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Statistical models for neural data: from GLMs to latent variables (Part 1)

Difficulty level

Part 1 of 2 of a tutorial on statistical models for neural data

Topics covered in this lesson
  1. Introduction to the neural coding problem: how are stimuli and actions encoded in neural activity?
  2. What aspects of neural activity carry information?
  3. Regression models, latent variable models: aim to capture hidden structure underlying neural activity
  4. Model fitability/tractability vs richness and flexibility, finding a "sweet spot" in between


Spike count models and maximum likelihood
  simple example #1 (21:46): linear Poisson neuron, maximum likelihood estimation, log-likelihood
  simple example #2 (42:22): linear Gaussian neuron
  example #3: (50:17): unknown neuron, basic Poisson GLM
Spike train models (GLMs with spike history)
  What is a GLM? (57:20)
  Design matrix (1.00.07)
  GLM summary (1.20.18)
Multiple spike train models (GLMs with coupling)
  GLMs with spike history and coupling (1.23.19)
  GLM dynamic behaviour examples (1.34.43)
Beyond GLM
Latent variable models