Course coordinator
Dr Clarissa Whitmire
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
The electrical activity within the nervous system represents information about the external world, the internal state, and the commands to control the body. This transformation between the information represented in the neural pathways and the electrical activity of neurons is referred to as the neural code. This course provides an overview of the underlying biological processes that give rise to neural activity, the models of neural encoding and decoding including leading hypotheses about information representation, and the extension of these models to population encoding in neural circuits. This course is organized into thematic sections with formal lectures on critical topics to gain exposure to state-of-the-art neural engineering.
This is an interdisciplinary course that links subject matter across multiple fields. Students must attend sessions in person to maximally engage with the content. This is the first offering of this course.
Course requirements
Assumed background
Students are assumed to be competent in signals and systems at an intermediate level. Knowledge of scientific programming language (e.g. Matlab, Python) and/or neurobiology are an advantage. If you have not previously studied these materials, you need a commitment and willingness to learn these topics on your own. An interest in neuroengineering is essential.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(ELEC3004 OR BIOE3001) AND (STAT2201 OR CHEE2010)
Course contact
Course staff
Lecturer
Dr Clarissa Whitmire
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course is aimed at exposing engineering students to neuroscience. It is expected that upon successful completion of the course students will have an in-depth understanding of signal generation in neuron and the mechanisms by which neurons can represent information. Students should be able to implement common neural models and analytical tools, and for a selected set of state-of-the-art technologies, students should be able to understand the technical solution employed and to discuss the approach based on an understanding of commonly applied methods.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Demonstrate how neurons generate and propagate electrical signals and the basic properties of signal transmission in neural circuits
LO2.
Critique and select relevant frameworks for analysing neural data
LO3.
Implement signal processing algorithms for practical problems involving neuroscience
LO4.
Demonstrate the need of engineering frameworks for large scale neural modelling
LO5.
Critically analyse information independently acquired from diverse sources (e.g., research articles, books, patents, company product data sheets) in providing solutions to real-world neuroscience problems.
LO6.
Effectively communicate (using oral and written communication and appropriate figures and visualizations) the real-world implementation of analytical frameworks in neuroscience
LO7.
Orally present and report a research project on neural signal processing
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Practical/ Demonstration, Tutorial/ Problem Set |
Problem Sets
|
45% |
Problem Set #1 19/03/2025 2:00 pm Problem Set #2 2/04/2025 2:00 pm Problem Set #3 7/05/2025 2:00 pm |
| Paper/ Report/ Annotation | Project Proposal | 10% |
16/04/2025 2:00 pm |
| Computer Code, Paper/ Report/ Annotation | Project Final Report | 20% |
23/05/2025 2:00 pm |
| Presentation |
Project Presentation
|
25% |
26/05/2025 2:00 pm
Project slides will be due at 2pm 26/05/2025. Project presentations will take place in an allocated timeslot in Week 13. |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Problem Sets
- Identity Verified
- In-person
- Mode
- Oral, Written
- Category
- Computer Code, Practical/ Demonstration, Tutorial/ Problem Set
- Weight
- 45%
- Due date
Problem Set #1 19/03/2025 2:00 pm
Problem Set #2 2/04/2025 2:00 pm
Problem Set #3 7/05/2025 2:00 pm
Task description
Each problem set covers the technical content presented in each course module. Each problem set involves developing computer code to analyse data, reproduce the results of the selected model, or other theory-based questions.
Rationale: After the course module, students are asked to implement the topics they have learned through hands on problem sets. The assignment is to be completed individually. Following submission of the assignment, the problems and solutions will be discussed in class. Students will be expected to present answers orally during the session to demonstrate their solutions.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Therefore, the grading will be assessed based on the submitted written work and the orally presented defence of that work.
Submission guidelines
Problem sets will be submitted electronically through the learning management system.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 3 days. If there are exceptional circumstances, an exemption may be approved and may involve submitting/discussing your work as it stands. Exemptions must be requested as an extension with a note specifying exemption via my.UQ.
Late submission
100% Late Penalty after 1 hour grace period. The one-hour grace period is recorded from the time the submission is due.
Project Proposal
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 10%
- Due date
16/04/2025 2:00 pm
Task description
Rationale: Students are asked to present the final project definition, scope, background and planned work. Projects are individual. The written document should cover the scope and relevance of the project, the reviewed literature and background material, the work carried out so far (if applicable), and the work remaining to be done (plan). Open questions or uncertainty about prioritising of different aspects should be brought forward here to receive feedback from the course coordinator. Although all the above is needed for a proposal to be self-contained, the emphasis should be on the thesis definition, scope and future work. The material should be prepared in a fashion that suits written presentation.
Document Preparation and Delivery: All project proposals should be word processed and grammar and spelling should be correct throughout. Figures should be clearly and readily understood. There is no set length of the proposal, but the general expectation is that it will be less than 5 pages in length. (Page counts are based on 1.5 spaced 12-point font and do not include front-matter or appendices.).
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
The project proposal will be submitted electronically through the learning management system.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Project Final Report
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 20%
- Due date
23/05/2025 2:00 pm
Task description
Rationale: Students will be required to implement the project of their design (proposed in the course) that builds on the concepts developed in the course. Students will be required to write a final report about their project and present their work orally. The students will work individually. The format and marking follow the project rubric.
The project report is the major means of reporting the contribution of the project. The report should comprehensively include material on the problems and goals of the project, applicable methods, the approach taken, major decisions and the reasons for the selection of goals and methods, results, the extent to which the goals have been achieved, the relevance, importance and context of achievements, and the reasons for any shortcomings. Production of the report is regarded as part of the educational value of the project, and the course coordinator can give guidance where appropriate.
Document Preparation and Delivery: All reports should be word processed and grammar and spelling should be correct throughout. Figures should be clearly and readily understood. There is no set length of the final report but the general expectation is that it will be 15-20 pages in length. (Page counts are based on 1.5 spaced 12-point font with figures and do not include front-matter or appendices.). Follow the preferred document standard and method of specifying references. Variations from this should be discussed with the course coordinator.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
The project report will be submitted electronically through the learning management system.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Project Presentation
- Hurdle
- Identity Verified
- Mode
- Oral
- Category
- Presentation
- Weight
- 25%
- Due date
26/05/2025 2:00 pm
Project slides will be due at 2pm 26/05/2025. Project presentations will take place in an allocated timeslot in Week 13.
Task description
Rationale: In the last week students are asked to present their project definition and scope, the work they carried out and the conclusion they could draw. The seminar should cover the scope and relevance of the project, the work carried out, conclusions and recommendations/open questions for future investigations. Although all the above is needed for a seminar to be self-contained, the emphasis should be on the performed work and open discussion points. The material should be prepared in a fashion that suits oral presentation.
Delivery: The seminar will be scheduled in a 15-minute time slot with 10 minutes presentation and 5 minutes time for Q&A. The date and time of the presentation will be provided separately. The deadline is for the submission of the slides.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
In accordance with UQ Assessment Policy, your presentation will be recorded.
Hurdle requirements
To achieve a grade of 4, students must achieve a grade of 50% on this assessment item. To achieve a grade of 3, students must achieve a grade of 40% on this assessment item.Submission guidelines
The project presentation slides will be submitted electronically through the learning management system. The project presentation will be presented orally to the class during predetermined time slots.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
All extensions will result in a scheduled presentation as set by the course coordinator.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
100% Late Penalty after 1 hour grace period. The one-hour grace period is recorded from the time the submission is due.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be capped at 2 if the final project presentation grade is less than 40%. Grades will be capped at 3 if the final project presentation grade is less than 50%.
The course coordinator reserves the right to moderate marks.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Problem-based learning |
Content Delivery, Exploration, and Workshops |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: