This is a 3rd year Computer Science unit. We will try to understand how the brain works from a computational point of view.
- Laurence Aitchison (unit director)
- Conor Houghton
- Lectures (weeks 1-7)
- 9am Tuesdays (FRY BLDG G.10 LT)
- 3pm Thursdays (CHEM BLDG LT1)
- Problem classes (starting week 2, 5th Oct)
- 10am-11am Wednesdays (MVB 1.11)
- Weeks 1-7: Lectures
- Weeks 2-: Problem classes. These will be held in MVB 1.11 with TAs + sometimes the lecturer. Please bring any questions on problem sheets, past exams etc. to this session! The idea is to ask TAs questions individually, this isn't a "big" QA session where everyone listens to everyone else's questions!
- Weeks 8-10: Coursework time, for those in CS taking the 20-credit coursework version of the unit. (If you aren't in CS, you're not doing coursework!)
- Week 11-12: Consolidation/revision week for students taking the exam.
Please see the folder and the pdf. This is only relevant for those in the Coursework part of the unit (see "Exam vs coursework unit" if you're not sure what part of the course you're on).
If you're not a CS student, you're doing the exam version of the unit. If you are a CS student, look on your BB course list.
- If you see: COMS30063_2022_TB-1: Computational Neuroscience (with Coursework) 2022 then you're doing coursework.
- If you see COMS30016_2022_TB-1: Computational Neuroscience 2022 you're on the exam unit.
Please get in touch with your school office (for CS, coms-info@bristol.ac.uk) if you have any questions about this. Conor and I don't touch the enrollment process!
You additionally may have a COMS30017_2022_TB-1: Computational Neuroscience (Teaching Unit) 2022, which is common to everyone.
The course has two primary routes for QA:
- Synchronous QA in the problem classes in MVB 1.11 on Wednesdays at 10-11am.
- Asynchronous QA on Teams, (as we did during COVID). You should already have been added to the group "Welcome to COMS30017: Computational Neuroscience (Teaching Unit) 2022/23 (TB-1, A)". You can download the MS Teams app free here, or use their web app (https://teams.microsoft.com/; Chrome seems least buggy...).
It seems that we can get access to the lecture videos through BlackBoard. Unfortunately, it may take a couple of days for videos to appear here.
- Go to the teaching unit in BlackBoard (COMS30017_2022_TB-1: Computational Neuroscience (Teaching Unit) 2022)
- Go to Re/Play in the menu on the left
The exam
- is closed book
- is in person
- is handwritten (unless you have AEAs, please get in touch with the school office to confirm these coms-info@bristol.ac.uk).
- allows calculators (with the usual restrictions that they be "non-programmable"; please email coms-info@bristol.ac.uk for any queries about calculators)
Past exams:
The format is very slightly different to last year:
- There are 12 compulsory short questions.
- In addition, there are 3 long questions. You pick 2 of the 3 long questions. The long questions is really just a collection of a few short answer questions, but where there can be some links across the questions.
- Previous years have multiple choice questions and short answer questions, while this year we just have short answer questions (remember that the long questions are really just a collection of shorter questions). Importantly, the content + difficulty of the MCQs vs short answer questions should be very similar.
| files | |
|---|---|
| Lecture 1 | Why brains; Brain anatomy |
| Lecture 2 | Neuron anatomy; Neural communication; Brain recording |
| Problem Sheet | Problem Sheet |
| material | |
|---|---|
| Lecture 1 | The leaky bucket |
| related slides | slides |
| related video | youtube |
| material | |
|---|---|
| Lecture 2 | Integrate and fire |
| related slides | slides |
| related video | youtube |
| Lecture 3 | Integrate and fire / Hodgkin Huxley |
| material | |
|---|---|
| Lecture 4 | more Hodgkin Huxley / synapses |
| Lecture 5 | more synapses |
| Worksheet | worksheet |
| Q5 solution | network.py / network.jl |
| files | |
|---|---|
| Lecture 1 | The Hippocampus and long term memory; Spatial Navigation |
| Lecture 2 | Pattern Separation; Hopfield Networks; Continuous attractors and navigation |
| Problem Sheet | Problem Sheet |
| Answers | Answers |
Laurence managed to get COVID last week, so it'll be video lectures while he is recovering. Hopefully back to usual service next week!
| 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] |
| 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] |