This is a 3rd year Computer Science and MSc unit. We will try to understand how the brain works from a computational point of view.
See the coursework folder.
- Laurence Aitchison (unit director)
All live sessions will be hosted on Microsoft Teams. You should already have been added to the group "COMS30017: Computational Neuroscience (Teaching Unit) 2021/22 (TB-1, A)" You can download the MS Teams app free here, using your UoB email address.
- Unit forum subreddit: ask questions here. It includes questions from the previous year of the course, so some of the "admin" things may be different. But the course content will be similar!
- Blackboard page: official University of Bristol landing page.
- Weeks 1-7: Video lectures for the week released each Monday. Please watch when you can during the week. Each week will cover a different topic. Most weeks will also come with question sheets.
- Weeks 2-8: Monday 12-1pm, live Q+A session with lecturer(s). Chance to ask questions to the lecturer about the previous week's material. Hosted on Microsoft Teams.
- Weeks 2-7: Wednesday 11-12am, live bubble discussion seminars. Each bubble will be moderated by a TA or lecturer with discussion topics related to the lecture material. Hosted on Microsoft Teams.
- Weeks 8-10: Coursework time, for those taking the 20-credit coursework version of the unit (COMS30015).
- Week 11: Consolidation week. Catch up week for any further lecture material.
- Week 12: Revision week. Preparation week for those students taking the 10-credit written exam version of unit (COMS30016 or COMSM0039).
The 2021 January exams with solutions can be found here:
Previous years' exams can be found here (note they were a different format and had a different syllabus, so the above exam is more relevant): https://github.com/coms30127/exam_papers
| video | slides |
|---|---|
| Stream link |
[Lectures are from Cian, but please don't contact Cian as he has now left the university! Please ask at the lecturer-led Q&A, ask your TA, on the reddit or ask me ([email protected]).]
| Lecture | video | slides |
|---|---|---|
| 1. Why brains? | 19:47 Stream link | |
| 2. Brain anatomy. | 21:37 Stream link | |
| 3. Neuron anatomy: axons, dendrites, synapses. | 12:02 Stream link | |
| 4. Neuronal communication. | 14:37 Stream link | |
| 5. Measuring, recording and stimulating the brain. | 19:54 Stream link | |
| Problem sheet | --- | |
| Answers to problem sheet | --- | |
| QA | Stream link | --- |
Lectures:
You may notice that these were originally compiled for a PPN course. We need to go a bit further. (Hopefully, you'll know at least one of the topics below, depending on your course):
- Computing notes
- Mathematics notes
- Numerical notes (We're aware the link may be broken if you're looking at this on Monday.
Link to last year's lectures/content
Hodgkin Huxley, modelling neurons, analysing spiking data.
[Lectures are from Cian, but please don't contact Cian as he has now left the university! Please ask at the lecturer-led Q&A, ask your TA, on the reddit or ask me ([email protected]).]
| Lecture | video | slides |
|---|---|---|
| 1. Modelling neurons | 26:29 [Stream link] | [pdf] |
| 2. Ion channels & dendritic integration. | 23:24 [Stream link] | [pdf] |
| 3. Hodgkin-Huxley model. | 33:51 [Stream link] | [pdf] |
| 4. Analysing spike data. | 17:05 [Stream link] | [pdf] |
| 5. Neural decoding. | 23:02 [Stream link] | [pdf] |
| Problem Sheet | --- | [pdf] |
| Answers to problem sheet |
|QA| [Stream link]
Previous notes from CH:
| Lecture | video | slides |
|---|---|---|
| 1. What is a synapse? | 26:29 [Stream link] | [pdf] |
| 2. Computational modelling of a synapse | 25:31 [Stream link] | [pdf] |
| 3. Synaptic plasticity | 11:02 [Stream link] | [pdf] |
| 4. Short-term plasticity | 14:38 [Stream link] | [pdf] |
| 5. Long-term plasticity | 33:07 [Stream link] | [pdf] |
| Video of simulation of synaptic activity | 3:00 [Stream link] | |
| Problem Sheet | --- | [pdf] |
| Answers to problem sheet |
| Lecture | video | slides |
|---|---|---|
| 1. The Hippocampus and long-term memory | 16:32 [Stream link] | [pdf] |
| 2. The Hippocampus and spatial navigation | 10:37 [Stream link] | [pdf] |
| 3. Pattern Separation | 20:43 [Stream link] | [pdf] |
| 4. Hopfield networks (discrete attractors) | 12:35 [Stream link] | [pdf] |
| 5. Continuous attractors and navigation | 12:28 [Stream link] | [pdf] |
| Problem sheet | --- | [pdf] |
| Answers | --- | [pdf] |
| Q&A | [Stream link] | --- |
| Lecture | video | slides |
|---|---|---|
| 1. Firing rates and receptive fields | 16:51 [Stream link] | [pdf] |
| 2. The visual pathway | 15:17 [Stream link] | [pdf] |
| 3. Retina | 7:08 [Stream link] | [pdf] |
| 4. V1 and the cortical microcircuit | 15:46 [Stream link] | [pdf] |
| 5. Topographic maps and sparse coding | 20:43 [Stream link] | [pdf] |
| Problem sheet | --- | [pdf] |
| Answers | --- | [pdf] |
| Q&A | [Stream link] | -- |
| Lecture | video | slides |
|---|---|---|
| 1. Supervised learning using the delta rule | 12:00 [Stream link] | [pdf] |
| 2. Cerebellar anatomy, function and microstructure | 16:27 [Stream link] | [pdf] |
| 3. Classical conditioning | 19:32 [Stream link] | [pdf] |
| 4. Temporal difference learning and dopamine | 12:17 [Stream link] | [pdf] |
| Problem sheet | --- | [pdf] |
| Answers | --- | [pdf] |