Induction week classes
| Date |
Subject |
Taught by |
Materials |
| 25/09/172 - 4pm |
Intro to neuroanatomy and sensory pathways. |
Kirsty McNaughtJesse Geerts |
Neuroanatomy slides
Sensory pathway slides
|
| 26/09/172 - 3.30pm |
Intro to vector spaces (prerequisites for kernels course) |
Michael ArbelWenkai Xu |
Kernels background notes
|
| 27/09/1711am - 12.30pm |
Basics of linear algebra |
Jorge Menendez Canelas |
|
| 27/09/172 - 4pm |
Using source control and intro to git |
Kirsty McNaughtSteve LenziCharly Rousseau |
Version control slides
|
| 28/09/1710.30am - 12.30pm |
Intro to applied Bayesian methods (prerequisites for machine learning course) |
Lea Duncker |
Stats background notes
|
| 28/09/172 - 4 pm |
Basic cell biology and experimental methods |
Matt PhillipsSteve Lenzi |
|
| 29/09/173 - 4.30pm |
Practical PhD skills and tips, Q&A with students |
Everyone |
PhD skills notes
|
Additional resources
Student notes
Below are some notes made by previous students. They may contain errors; you are strongly encouraged to report back to us and contribute changes. There is significant overlap in some areas; pick the perspective you prefer.
Theoretical Neuroscience
| Topic |
Link |
Comments |
| Learning rules |
Learning rules (Kirsty) |
Mainly plasticity/hebbian rules, summarised from Dayan&Abbott. |
| Biophysics notes |
Biophysics (Kirsty) |
Covers most of Peter Latham's biophysics, at a higher level than Jorge's more comprehensive notes |
| Information theory for TN |
Information Theory (TN) (Kirsty) |
High level definitions and relationships between Information Theory terms |
Machine Learning (including kernels)
Latex template
It is highly recommended that you use latex for your assignment in this course and others. You may find it helpful to start with this sample assignment template, which demonstrates some figures, maths and other useful things.
Assignment template
Recommended textbooks:
Systems and Theoretical Neuroscience
Machine Learning
Useful links for your life at Gatsby/SWC