New experimental methods like patch-clamps, optogenetics, and two-photon imaging reveal new behaviors and computational abilities. In particular, we see that dendrites can organize and filter incoming signals in ways that greatly increase the complexity of each neuron. These findings could advance computer science, but they remain silioed in biology. I believe this is because they are expressed in a language of biology and electrophysiology that is inaccessible to computer scientists.
The goal of my masters thesis research was to translate recent biological findings into the language of computation. We used patch clamps to inject time-varying currents into neurons and study their response. Then I developed a theoretical computational framework to account for these findings which was consistent with findings from other experimental methods. The result was a mixed linear/nonlinear framework which simplified our understanding of the data. I also proposed a computational structure that could explain the function of some neurons that process time-signals. I believe that more research in this area can strengthen the connection between biological neuroscience and theoretical computational neuroscience, to the benefit of both fields.