BAMB2025

Tutorials for each day of BAMB! 2025. Each folder contains two tutorial versions:

• basic tutorial file, not or only partially filled (eg. modeling_101.ipynb)
• tutorial with solutions (modeling_101_solutions.ipynb)

Day 1 - General Introduction to Behavioral Modelling

• Introductory lecture: What is a model ?
• Lectures, divided in 3 parts: part A - Model definition and estimation; part B - Parameter fitting and recovery; part C - Model comparison
• Tutorial, also divided in 3 parts: part A ; part B ; part C

Day 2: Reinforcement Learning

• Tutorials:
  • Introduction and structure
  • Part 1: RL Basics:
    • Overview
    • 
  • Part 2: Fitting RL models to behavior:
    • Slides
    • Colab notebooks:
      • Instructions
      • Solutions

Day 3: Bayesian decision-making and model-fitting

• Tutorials

  • Part 1: Introduction and Bayesian Brain

    • Instructions:
    • Solutions:
    • Slides

  • Part 2: Complex behavior and connections

    • Instructions:
    • Solutions:
    • Slides

Day 4: Latent variable models

• Tutorials
  • Part 1: Mixture models and the general expectation maximization algorithm
    • instructions
    • solutions
  • Part 2: Hidden Markov models
    • instructions
    • solutions
  • slides

Day 6: Drift-diffusion model (DDM)

• Tutorial
  • instructions
  • solutions

Day 7: Neural Networks

• Tutorial instructions: Feedforward models
• Tutorial solutions: Feedforward models
• Tutorial instructions: RNNs
• Tutorial solutions: RNNs