Apparaît dans la collection : 2022 - T1 - WS2 - Mathematical modeling and statistical analysis in neuroscience

Point process modeling and estimation methods have become pervasive in the analysis of spiking data from neural populations. Recent technological developments have led to a massive increase in the size and dimensionality of neural datasets and focused researchers on models that can capture the structure of activity from populations of simultaneously recorded neurons. This has spurred the development of new modeling and estimation methods based on the theory of marked point processes.


In this talk, I will present a case study focused on understanding how mental exploration drives learning, which highlights a number of statistical problems that can be addressed under a marked point process modeling framework. I will discuss adaptive models that can capture nonstationarities in the data, goodness-of-fit methods that allow for model assessment and refinement, and state-space estimation methods that allow us to decode signals directly from the observed neural activity.