MSc Cognitive and Computational Neuroscience
One of the greatest challenges in modern science is to understand the relationship between brain and behaviour. For the last 25 years, researchers in our department have been recognised internationally for their ground-breaking insights into this relationship. These innovations continue today in our Adaptive Behaviour Research Group and Active Touch Laboratory.
We tackle the problem from two directions. From the bottom-up, we investigate the emergence of brain function from underlying neural circuitry, by constructing rigorous computational and theoretical models. From the top-down, we relate cognitive and behavioural function back to the underlying neural substrate, by applying novel experimental and analysis techniques.
This multi-disciplinary masters course provides a great foundation for students who want to tackle questions about the relationship between brain and behaviour, using this combination of top-down and bottom-up approaches. As the course content crosses traditional disciplinary boundaries, we accept students with interests in brain and behaviour that stem from backgrounds in psychology, neuroscience, and biology, as well as physics, computer science, engineering, and mathematics.
Our experts have carefully constructed this course over the last decade, so that it is both challenging and rewarding, making the most of the range of skills that our students bring to the study of brain and behaviour. Many of our students and graduates have gone to publish research based on the work they completed during this course, or during their subsequent PhD.
|Examples of recent publications by our students||
The following is a list of just a few publications based on research completed by our students, either during their masters degree or during a PhD they completed after graduating from this course. Our students' names are highlighted in bold.
To apply for this course, complete the University of Sheffield's postgraduate online application form.
You can find more information about the application process on the University's postgraduate webpages.
Course Director: Dr Stuart Wilson
If you would like to know anything else about this course, contact: firstname.lastname@example.org | +44 (0)114 222 6533
You can also visit us throughout the year:
Pathway programme for international students
|About the course||
This course provides graduate training in computational and cognitive neuroscience. It introduces core problems in computational neuroscience, adaptive behaviour, neuroscience, ethology, evolutionary biology, connectionism, and robotics, and introduces core techniques in experimental cognitive psychology, computer simulation and modeling, brain imaging and mathematics.
We have a strong track record in both computational neuroscience and cognitive neuroscience, consolidated in the Adaptive Behaviour Research Group, the Active Touch Laboratory, and the Centre for Signal Processing in Neuroimaging and Systems Neuroscience.
This is a full-time course taught over 12 months through lectures, seminars and laboratory classes. You will also complete your own independent research project, working under the close supervision of an academic member of staff. The course starts in September, preparation for your research project begins in March, and you will submit your final dissertation after exams at the end of August.
Investigative techniques covered: Computational and mathematical modelling at several levels of description (from the biophysics of neural membranes to neural populations), neural network theory and learning algorithms, biomimetic robotics, anatomical tracing, multi-electrode electrophysiology, optical imaging, behavioural observation.
Areas of interest covered: The neurobiology and cognitive neuroscience of action selection, sensorimotor control, learning and memory, the neural basis of addiction, sensory control of dopamine, information theory and neural coding, neural networks and connectionism, the rat somatosensory pathway, and automatic and controlled processing.
This degree will give you:
Read more about this course on the University of Sheffield's webpages for postgraduate students:
This course use mathematical concepts, but a large amount of teaching time is devoted to these areas, so the main thing we ask for is a willingness to learn. For example, you will learn about:
To get some idea of the sort of technical knowledge you'll be aiming to have at the end of your course, take a look through the following PDF documents.
For this course, we usually ask for A 2:1 honours degree or equivalent qualification in either a life science or mathematical/physical science. You should also include one or two paragraphs of text when completing your application form, giving evidence of your mathematical experience or qualifications, to show us that you will be able to tackle the type of material listed under the 'Technical content' tab.
If your background is in life sciences, you can take specially-designed mathematics and programming classes during your degree. Relevant mathematics teaching is built into individual modules.
If you have a qualification in mathematics, engineering, or the physical sciences, this degree includes is an introductory neuroscience course to ensure that all students have the same key knowledge.
We also accept medical students who wish to intercalate their studies.
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.
|What our graduates say||
Jake studied BEng Mechatronic and Robotic Engineering at Sheffield before choosing to pursue the MSc Cognitive and Computational Neuroscience.
Laura chose the MSc because it gave her the opportunity to learn about computational modelling methods alongside imaging techniques such as fMRI.
All students will study:
|Fundamentals of Neuroscience (15 credits)||
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.
|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.
|Mathematical Modelling and Research Skills (15 credits)||
This module develops basic skills required to understand and participate in research in computational and cognitive neuroscience. The course begins with a refresher course in reading and writing skills, understanding of quantitative data and basic algebra and calculus. The course moves on to cover more advanced mathematical modelling techniques including matrix algebra, ordinary differential equations and optimisation methods. Programming skills are introduced via the MatLab modelling language. All topics are illustrated by application to concrete modelling examples relevant to contemporary neuroscience.
|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.
|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.
|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.
|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.
|Modelling Natural Systems (15 credits)||
This module will provide a practical introduction to techniques used for modelling and simulating dynamic natural systems. Many natural systems can be modelled appropriately using differential equations, or individual based methods. In this module, students will explore and understand both modelling approaches. They will gain knowledge of the assumptions underlying these models, their limitations, and how they are derived. Students will learn how to use MATLAB to simulate and explore the dynamics of computational models, using a variety of examples drawn from both natural systems.
|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, apsyfter 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.
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.