Date
November 29 at 13:30 - 15:00, 2021 (JST)
Speaker
Dr. Enrico Rinaldi (Research Fellow, Physics Department, University of Michigan, USA) Edit
Venue
via Zoom
Language
English

In many scientific fields, ranging from astrophysics to particle physics and neuroscience, simulators for dynamical systems generate a massive amount of data. One of the crucial tasks scientists are spending their precious time on is comparing observational data to the aforementioned simulations in order to infer physically relevant parameters and their uncertainties, based on the model embedded in the simulator. This poses a problem because the likelihood function for realistic simulations of complex physical systems is intractable. Simulation-based inference techniques attack this problem using machine learning tools and probabilistic programming. I will start with an overview of the problem and explain the general application of simulation-based inference methods. Then I will describe an application of the methods to a model of neurons in the visual cortex of mice."

References

  1. Kyle Cranmer, Johann Brehmer, Gilles Louppe, The frontier of simulation-based inference, PNAS 117, 48, 30055-30062 (2020), doi: 10.1073/pnas.1912789117
  2. Agostina Palmigiano, Francesco Fumarola, Daniel P. Mossing, Nataliya Kraynyukova, Hillel Adesnik, Kenneth D. Miller, Structure and variability of optogenetic responses identify the operating regime of cortex (2021), doi: 10.1101/2020.11.11.378729