CNS: Schedule
Note: The below schedule is subject to change. We have intentionally left some weeks empty in order to allow for the flexibility to run revision sessions, go more slowly through lecture material, etc.
Much of the course follows the content of a previous course, Neural Computation. We provide the lecture notes from Neural Computation and explicitly state which sections are examinable and which are optional (i.e., non-examinable) in the schedule below. In general, anything which is covered in the live lectures (i.e., in the slides) should be considered examinable, and anything which is not covered in the lectures should be considered non-examinable.
The lecture notes can be found here:
Other useful reading materials are:
Dayan and Abbott (Theoretical Neuroscience)
Gerstner (Neuronal Dynamics)
Jorge Menendez's Theoretical Neuroscience notes (a nice set of notes covering many of the topics in this course)
Ermentrout and Terman (Mathematical Neuroscience) Ch. 1 - good for single neuron biophysics
The above textbooks can be accessed via the Library Resources section of the course Learn page.
| Week | Commencing | Key Topics | Lectures | Labs | Reading |
|---|---|---|---|---|---|
| 1 | 21-Sep-2026 | Introduction to computational neuroscience | Lecture 1 (Introduction to course) [slides: pdf, ppt]
Lecture 2 (Overview of computational neuroscience) [slides: pdf] | Lab 0 (Introduction to differential equations) [link to Google Colab]
| Chapter 1 of lecture notes |
| 2 | 28-Sep-2026 | Models of Neurons | Lecture 3 (Passive membrane dynamics) [slides: pdf]
| Required: Ch. 2.1-2.2 and 3.1 of lecture notes Optional: Ch. 2.3 and 3.2-3.5 and of lecture notes. Dayan and Abbott Chapter 5 | |
| 3 | 5-Oct-2026 | Models of synapses and neural firing statistics |
Lecture 5 (The integrate and fire neuron) [slides: pdf]
Lecture 6 (Spike statistics) [slides: pdf] | Lab 1 (Neuron models) [link to Google Colab]
| Required: Ch. 5 and 6 of lecture notes Optional: Ch. 4 of lecture notes Dayan and Abbott Ch. 1 |
| 4 | 12-Oct-2026 | The early visual system |
Lecture 7 (The early visual system) [slides: pdf]
Lecture 8 (Neural coding 1: Information theory) [slides: pdf] | Required: Ch. 7 of lecture notes Optional Ch. 8 of lecture notes Dayan and Abbott Ch. 2 and 4
| |
| 5 | 19-Oct-2026 | Neural coding | Lecture 9 (Neural coding 2: Generative models, natural image statistics) [slides: pdf]
Lecture 10 (Neural coding 3: Population coding, Fisher information) [slides: pdf] | Lab 2 (Firing statistics) [link to Google Colab]
| Required: Ch. 9 of lecture notes Optional: Dayan and Abbott Ch. 3 |
| 6 | 26-Oct-2026 | Network models |
Lecture 11 (Networks 1) [slides: pdf] Lecture 12 (Networks 2) [slides: pdf] | Required: Ch.10 of lecture notes Optional: Dayan and Abbott Ch.7 | |
| 7 | 02-Nov-2026 | Plasticity and learning |
Lecture 13 (Plasticity) [slides: pdf] Lecture 14 (Functional models of plasticity and learning) [slides: pdf] | Lab 3 (Neural coding) [link to Google Colab] Assignment [Released 06/11, due 12pm 20/11. The attachment is last year's assignment.] | Required: Ch. 12 and Ch.13 of lecture notes Optional: Dayan and Abbott Ch. 8 |
| 8 | 09-Nov-2026 | Memory |
Lecture 15 (Memory) [slides: pdf] | No required reading
| |
| 9 | 16-Nov-2026 | Lab 4 (The ring network) [link to Google Colab]
| |||
| 10 | 23-Nov-2026 |
| |||
| 11 | 30-Nov-2026 |
| |||
| 12 | 07-Dec-2025 | Revision | Revision session | ||
| 13 | |||||
| 14 |