- Exaggerated Claims Undermine Science by Ignoring the Scientific Method (July 2020)
- Full lecture video (25 minutes)
- Summary (4 minutes)
- Uncertainty, Information, and Narrative: A Statistical Perspective on Scientific Storytelling (March 2019)
- Full lecture (47 minutes)
- Sam Behseta "podcast" interview of Rob Kass about lecture 15 minute audio summary

*Statistical Models of the Brain* is a course in computational neuroscience I began teaching in 2011.
Brent Doiron co-taught it with me on 4 occasions, 2016-2019. It has evolved a lot, mainly in terms of
refined focus and pedagogy.

A quick video summary (less than 5 minutes) of the Spring 2021 version may be found here: Wrap up

In 2021 I decided to create short video lectures, 15-30 minutes, that students would view prior to class. Topics with links are listed below.

For additional details see the web page on Statistical Models of the Brain.

- 1. What is computational neuroscience?
- 2.
*(Background)*Random variables; What is a statistical model? Fitting statistical models to data. - 3.
*(Background)*Log transformations; random vectors; important probability distributions and the way they model variation in data. - 4.
*(Background)*The Law of Large Numbers and the Central Limit Theorem; statistical estimation; least-squares linear regression and the linear algebra concept of a basis.

- 5. Random walk models of integrate-and-fire neurons; effects of noise: balanced excitation and inhibition
- 6. Population vectors
- 7. Information theory in human discrimination
- 8.
*(Background)*Differential equations - 9. Electrical circuit model of a neuron; passive synaptic dynamics and phenomenological models of spiking; integrate-and-fire dynamics
- 10. The Hodgkin-Huxley model of action potential generation (Nour Riman)
- 11. Network dynamics (No Video)
- 12.
*(Background)*Bayes' Theorem; optimality of Bayesian classifiers; mean squared error; Bayes and maximum likelihood. - 13. Cognition and optimality; ACT-R
- 14.
*(Background)*Statistical tests, ROC curves, signal detection theory - 15. Optimal observers in perception and action
- 16.
*(Background)*Regression and generalized regression. - 17. Firing rate and neural coding; spike trains as point processes
- 18. Information theory in neural coding (2nd video)
- 19. Neural implementation of Bayesian inference
- 20. Population-wide variability: spike count correlations; dimensionality reduction
- 21. Neural basis of decision making (systems level)
- 22. Network models of working memory and decision-making (Chencheng Huang)
- 23. Reinforcement learning
- 24. Graphs and networks
- 25. What is science? (Additional video: wrap-up)