Computational Neuroscience 2026

This course will cover fundamental aspects of computational approaches to neuroscience problems, including: analytical modeling, numerical calculations, data processing, visualization, and functional applications.

Time: 9 to 12 AM on Wednesdays

Place: Room 839, Library, Information and Research Building

Office hour (online): 10 AM on Fridays by appointment (email cjj@uw.edu to reserve by Thursdays)

Online: https://meet.google.com/qzc-kebi-nvn

• Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Dayan & Abbott (2005, ebook)
  (downloadable version available from school)
• Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition by Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski (2014, online)
• Computational Neuroscience by Students of NS/PY 357 Bates College (2022, online)

• Basics on tools
  • Programming in python
  • Linear algebra
  • Differential equations
  • Information theory
• Neural representations
  • Spike trains and firing rates
  • Spike-trigger average and correlation
  • Tuning curve and receptive fields
  • Discrimination and inference
  • Mutual information and entropy
• Modeling neural circuits
  • Spiking neurons
  • Synaptic transmissions
  • Neuronal networks
  • Details and abstractions
• Functions
  • Adaptive dynamics and plasticity
  • Information filtering and prediction
  • Decision making
  • Learning in neural networks

Homework is assigned weekly
Online quiz will be posted along with each homework
Take home final in the last week

Schedule and materials Videos