Statistical models for neural data: from GLMs to latent variables (Part 2)

Statistical models for neural data: from GLMs to latent variables (Part 2)

By
COSYNE
Difficulty level
Beginner
Speaker
Jonathan Pillow
Type
Video
Duration
1:50:31
Topic
Computational neuroscience
Neural coding

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

Topics covered in this lesson
  1. Multiple spike train models (GLMs with coupling)
    • Multi-neuron GLM (9.17), coupling filters
    • Plot examples from the GLM tutorial (15.06)
    • Fitting, maximum likelihood (18.22)
    • Decoding (28.12), Bayesian decoding
  2. Regularization (33.11)
  3. Beyond GLM (51.45)
    • Polynomial models, Volterra/Wiener models
    • Multi-filter NLP (57.37)
    • Extending GLM to conductance-based model (1.03.11)
    • Deep Neural Networks (1.10.45)
  4. Latent variable models (1.19.55), fitting latent variable models (1.27.00)
Prerequisites

Watch the first part of the lesson

Back to the course
Statistical models
GLM, regression models, and latent variables