In broader terms, I am interested in the dynamics of activity in biologically plausible models of neural networks, related to storing and integrating sensory (or in general any kind of) information in animals.
For my PhD. I am working on a biophysically plausible models of grid cells. These cells have an intricate property of their firing fields being confined to specific positions of an animal's environment. Moreover, the firing pattern of a single cell repeats, forming a two dimensional, regular triangular grid. In the project, I am building on a hypothesis that an animal can integrate self-motion cues during its movement, and by using mechanism called path integration, can update a neural representation of its relative position, reflected in the firing of a grid cell. I want to investigate properties of spiking attractor networks in this context, their stability conditions in reproducing the grid cell properties in simulations, and the precision of path integration algorithm.