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    Neural Data Analysis: The Bayesics

    Difficulty level
    Beginner
    Speaker
    Type
    Duration
    1:12:38

    In this lesson, you will learn about Bayesian neuron models and parameter estimation.

    Topics covered in this lesson
    • Bayesian statistics
    • Posterior and prior probabilities, likelihood
    • Generalized linear model (GLM) - relating stimuli to neural responses
    • Poisson process, the mother of all spike train models
    • Time binning and maximum likelihood
    • Estimating posteriors
    • GLM dependency of neural spike rate on time, stimulus, spike history
    • Latent variables
    • Expectation maximization algorithm
    Prerequisites
    • Calculus (integration and differentiation)
    • Basic linear algebra (matrices, determinants)
    • Some basic transform theory, such as knowing what Fourier transforms do, what a convolution is