Skip to content
This repository has been archived by the owner on Mar 2, 2022. It is now read-only.

Latest commit

 

History

History
37 lines (28 loc) · 2.24 KB

README.md

File metadata and controls

37 lines (28 loc) · 2.24 KB

Module 1: Mechanistic models of decision making

Goals:

You will learn how to implement and tune a model of perceptual decision making. The ‘high-level’ objective will be to understand how to set up neural simulations, but also to get an appreciation of their limitations, as well as their dependence on the parameters/assumptions.

Week 1

In week 1, you will get to know the model with which we will working in this project.

Tasks:

  • Read through background materials 1 - 2.
  • Work on exercises 1 and 2.
    • create your fork of the repository by accepting the github classroom assignment
    • work through the notebook, add code and plots in code cells, add text answer in markdown cells.
    • commit regularly, your last commit before the deadline counts as submission.

Week 2

This week you will analyze the model. Link to github classroom assignment.

Tasks:

  • Recap week 1, read through the solutions and make sure you understand them. Disuss with your colleages or open an issue in case you have questions or spot a mistake in the solutions.
  • Work on exercises 3 and 4. Discuss with your colleages or open an issue if you have questions or if you are stuck.

Week 3

This week you will learn about Bayesian parameter inference via simulation-based inference, using the example of the drift-diffusion model. Link to github classroom assignment.

Tasks:

  • Read through the instructions in the notebook carefully and install the required packages in your conda environment.
  • Work on exercises 1-3 (optional 4) and discuss with your colleages or open an issue if you have questions.

Reading:

  1. Chapter 16 of Neuronal Dynamics et al.: https://neuronaldynamics.epfl.ch/online/Ch16.html
  2. Wong, K.-F. & Wang, X.-J. A Recurrent Network Mechanism of Time Integration in Perceptual Decisions. J. Neurosci. 26, 1314–1328 (2006)
  3. Goncalves, Lueckmann, Deistler et al. 2019. Training deep neural density estimators to identify mechanistic models of neural dynamics

Logistics

We summarised all the logistics you need to take care of in logistics.md.