Learning Interneuron DiversityBack to Research
Inhibitory interneurons show great diversity in their intrinsic properties, connectivity patterns and morphology. Modern experimental techniques have led to rapid progress in characterizing these properties, but the functional reasons for this diversity are not well understood. In this project we hypothesize that the diversity of interneurons arises as a solution to particular problems neural circuits have to solve. We investigate this hypothesis by optimizing a range of interneuron properites using advances in training spiking neural networks. Subsequent comparison of the optimal properties with experimental data can elucidate the extent to which aspects of interneuron diversity can be explained.