Table of Contents

Neuroscience, Cognitive Science, etc.

Computational/Theoretical Neuroscience

• Books:
  • Theoretical neuroscience (Larry Abbott, Peter Dayan)
  • Vision: A computational investigation into the human representation and processing of visual information (David Marr)
  • Reinforcement Learning and the Basal Ganglia (Yael Niv)
• Online Courses
  • The Fundamentals of Neuroscience (Cox)
  • Brains, Minds and Machines Summer Course
  • Visual object recognition: computational and biological mechanisms
• Tutorials
  • A tutorial on the free-energy framework for modelling perception and learning (Bogacz) - relevant if you’re interested in Karl Friston’s Active Inference
  • Biological Neural Networks – Part I, Spiking Neurons by Jack Terwilliger, Research Associate @ MIT

Computational Cognitive Science

• Online Courses
  • Probability and Causality in Human Cognition (Tenenbaum)
  • Computation and Cognition: the Probabilistic Approach (Goodman)
  • Probabilistic Models of Cognition (Goodman)
  • Models Agents with Probabilistic Programs
  • Advancing AI through cognitive science (Brenden Lake)
• Tutorials
  1. A tutorial on Gaussian processes (Schulz) - oriented towards psychologists
  2. A tutorial on Bayesian nonparametric models  (Gershman) - includes discussion on models for cognitions and oriented towards psychologists
  3. Tutorial on maximum likelihood estimation (Myung) - focus on mathematical psychology