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Neuroinformatics class page

This part of the site contains materials for Kenneth Harris' graduate course on "Neuroinformatics" (Spring 2022). Lectures will be delivered  in person 11am-1pm Wednesdays in the SWC ground floor lecture room (at least that is our current plan!) and will also be recorded for those accessing remotely. Workshops will be at 1:30pm Wednesdays, location to be confirmed.

Powerpoints: 

10/1/24
Review of Statistics
Spike Trains

17/1/24
LFPs
Spectral analysis

24/1/24
Spectrograms and non-stationary signals
Multiple Timeseries

31/1/24
Circular Statistics, local likelihood
The bootstrap

7/2/24
Singular Value Decomposition
Dimensionality Reduction

14/2/24
Reading week

21/2/24
Multiple linear regression
Ridge Regression and Bayesian regression

28/2/24
Lasso, Support Vector Machines, GLMs
The kernel trick

6/3/24
Analysis of multiple spike trains
Nonsense correlations

 

14/3/24

Cluster analysis and spike sorting
Bayesian methods

20/3/24
Information theory
Estimating mutual information

 

WORKSHEETS:

Week 1: 

The t-test and randomization tests
Making a raster plot

Week 2: 

Power spectra

Week 3:

Spectrograms and coherence

Week 4:

Circular statistics

Week 5:

Singular value decomposition

Week 6 (assessed):  

Multiple linear regression
Open the Colab page, and press File->Save a copy in Drive.  Rename it to include your candidate number. Fill in the cells that ask for text responses and for code, and run your code.  Then send a link to Martina so it can be assessed.

Week 7:

Poisson regression

Week 8 (assessed):  

Information theory
Open the Colab page, and press File->Save a copy in Drive.  Rename it to include your candidate number. Fill in the cells that ask for text responses and for code, and run your code.  Then send a link to Martina so it can be assessed.

MATLAB code (for the figures in the slides):

Please note the extensions of these files have been changed from .m to .txt to comply with our web service. Once you download them, you need to change them back to .m to run them in Matlab.

Week 1

Week 2

Week3

Week 4

Week 5

Week 6

Week 8

Week 10

Week 13:

Week 18: