Outline

Overview

Major technological advances in recent years have allowed us to finally tackle some of the hardest questions in neuroscience research. How does a complex interconnected cellular system generate intelligence, feeling and consciousness? What makes the human brain unique? Neuroimaging allows us to visualise brain structure and function in real-time in higher resolution and complexity than ever before. Computational neuroscience modelling allows us to predict how the brain networks connect on a system-wide level. Advances in neuro-engineering and brain-computer interfaces allow an unprecedented ability to understand nervous system function and modulate neural function in health and disease. This unit will provide an introduction into neuroimaging, computational neuroscience and neuro-engineering tools to enable you to investigate complex questions. With an interest in practical application, you will have the opportunity to consider how to apply these tools in the context of social and affective neuroscience. This unit is an opportunity to integrate and apply your knowledge of neuroscience theory, anatomy and cellular function at a network level to tackle fundamental questions and to understand how a systems perspective can assist in understanding complex behaviour.

Unit details and rules

Academic unit	Department of Medical Sciences
Credit points	6
Prerequisites ?	(NEUR2001 or ANAT2010 or MEDS2005 or ANAT2910) and 72 credit points 1000 to 3000 level units
Corequisites ?	None
Prohibitions ?	NEUR3001 or NEUR3901 or NEUR3002 or NEUR3902 or NEUR3906
Assumed knowledge ?	NEUR2001
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Susanna Park, susanna.park@sydney.edu.au
Lecturer(s)	David Mor, david.mor@sydney.edu.au
	Eli Muller, eli.muller@sydney.edu.au
	Mac Shine, mac.shine@sydney.edu.au
	Collin Anderson, collin.anderson@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Experimental design	Applied neuroscience portfolio: Hypothesis generation In-tutorial activity guiding students through hypothesis generation and research design, focused on neuroimaging module.	0%	Week 02 Due date: 06 Aug 2025 at 23:59	1.5 hours	AI allowed
Outcomes assessed:
Written work	Applied Neuroscience Portfolio: Research Context Following on from the hypothesis generation activity, students will prepare a research grant expression of interest, consisting of an abstract, significance statement and annotated bibliography related to neuroimaging module content.	20%	Week 04 Due date: 29 Aug 2025 at 23:59 Closing date: 12 Sep 2025	750 words	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Applied Neuroscience Portfolio: Computational Neuroscience Workbook Activity-based in-tutorial assessment related to computational neuroscience module.	15%	Week 06 Due date: 10 Sep 2025 at 23:59	1.5 hours	AI prohibited
Outcomes assessed:
Written work	Applied Neuroscience Portfolio: Social & Clinical Context Prepare a "The Conversation" style article addressing a controversial neuroscience topic related to neuroengineering or its clinical application, utilising research literature as key references.	20%	Week 09 Due date: 10 Oct 2025 at 23:59 Closing date: 31 Oct 2025	900 words	AI allowed
Outcomes assessed:
Experimental design	CREATive Poster Introduced in tutorials, students will examine topics in affective neuroscience through primary scientific literature, and prepare a poster dissecting a paper with follow-up experiments/theoretical concepts as per the CREATE framework.	20%	Week 12 Due date: 31 Oct 2025 at 23:59 Closing date: 05 Nov 2025	Poster format	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Integrated Poster Showcase and Presentation Students will present their poster in a showcase session and answer facilitator-led questions about the poster and the development process	25%	Week 13 Due date: 05 Nov 2025 at 23:59	1.5 hours	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

Applied Neuroscience Portfolio in a Research Context: You will prepare a research grant expression of interest, related to neuroimaging module content, composed of hypotheses, abstract and significance statement and annotated reference list.

Computational Neuroscience Workbook: Working in small groups, you will prepare a computational neuroscience workbook during tutorial sessions.

Social & Clinical Context: Working in small groups, you will write a ‘The Conversation’ style article addressing a controversial neuroscience topic related to neuroengineering or its clinical applications.

CREATive poster: Introduced in tutorials, students will examine topics in affective neuroscience through primary scientific literature, and prepare a poster dissecting a paper with follow-up experiments/theoretical concepts as per the CREATE framework.

Poster showcase: Present your poster in an engaging way and answer facilitator-led questions about your poster and your process.Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Students are expected to manage their time and to prioritise tasks to meet deadlines. Assessment items submitted after the due date without an approved extension using a special consideration or special arrangement form or request will incur penalties. Failure to meet assessment deadlines will incur mark deductions of 5% of the maximum awardable mark available for every day past the due date (for electronic submissions, days late includes Saturdays, Sundays and public holidays). These deductions will continue for 10 calendar days, until the solutions for the assignment are released, or marked assignments are returned to other students. At that point the mark awarded will be zero. For example, on an assignment given a mark of 70/100, the penalty would be 5 marks if submitted up to 24 hours late, resulting in a final mark of 65/100. If the assignment is submitted 6 days late, the penalty would be 30 marks and the final mark would be 40/100. If the assignment is more than 10 days late, submitted after the solutions for the assignment are released, or marked assignments are returned to other students, the final mark will be 0/100.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Neuroimaging Module (6 lectures beginning in week 1)	Lecture (6 hr)
Week 01	Neuroimaging module tutorials (1.5 hours per week for weeks 1-3)	Tutorial (4.5 hr)
Week 04	Computational neuroscience module (6 lectures beginning in week 4)	Lecture (6 hr)
Week 04	Computational neuroscience module tutorials (1.5 hours per week for weeks 4 - 6)	Tutorial (4.5 hr)
Week 07	Neuroengineering module (6 lectures beginning in week 7)	Lecture (6 hr)
Week 07	Neuroengineering module tutorials (1.5 hours per week for weeks 7 - 9)	Tutorial (4.5 hr)
Week 10	Affective neuroscience module (6 lectures beginning in week 10)	Lecture (6 hr)
Week 10	Affective neuroscience tutorials (1.5 hours per week for weeks 10-12)	Tutorial (4.5 hr)
Week 13	CREATive poster showcase, presenting your poster and answering questions	Presentation (2.5 hr)

Attendance and class requirements

Attendance: All students are expected to attend all lectures, practical classes, tutorials, and case studies. A variety of notes, handouts, data sheets, and information provided throughout the Unit of Study are intended to supplement the lectures not to substitute for them. Absences from all scheduled practical sessions, tutorials and case studies must be explained and supported by appropriate documentation. Even if special consideration has been granted, it is the student’s responsibility to know and understand the material covered in the missed session. Please note that the Faculty of Science has a minimum 80% attendance requirement for a student to pass any unit of study (Faculty of Science Resolutions at http://sydney.edu.au/handbooks/science/rules/faculty_resolutions.shtml).

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Critically analyse the methods by which higher brain functions are measured and evaluated
LO2. Integrate statistical and data science approaches with functional-anatomical knowledge of the brain
LO3. Justify and compare the use of brain imaging modalities
LO4. Debate current controversies in neuroscience
LO5. Demonstrate applied knowledge of social and affective neuroscience
LO6. Design and execute investigations of your neuroscience knowledge in real-life situations
LO7. Demonstrate advanced knowledge of neuroanatomy, cellular function within the brain, and how these influence human behaviour

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

NEUR3006: Applied Neuroscience

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links