have quite received full understanding in that community.

There seems to be a lot of convincing to be done.

The conference was not overwhelming as cautioned because I think I put enough (perhaps too many?) constraints on my activities. If I tried to do too much, I felt that I would have learned nothing. But focusing on learning a few things I know about and a few things I’m interested in learning pertaining to my own work, and the conference was very useful.

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