December 16, 2022
ISBA - C115 (1st Floor)
Statistics Seminar on "Statistical point process models for spike-trains obtained from grid cells"
The growing complexity of experiments in biology requires appropriate data analysis methods for establishing how firmly certain biological functions can be identified from the data. One such biological function relates to how the human brain functions and is arguably one of the most challenging problems in science today. Recent advances in neuroscience have made the landmark discovery of certain types of neurons in the brain, termed grid cells, and have highlighted their role as the most plausible mechanism for how the brain performs a range of tasks including spatial navigation. We argue that statistical modelling of the firing activity of grid cells has not been dealt with in-depth and that commonly employed approaches may potentially mask the effect of key features such as the direction of navigation of the animal as well as residual temporal variation. To our knowledge, there is no formal statistical framework developed so far that can facilitate their inclusion of such features. As a result, we are led to develop likelihood-based procedures for modelling and estimating the firing activity of grid cells conditionally on biologically relevant covariates. Our approach rests on modelling the firing activity of cells using Poisson point process on trajectories of animals with latent Gaussian effects defined in the environment that the animal explores. The latent prior Gaussian effects accommodate for overdispersion and are carefully chosen so that they mimic closely the behaviour of the firing activity from grid cells whilst accounting for unexplained variation. Inference is performed in a fully Bayesian manner which allows us to quantify uncertainty and provide evidence that supports the hypothesis of the presence of effects that are typically missed out from most of the existing analyses.