Neural Data Analysis: The Bayesics
Neural Data Analysis: The Bayesics
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
External Links
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
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