Stanford University
Introduction to Computational Neuroscience
Spring 2026

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Lectures

  • Introduction and perceptrons (4/20/2026)
    Course goals and logistics. Introduction to perceptrons, the basic model of synaptic learning.
    [slides] [notes]

    Pre-lesson reading:

    • Vectors (10 minute video)
    • Dot product (the relevant part is the first 2 minutes 10 seconds, but feel free to watch the whole thing if you like)

    Optional Material:

    • Hertz, Krogh, Palmer Introduction to the theory of neural computation, chapters 5 and 6
  • Neural Encoding (4/22/2026)
    Review of theory for describing neural responses.
    [slides]

    Suggested reading:

    • Schwartz et. al. 2006 (a review of spike-triggered analysis)
    • Fairhall 2006 (applies spike-triggered covariance analysis to retinal data)
    • Mease et. al. 2013 (investigates adaptation in single neuron models)

    Advanced reading:

    • Aljadeff, Landsdell, Fairhall, Kleinfeld 2016. Analysis of Neuronal Spike Trains, Deconstructed
    • Yamins and DiCarlo 2016

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