Computational Foundations of Cognitive Neuroscience

This textbook provides an introduction to computational cognitive neuroscience. It attempts to unify many aspects of neural computation through the lens of decision theory. This approach allows us to address not only "how" questions (using an elementary set of neural primitives) but also "why" questions (what are the design principles?).

This is a draft and subject to change!

  • Chapter 1: Reverse engineering the brain
  • Chapter 2: Neural primitives of thought
  • Chapter 3: Principles of perceptual representation
  • Chapter 4: The Bayesian brain
  • Chapter 5: Approximate inference
  • Chapter 6: Attention
  • Chapter 7: Perceptual decisions
  • Chapter 8: Object categorization
  • Chapter 9: Plasticity as optimization
  • Chapter 10: Learning to predict
  • Chapter 11: Learning to act
  • Chapter 12: Predictive maps
  • Chapter 13: Simulation and planning with mental models
  • Chapter 14: Memory systems
  • Chapter 15: Generalization, geometry, and causality

    © 2025 Samuel J. Gershman (CC-BY license)

    Acknowledgments: I'm grateful to Arthur Prat-Carrabin and Momchil Tomov for detailed feedback.


    • Related resources

  • Lecture slides
  • Mini-projects