| Unit name | Computational Neuroscience (Teaching Unit) |
|---|---|
| Unit code | COMS30017 |
| Credit points | 0 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Aitchison |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Basic knowledge of Python or Julia programming languages is helpful as the coursework (both formative and summative) will be easiest implemented in these languages. No previous neuroscience knowledge is required. |
| Units you must take alongside this one (co-requisite units) |
EITHER COMS30016 Computational Neuroscience (10 credit examination assessment) OR COMS30080 Computational Neuroscience (20 credit examination and coursework assessment). Please note: COMS30017 is the Teaching Unit for the Computational Neuroscience option. Computer Science and Mathematics and Computer Science students can choose to be assessed by either examination (10 credits, COMS30016) or examination and coursework (20 credits, COMS30080) by selecting the appropriate co-requisite assessment unit. Any other students that are permitted to take the Computational Neuroscience option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30016). |
| Units you may not take alongside this one |
None. |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
This unit Aims to provide the student with an understanding of computational principles of biological computations performed in the brain by single neurons and network of neurons, for the following brain processes:
How does this unit fit into your programme of study
This is an optional unit that can be taken in Year 3.
An overview of content
The unit gives an introductory overview of the field of computational neuroscience. We study the brain at scales ranging from very small (synapses) to very large (systems).
How will students, personally, be different as a result of the unit
Students will gain general insight into how computational neuroscientists understand brain function.
Learning Outcomes
On successful completion of this unit, students will be able to:
1.Employ computational principles of the brain in their future engineering work.
2.Undertake research on the brain with comprehension of brain’s purpose (i.e., information processing).
3.For each levels of abstraction (single neuron, network of neurons, interacting brain areas): comprehend the assumptions made by the models, validity of the assumptions, and computational principles.
4.Have general overview knowledge about the brain’s computational functions.
5.Be able to derive and solve some of the key mathematical equations underlying classic computational models of brain function.
When the unit is taken with the associated 20 credit option that includes coursework, students will also be able to:
1.Simulate simple models of neurons, networks, and cortical areas in languages such as Python or Julia.
2.Analyse a dataset of brain activity.
In addition to lectures, each week there is a problem sheet, with support in a Teaching Assistant-led problem class. If taken with coursework, the unit provides weekly coursework support sessions.
Tasks which help you learn and prepare you for summative tasks (formative):
Teaching will take place over Weeks 1-7, with coursework support in weeks 9-11 and for students assessed by examination, consolidation and revision sessions in Weeks 12. We will have lectures in weeks 1-7, along with problem classes with TA support in weeks 2-8
Tasks which count towards your unit mark (summative):
2 hour exam (10 credits: COMS30016 - 100%; COMS30080 – 50%)
In addition, students taking COMS30080 Computational Neuroscience (with Coursework) will also take a coursework in weeks 9-11 (50%).
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMS30017).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
