Engineering, not computation?

I heard Noah Cowan of JHU give a great talk today. He talked about having an engineer's perspective, a systems perspective, applied to scientific questions in biology. There is so much to be learned about the neural control of animal behavior, and he and colleagues are figuring out how animals close the sensorimotor feedback loop. It turns out, for example, both theoretically and experimentally that cockroaches can't be using simple proportial control when they are running at 1.5m/sec along a wall, but a PD model does in fact predict pretty well the roach motion. Or there are these weakly electric knifefish that really like hiding inside tubes. If you move the tube around, it will look like the fish is attached to it with a spring as it follows along. And it turns out that a harmonic oscillator (spring-mass) mechanics model in fact correctly predicts the behavior at various frequencies of moving the tube around, whereas a kinematic model does not.

As I payed close attention to the controller-plant-feedback diagrams, the second-order differential equations and the low-pass, high-pass and band-pass filter discussions, I noticed that all of these useful tools have nothing to do with computation or a computational worldview. The engineer has a formidable toolbox for use in the science of neural control; the progress that can be made is remarkable. But what can a computer scientist bring to the table? What models, what theoretical tools?

It's pretty obvious to me that computation won't be helpful in figuring out the science of animal motion, in all its graceful and fast glory. So what kinds of biology questions might we address armed with our understanding of data structures, algorithms and complexity? Ideally, questions beyond proving this or that purportedly intelligent activity is NP-hard? My hunch (and I'm betting my research on it) is that our computational tools will be best employed in asking and answering questions at a level above that of the neural control of one organism. Instead, we can study the interactions, communications, and group-level behaviors of many organisms.

Bayesian models of human cognition notwithstanding.