======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 [[mailto:cjj@uw.edu|cjj@uw.edu]] to reserve by Thursdays) **Online**: https://meet.google.com/qzc-kebi-nvn =====Textbooks and References===== * [[https://mitpress.mit.edu/9780262311427/theoretical-neuroscience/|Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems]] by Dayan & Abbott (2005, ebook) \\ (downloadable version available from school) * [[http://neuronaldynamics.epfl.ch/|Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition]] by Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski (2014, online) * [[https://mrgreene09.github.io/computational-neuroscience-textbook/|Computational Neuroscience]] by Students of NS/PY 357 Bates College (2022, online) =====Syllabus===== * 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 [[2026cns:homework|Homework]] is assigned weekly\\ Online quiz will be posted along with each homework\\ Take home final in the last week [[2026cns:content|Schedule and materials]] [[https://www.youtube.com/playlist?list=PLKKQ6OvQMh30|Videos]]