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. depending on the demand.
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 | 18-Sep-2023 | 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] Solution: [link to Google Colab] | Chapter 1 of lecture notes |
| 2 | 25-Sep-2023 | No Lectures | |||
| 3 | 02-Oct-2023 | Models of Neurons | Lecture 3 (Passive membrane dynamics) [slides: pdf] | Lab 1 (Neuron models) [link to Google Colab] Solution: [link to Google Colab] | Required: Ch. 1 and Ch. 2.1-2.2 of lecture notes Optional: Ch. 2.3 of lecture notes. Dayan and Abbott Chapter 5 |
| 4 | 9-Oct-2023 | Models of synapses and neural firing statistics | Lecture 5 (The integrate and fire neuron) [slides: pdf, ppt] Lecture 6 (Spike statistics) [slides: pdf] | Required: Ch. 3.1, Ch. 5 and Ch. 6 of lecture notes Optional: Ch. 3.2-3.5 and Ch. 4 of lecture notes Dayan and Abbott Ch. 1 | |
| 5 | 16-Oct-2023 | The early visual system, information theory | Lecture 7 (The early visual system) [slides: pdf] Lecture 8 (Neural coding 1: Information theory) [slides: pdf] | Lab 2 (Firing statistics) [link to Google Colab] Solution: [link to Google Colab] | Required: Ch. 7 of lecture notes Optional Ch. 8 of lecture notes Dayan and Abbott Ch. 2 and 4 |
| 6 | 23-Oct-2023 | 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] | Required: Ch. 9 of lecture notes Optional: Dayan and Abbott Ch. 3 | |
| 7 | 30-Oct-2023 | Network models | Lecture 10 continued
Lecture 11 (Networks 1) [slides: pdf]
| Lab 3 (Neural coding) [link to Google Colab] Solution: [link to Google Colab] | Required: Ch.10 of lecture notes Optional: Dayan and Abbott Ch. 7 |
| 8 | 06-Nov-2023 | Network models | Lecture 12 (Networks 2) [slides: pdf] | ||
| 9 | 13-Nov-2023 | Plasticity and learning | Lecture 13 (Plasticity) [slides: pdf] Lecture 14 (Functional models of plasticity and learning) [slides: pdf] | Lab 4 (The ring network) [link to Google Colab]
Solution: [link to Google Colab] | Required: Ch. 12 and Ch.13 of lecture notes Optional: Dayan and Abbott Ch.8 |
| 10 | 20-Nov-2023 | Memory | Lecture 15 (Memory) [slides: pdf]
| ||
| 11 | 27-Nov-2023 | Neural data analysis | Lab 5 (Neural data analysis) [link to Google Colab] Solution: [link to Google Colab]
| ||
| 12 | 04-Dec-2023 | Revision | Revision session | ||
| 13 | |||||
| 14 |