MSc Systems Neuroscience
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 give rise to sensori-motor control and information processing, and it’s these interactions that provide us with a foundation for understanding the relationship between brain structure and brain function in the context of the cognitive, perceptual and motor mechanisms that underpin behaviour.
To develop our understanding of this relationship we need to integrate experimental and theoretical approaches. That’s why the MSc Systems Neuroscience course has been designed to provide you with interdisciplinary training in cutting-edge approaches to understanding how brain cells interact in networks to contribute to function and behaviour.
At Sheffield, we have a strong research track-record in computational neuroscience, cognitive neuroscience, and systems neuroscience and by giving you access to experts in the field as well as world-class research facilities from neurobotics platforms to neuroimaging equipment, we’re training the next generation of brain scientists to tackle the relationship between brain and behaviour.
Course Director: Dr Chris Martin
If you would like to know anything else about this course, contact: email@example.com | +44 (0)114 222 6531
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Pathway programme for international students
|About the course||
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 computer simulation modelling, data visualisation and neuroanatomy.
In semester one you’ll build on your existing knowledge, giving you a thorough understanding of the fundamentals of neuroscience, computational neuroscience and mathematical modelling. Once you’ve developed a solid foundation in these areas at the core of systems neuroscience, semester two will be devoted to advanced modules where you’ll tailor your learning and choose to specialise in one of two distinct routes of study: pathway 1 or pathway 2.
Pathway 1 will train you in methods for visualising brain networks, including electrophysiology, optical imaging, and functional magnetic resonance imaging, as well as experimental protocols. Pathway 2 will build on your theoretical skills in this area of computational neuroscience before exploring advanced topics including memory, language, and perception at the level of brain networks. Whichever route you choose, this training will develop your transferable skills in critical reading, writing, project management, computational modelling, data visualisation, and scientific programming.
Semester three will be devoted to your extended research project which is the biggest part of this MSc. With one-to-one supervision, you’ll spend 17 weeks applying the knowledge you’ve gained throughout your masters to investigate a specific issue at the cutting-edge of cognitive neuroscience that’s linked to active research in the Department of Psychology. Previous project titles include:
This course is designed to equip you with the skills and knowledge to approach an exciting career in neuroscience, data science or further study to PhD or a Doctorate in Clinical Psychology. To do this, you’ll learn through hands-on laboratory sessions, problem-solving classes, lectures, seminars and individual projects. You’ll be assessed through formal examinations and coursework which may include essays, poster presentations and a dissertation.
Read more about this course on the University of Sheffield's webpages for postgraduate students:
For this course, we usually ask for a 2:1 honours degree or equivalent qualification in either a life science or mathematical/physical science.
We can also accept qualifications from other countries. You can find out which qualifications we accept from your country on the University's webpages for international students.
International pathway programmes
If you are an international student who does not meet our entry requirements, the University of Sheffield International College offers a Pre-Masters in Science and Engineering programme. This programme is designed to develop your academic level in your chosen subject, introduce you to the study skills that will be vital to success and help with language if you need it.
Upon successful completion, you can progress to this degree at the University of Sheffield.
English Language Requirements
If you have not already studied in a country where English is the majority language, it is likely that you will need to have an English language qualification.
You can find out whether you need to have an english language qualification, and which other English language qualifications we accept, on the University's webpages for international students.
The English Language Teaching Centre offers English language courses for students who are preparing to study at the University of Sheffield.
|Funding and scholarships||
Funding is available, depending on your fee status, where you live and the course you plan to study. You could also qualify for a repayable postgraduate masters loan to help fund your studies.
Up-to-date fees can be found on the University of Sheffield's webpages for postgraduate students:
Departmental Taught Postgraduate Bursaries
Each year we offer two bursaries to students on this program. Students who are awarded a bursary get a £1,500 reduction in their tuition fees. These bursaries are awarded on a competitive basis, based on:
To be considered for a bursary for a course you intend to start in 2019, submit your application by 31 May 2019. All applications received before this deadline will automatically be considered for a bursary.
All students will study:
|Fundamentals of Neuroscience (15 credits)||
This module provides an introduction to core aspects of contemporary neuroscience, and describes 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.
|Computational Neuroscience 1 (15 credits)||
This module provides an introduction to methods in computational neuroscience from two different, but complementary perspectives. First, a high-level or ‘top-down’ view explores how neurons encode and decode sensory information. Second a ‘bottom-up’ or mechanistic approach looking at single neuron models at different levels of abstraction – from a simple ‘integrate-and-fire’ approximations to full conductance-based compartmental models. Throughout this module, the emphasis is on the use of mathematical and computational models of single neuronal function.
|Applied Neuroanatomy and Clinical Neuroscience (15 credits)||
This module comprises studying practical neuroanatomy (human brain dissection) and an overview of neuroradiology (mainly magnetic resonance imaging [MRI] and computed tomography [CT]). The two complementary components of neuroanatomy and neuroradiology will be taught in parallel over the semester. For neuroanatomy, a five-session programme involving practical brain dissection and detailed coverage of the structural and functional units of the human brain will be undertaken. For neuroradiology a series of lectures will be given. Both components will be supported by tutorials.
|Systems Neuroscience (15 credits)||
The module provides an advanced understanding of the brain's major computational systems and how they have been modelled. Major processing units of the brain (e.g, cerebellum and basal ganglia) will be described and, where appropriate, emphasis will be placed on understanding each of these structures as a series of repeating micro- or macrocircuits. The various strategies adopted for modelling these circuits and their interactions with other brain systems will be presented and their predictions for biology considered.
|Data Analysis and Visualization (15 credits)||
This module will train students in 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 and will focus on the programming language Python and associated scientific software. No prior programming experience will be necessary.
|Research Project in Cognitive Neuroscience (75 credits)||
The module allows students to work on an extended (17 week) 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.
Students will also study two modules in either pathway 1 or pathway 2.
|Neuroimaging 1 (15 credits)||
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 design and efficiency will also be discussed and taught in depth.
|Neuroimaging 2 (15 credits)||
This module further develops on the foundational material in Neuroimaging 1 and provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the techniques of electrophysiology, EEG, and MEG, 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.
|Fundamentals of Cognition (15 credits)||
This module provides an overview of the fundamental issues in cognitive neuroscience and its contributory disciplines. Topics include: fundamental issues in cognition (memory, attention, learning, perception, affect), developmental processes from neuroscience, psychology and dynamic systems perspectives, and theoretical approaches including cognitive neuropsychology, symbolic and sub-symbolic modelling, and methodological issues.
|Computational Neuroscience 2 (15 credits)||
This module is based on the themes of information theory, Bayes' theorem, and learning algorithms. Information theory places limits on how much Shannon information can be transmitted/received by any communication channel, Bayes' theorem provides a method for interpreting incomplete or noisy information, and learning algorithms provide a mechanism for acquiring/storing/retrieving information about the environment. These three related ideas will be explored in the context of neuronal information processing.
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.
We timetable teaching across the whole of our campus, the details of which can be found on our campus map. Teaching may take place in a student’s home department, but may also be timetabled to take place within other departments or central teaching space.