[Statlist] Research Seminar in Statistics | *FRIDAY 28 APRIL 2023* | GSEM, University of Geneva

[gsem-support-instituts]

Dear all,

We are pleased to invite you to our next Research Seminar, organized by Professor Sebastian Engelke on behalf of the Research Center for Statistics. (https://www.unige.ch/gsem/en/research/institutes/rcs/team/)

FRIDAY 28 APRIL 2023 at 11:15 am, Uni Mail M 3393 & Online
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192

Bayesian Inference of Grid Cell Firing Patterns Using Poisson Point Process Models with Latent Oscillatory Gaussian Random Fields
Ioannis PAPASTATHOPOULOS, University of Edinburgh, UK
https://www.research.ed.ac.uk/en/persons/ioannis-papastathopoulos

ABSTRACT:
Questions about information encoded by the brain demand statistical frameworks for inferring relationships between neural firing and features of the world. The landmark discovery of grid cells demonstrates that neurons can represent spatial information through regularly repeating firing fields and has motivated explanatory frameworks for neural computation of location and non-spatial variables. Grid codes may be multiplexed with information about variables such as head-direction. However, the influence of covariates may be masked in current statistical models of grid cell activity, which by employing approaches such as discretizing, aggregating, and smoothing, are computationally inefficient and do not account for the continuous nature of the physical world. These limitations motivated us to develop likelihood-based procedures for modelling and estimating the firing activity of grid cells conditionally on biologically relevant covariates. We introduce new statistical models, based on Cox processes, that are amenable to modeling the relationship between neural activity and covariates, including time, space, and other arbitrary parameters such as head direction. Our approach rests on modelling firing activity using Poisson point processes with latent Gaussian effects. The latent prior Gaussian effects accommodate for overdispersion and are chosen so that they mimic closely the behavior 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 previous analyses. Our approaches offer a novel and principled framework for analysis of neural representations of space.

> View the Research Seminar agenda: https://www.unige.ch/gsem/en/research/seminars/rcs/

Regards,


Sandra Vuadens
Assistant to research and institutes
gsem-support-instituts using unige.ch