Neural Data Modelling (2017)

Dates 25 January - 29 March 2017
Lectures Wednesdays 11:00-13:00
Lecturers Kenneth Harris
Maneesh Sahani
TAs Alexander Antrobus, Kevin Li, Sanjeevan Ahilan
Location Ground Floor Seminar Room, 25 Howland Street, London W1T 4JG
About the course

This course is designed for first-year PhD students in the Gatsby Unit and SWC, and for other quantitative graduate neuroscientists at UCL.
General attendance

The course is open on an informal basis to all UCL Students, postdocs and faculty. It is not possible to register for the course, although it may be possible to obtain graduate training credit.
Prerequisites

Some background in statistics, calculus, linear algebra and programming in MATLAB will be useful. Gatsby students will have taken courses in machine learning and be taking theoretical neuroscience concurrently; however we will endeavour to avoid material that depends heavily on those courses.

Lecture schedule and slides Lecture slides will be updated as the course progresses.

 

Date Lecturer Content
25/1: KH Review of statistics | Spike trains
1/2: MS Point processes
8/2: MS Receptive field models
15/2: KH Circular statistics, maximum likelihood, local likelihood | The bootstrap
22/2: KH Information theory | Estimating mutual information
1/3: KH LFPs | Spectral analysis
8/3: KH Spectrograms and nonstationary signals | Multiple Timeseries
15/3: KH TBD
22/3: MS Population methods I
29/3: MS Population methods II
Additional reading
  • AF Meyer, RS Williamson, JF Linden, and M Sahani. Models of neuronal stimulus-response functions: Elaboration, estimation, and evaluation. Front Syst Neurosci, 10:109, 2017. online
To attend Please see the note above