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Cognitive and Systems Neuroscience
School of Psychology,
Faculty of Science
Course description
It’s widely recognised by modern neuroscience that taking a systems approach to exploring the brain is vital to understanding brain function. Systems neuroscience focuses on the interactions between neural structures in networks that influence sensorimotor control and information processing. It’s these interactions that provide us with a foundation for understanding the relationship between brain structure and brain function that underpins behaviour.
This 12-month course is designed to provide you with in-depth training into the core problems in systems neuroscience, and will develop your understanding of the disciplines and techniques used to address these problems, such as research methods in cognitive neuroscience, data visualisation, computational modelling and neuroimaging methods.
Over six months, you'll work on your research project in cognitive neuroscience with one of our world-leading experts. Your research topic could range from theoretical to basic neuroscience. You may have the opportunity to collect and analyse real-life cognitive brain science data, using state-of-the-art equipment, before presenting your findings at our summer postgraduate students' conference.
The project gives you the opportunity to put your new techniques in experimental neuroscience into practice, while exploring ideas at the cutting-edge of cognitive neuroscience. MSc research projects often form the basis of publications in peer-reviewed journals.
- Example research projects
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- Impaired neurovascular coupling in hAPP-J20 Alzheimer's mice models following electrode insertion
- Investigating the effect of 5-HT2a receptor agonist 1P-LSD on cerebral blood flow and cerebral blood oxygenation in an animal model
- The contribution of cortical interneurons to neurovascular coupling: An optogenetic study
- An examination of parvalbumin expressing interneurons in murine models of autism spectrum disorder
- Example past papers published, including student authors
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- Brooke JM, James SS, Jiminez-Rodriguez A, Wilson SP (2022) Biological action at a distance: Correlated pattern formation in adjacent tessellation domains without communication. PLoS Computational Biology. doi:10.1371/journal.pcbi.1009963
- Wilson SP, James SJ, Whiteley DJ, Krubitzer LA (2019) Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors. Scientific Reports 9: 16750. doi:10.1038/s41598-019-53251-w
- Cafferata, R. M. T., Hicks, B., & von Bastian, C. C. (2021). Effectiveness of cognitive stimulation for dementia: A systematic review and meta-analysis. Psychological Bulletin, 147(5), 455–476. doi.org/10.1037/bul0000325
If you have a passion for understanding the brain and behaviour, whether your background stems from biology, engineering, physics, mathematics, psychology or medicine, this interdisciplinary course has been designed to make sure that all students gain in-depth knowledge of the fundamentals of neuroscience and research methods in cognitive neuroscience, ready for an exciting career in research or industry.
The University is home to the Neuroscience Institute which brings together internationally-recognised expertise in medicine, science and engineering to improve the lives of patients and families affected by neurological, sensory and developmental disorders.
Other courses in cognitive neuroscience
We offer MSc courses that cover the full breadth of cognitive neuroscience, from the biological basis, to imaging and simulation, allowing you to discover the area that you’re most interested in:
Modules
You'll study:
- Fundamentals of Cognition
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The module provides an overview of the fundamental issues in cognitive neuroscience and its contributory disciplines. The approach taken is in terms of its development over the past 50 years, providing an overview of the key concepts in the information processing approach and in cognitive science, followed by an analysis of the advances that have been made recently using cognitive neuroscience techniques. Topics include: fundamental issues in cognition (memory, attention, learning, language); theoretical approaches including cognitive neuropsychology, symbolic and sub-symbolic modelling; and methodological issues.
15 credits - Fundamentals of Neuroscience
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The module provides an introduction to core aspects of contemporary neuroscience, and it will consider the current state of knowledge in the field, central theoretical issues and key practical approaches. Topics that are discussed include: neural signalling, sensation and sensory processing, movement and its central control, the 'changing brain' (development and plasticity in the nervous system) and complex brain functions.
15 credits - Research Methods in Cognitive Neuroscience
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Researchers in Cognitive Neuroscience use a range of different methods and techniques to better understand the biological processes underpinning cognition. An understanding of the differences between these methods, and their advantages and disadvantages when addressing different research questions, is critical for being able to understand existing research as well as designing and conducting novel research projects.
15 credits
This module provides an introduction to a range of state-of-the-art methods used in cutting-edge cognitive neuroscience research, such as EEG, eye-tracking, and tDCS. The module comprises a mix of lectures introducing each technique, demonstrations where students will gain hands-on exposure to cognitive neuroscience equipment, and seminars where students present a recent scientific article using that method. By the end of the module, students will have acquired the knowledge and understanding of a range of cognitive neuroscience methods, their benefits and pitfalls, and be able to use that gained understanding to critically evaluate published research and design new studies. - Data Analysis and Visualisation
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This module provides basic skills in computational data analysis. Students will learn how to import/export scientific data sets in different formats, how to process and transform them, and how to visualise results. Teaching will be hands-on and computer lab-based. Teaching will focus on the programming language R and associated scientific software. No prior programming experience will be necessary.
15 credits - Systems Neuroscience
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The module provides an advanced understanding of the brain's major computational systems and the theoretical or model-driven approaches to research of these topics. Major processing units of the brain will be described and, where appropriate, emphasis will be placed on understanding each of these structures in terms of both their micro- and macro-circuitry. One focus of the module will be to impart an appreciation of how many fundamental questions relating to brain function requires study at a range scales, from single cell to whole brain and behaviour. The various strategies adopted for investigating and modelling brain-circuits, and the consideration of circuits as the defining feature of brain systems, will be presented.
15 credits - Research Project in Cognitive Neuroscience
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The module allows students to work on an extended research project within computational neuroscience and/or cognitive neuroscience and/or systems neuroscience and/or analysis of brain imaging data. Students will learn and apply appropriate research techniques, analyse and interpret the results, and write up the research findings using recognised journal frameworks. Students will receive guidance and regular feedback from their supervisors. The project culminates in an oral presentation and a written dissertation.
75 credits
You'll study one module from this group (15 credits):
- Neural Dynamics and Computation
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This module starts with a primer on neuroscience and the role of computational neuroscience. The module will cover various modelling approaches, from classic biologically plausible to abstract-level models of neurons. The module will then move to higher levels of modelling approaches, such as neural networks and reinforcement learning. While the module emphasises methodological issues and how models can be built, tested and validated at each level, we will also draw connections to specific brain regions to motivate and illustrate the models.
15 credits - Neuroimaging 1
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This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the functional imaging techniques of electrophysiology, optical methods and calcium imaging, each of which will be introduced in the lecture component of the module. In the associated lab classes, students will gain first-hand experience of analysing and processing data sets arising from these techniques.
15 credits
You'll study one module from this group (15 credits):
- Neurocognitive Modelling
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This module concerns inferring and modelling neural and cognitive processes underlying human behaviour using computational means. One part of the module will cover normative models, which allow us to solve problems optimally along with their neural or cognitive representations. The other part of the module will focus on cognitive models, which involve fitting models to behavioural data to estimate latent parameters that are assumed to underlie the data and allow us to make inferences about their properties.
15 credits - Neuroimaging 2
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This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies employed, specifically those based around functional magnetic resonance imaging (fMRI). The two aspects of neuroimaging (techniques and data analysis) will be taught over the semester. For neuroimaging techniques, after introducing the physical principles underlying fMRI, a description of fMRI-based methods for mapping brain structure and function will follow. For neuroimaging data analysis, the general linear model methodology will be introduced based on the software SPM (Statistical Parametric Mapping), which is one of the most widely used packages for fMRI data analysis. Issues concerning fMRI experimental de-sign and efficiency will also be discussed and taught in depth.
15 credits
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.
Open days
An open day gives you the best opportunity to hear first-hand from our current students and staff about our courses.
Open days and campus tours
Duration
1 year full-time
Teaching
You’ll learn through hands-on laboratory sessions, problem-solving classes, lectures, seminars and individual projects.
Your individual research project is the biggest part of your course, where you’ll be working alongside PhD students and experienced postdoctoral researchers. Here you’ll gain extensive first-hand experience as a researcher, and will have access to the outstanding research facilities in Sheffield.
Assessment
You'll be assessed through formal examinations and coursework which may include essays, poster presentations, coding assignments, and a dissertation.
Regular feedback is also provided, so you can understand your own development throughout the course.
Your career
With the valuable skills and knowledge that you’ll develop throughout your research training, including computational modelling, imaging, and analysis expertise, you’ll be well equipped for careers including:
- Roles within deep learning, machine learning or artificial intelligence
- Analysis and visualisation of data within hospitals, other healthcare providers or the pharmaceutical industry
- Pursuing a career in research, understanding major diseases like stroke, Alzheimer’s, Parkinsons and epilepsy within academia or governmental organisations.
If you choose to continue your research training, this course is great preparation for a PhD in areas including neuroscience, artificial intelligence, and brain interfaces, or to begin clinical training.
Learn more about where your psychology masters could take you here.
By choosing the School of Psychology for your postgraduate study, you'll join our global alumni network, where hundreds of our employed graduates are working across academia, healthcare, and related fields, and completing further study around the world. Explore our interactive map of graduate destinations:
School
School of Psychology
The School of Psychology at Sheffield is focused on exploring the science behind the human brain and human behaviour.
Our teaching is informed by cutting-edge scientific research, which ranges from cognitive and neural processes across the lifespan to the wellbeing of individuals and society. All of this has an impact on the population.
Our work explores child development, psychological therapies, health and wellbeing, lifestyle choices, cognitive behavioural therapy, safe driving, mother-baby interaction, autism, Parkinson's disease, and reducing prejudice and inequality. It’s research like this that our students are able to get involved in throughout their course.
Facilities
At Sheffield, we have a range of practical teaching and research facilities where you can get hands-on, applying the knowledge you’ve gained in your masters.
For your statistical training, we have computer labs where you can access industry standard statistical analysis software SPSS, computational modelling software MATLAB, as well as flexible programming languages Python and R.
You’ll also have the chance to access a range of tools for testing participants during your research projects. Depending on your project, these may include eye-tracking technology used in perception studies, TMS and TDCS equipment for experiments involving brain stimulation, and our state-of-the-art EEG suite for measuring brain activity. Individual and group testing rooms are also available.
Entry requirements
Minimum 2:1 undergraduate honours degree in a relevant subject with relevant modules.
Subject requirements
We accept degrees in the following subject areas:
- Behavioural Neuroscience
- Cognitive Neuroscience
- Computational Neuroscience
- Neuroimaging
- Neuropharmacology
- Neuroscience
- Psychology
We may be able to consider degrees in Life Sciences, Physical Sciences, Mathematics or Engineering.
Module requirements
You should have studied at least one module from each of the following areas:
Area 1 Neuroscience:
- Clinical Neuroscience
- Cognitive Neuroscience
- Computational Neuroscience
- Developmental Neuroscience
- Introduction to Neuroscience
- Neuroanatomy
- Neuroethics
- Neuroimaging
- Neuropharmacology
- Neurophysiology
Area 2 Quantitative:
- Advanced Research Methods in Psychology
- Data Analysis in Psychology
- Experimental Design
- Psychology of Research
- Quantitative Research Methods
- Research Ethics in Psychology
- Research Methods in Psychology
- Research Skills for Psychology
- Scientific Writing for Psychology
- Statistics for Psychology
We also accept medical students who wish to intercalate their studies.
English language requirements
IELTS 6.5 (with 6 in each component) or University equivalent
If you have any questions about entry requirements, please contact the school/department.
Fees and funding
Apply
You can apply now using our Postgraduate Online Application Form. It's a quick and easy process.
Contact
study@sheffield.ac.uk
+44 114 222 6533
Any supervisors and research areas listed are indicative and may change before the start of the course.
Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.