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MSc Cognitive and Computational Neuroscience

One of the greatest challenges in modern science is to understand the relationship between brain and behaviour. In order to achieve this, computational neuroscience works from the top-down, using neuroscientific data to construct rigorous computational models of brain function, whereas cognitive neuroscience approaches the problem from the bottom-up, relating cognitive and behavioural function to its underlying neural substrate.

Bringing together these two new and interdependent disciplines, the MSc Cognitive and Computational Neuroscience programme provides you with training in computer simulation, mathematical modelling, experimental cognitive psychology and brain imaging, allowing you to develop an understanding of the biological foundations of intelligence.

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 ensure that all students gain in-depth knowledge of the fundamentals of neuroscience, ready for an exciting career in research or industry.

Explore the breadth of cognitive neuroscience

At Sheffield, we have a strong research track-record in computational neuroscience, cognitive neuroscience, and systems neuroscience. Because of these expertise, our courses cover the full breadth of cognitive neuroscience, from the biological basis, to imaging and simulation, allowing you to discover and focus on the area that you’re most interested in as you progress through your course.

Other courses in cognitive neuroscience:

Discover the University of Sheffield Neuroscience Institute

Applying

To apply for this course, complete the University of Sheffield's postgraduate online application form.

Online application form

Programme codes:
One year full time: PSYT12
PhD with Integrated Studies: PSYT18

You can find more information about the application process on the University's postgraduate webpages.

How to apply: applying essentials

Contact

Course Director: Dr Stuart Wilson

If you would like to know anything else about this course, contact: psy-pg-admissions@sheffield.ac.uk | +44 (0)114 222 6533

You can also visit us throughout the year:
Postgraduate open days, visit afternoons and online chats

Pathway programme for international students
Don't meet our entry requirements? Pre-Masters in Science and Engineering

About the course

This 12-month course is designed to provide you with in-depth training into the core aspects of contemporary neuroscience, from sensation and sensory processing, to understanding complex brain functions and artificial intelligence, ready for an exciting career in research, healthcare, industry, or further study to PhD level. Throughout your course, our neuroscientists will introduce you to the core problems in computational neuroscience, adaptive behaviour, neuroethology, evolutionary biology, connectionism and robotics. We’ll teach you the core techniques in experimental cognitive psychology that you need, including computational modelling, biomimetic robots, and cutting-edge neuroimaging and give you opportunities to apply these techniques throughout your course.

In semester one, you’ll build on your existing knowledge, giving you a thorough understanding of the fundamentals of cognitive neuroscience, computational neuroscience, mathematical modelling and simulation. Once you’ve developed a solid foundation in these areas at the core of cognitive and computational 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 the fundamentals of biological and artificial cognition and the perspectives of computational biology, including a specialist module taught by the Department of Computer Science. Pathway 2 will build on your theoretical skills before exploring advanced topics in neuroimaging techniques, including multi-electrode electrophysiology, optical imaging, EEG and structural and functional MRI. Whichever route you choose, this training will develop your transferable skills in critical reading, writing, project management, computational modelling, data analysis and visualisation, and scientific programming using the languages Python and MATLAB.

The biggest part of your course is the Research Project in Cognitive Neuroscience. Over three months you’ll work with one of our world-leading experts in the Department of Psychology. Research topics could range from theoretical, to basic neuroscience, with 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 student-led conference. These projects give you the opportunity to put your new techniques in experimental neuroscience into practice whilst exploring ideas at the cutting-edge of cognitive neuroscience. Active research in our computational neuroscience group that you'll be exposed to include the neurobiology and cognitive neuroscience of action selection, sensorimotor control, learning and memory, the neural basis of addiction, information theory and neural coding, neural networks and connectionism, the somatosensory pathway, and automatic and controlled processing. It's common for MSc research projects to form the basis of publications in peer-reviewed journals.

Example research projects include:

  • Cortical arealization and pattern formation: spontaneity and control;
  • Simulating the interaction of self-organisation and selection with Boolean networks;
  • The effects of different spiking patterns and reuptake rates in a model of striatal dopamine;
  • Trial-to-trial variability in human EEG recordings during visual stimulation and behaviour.

Example past papers published including student authors:

  • Bruyns-Haylett M, Luo J, Kennerley AJ, Harris S, Boorman L, Milne E, Vautrelle N, Hayashi Y, Whalley BJ, Jones M, Berwick J, Riera J & Zheng Y (2016) The neurogenesis of P1 and N1: a concurrent EEG/LFP study. NeuroImage.
  • Dickinson A, Jones M & Milne E (2016) Measuring neural excitation and inhibition in autism: different approaches, different findings and different interpretations. Brain Research.
  • Slack R, Boorman L, Patel P, Harris S, Bruyns-Haylett M, Kennerley A, Jones M & Berwick J (2016) A novel method for classifying cortical state to identify the accompanying changes in cerebral haemodynamics. Journal of Neuroscience Methods, 267, 21-34.

In addition to technical skills and specialist knowledge of cognitive and computational neuroscience, throughout your course you’ll also develop transferable skills around critical thinking and communication, sought after by employers around the globe. In order to build these skills, you’ll learn through small-group teaching in a mixture of seminars and lectures, and problem solving, programming and laboratory classes. You’ll be assessed through formal examinations and coursework which may include essays, presentations and a dissertation.

Technical content

This course uses mathematical concepts, and a large amount of teaching time is devoted to equipping you with the skills and techniques you need. The main thing we ask for is a willingness to learn. Throughout your course you'll learn about:

  • Probability theory, probability density functions, Bayes' theorem and maximum likelihood estimation;
  • Calculus, differential equations, and finding extrema of functions;
  • The general linear model for regression and parameter estimation;
  • Programming MatLab to test computational models.

To give you an understanding of the sort of technical knowledge you'll be aiming to have by the end of your training, we've included some documents below:

Read more about this course on the University of Sheffield's webpages for postgraduate students:

MSc Cognitive and Computational Neuroscience

After your degree

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, these courses are great preparation for a PhD in areas including neuroscience, artificial intelligence, and brain interfaces, or to begin clinical training.

The University's Careers Service runs workshops on CV and application writing, job hunting and preparing for interviews. They offer events where you can meet employers, and opportunities to get work experience while you study. The Careers Service will even continue to support you for three years after you graduate.

Graduate spotlight

John Brooke 300

John Brooke
Now: PhD student, Department of Psychology, University of Sheffield

Read John's story

Entry requirements

For this course, we usually ask for a 2:1 honours degree or equivalent qualification in either a life science (including psychology) or mathematical/physical science (including engineering). 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 'technical content'.

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.

Prospective international students: Your country

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.

Pre-Masters in Science and Engineering

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.

  • International English Language Testing Service (IELTS): Overall grade of 6.5 with 6.0 in each component

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.

English language requirements

The English Language Teaching Centre offers English language courses for students who are preparing to study at the University of Sheffield.

English Language Teaching Centre

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.

Funding your postgraduate course

UK/EU scholarships

100+ scholarships image

We're offering 100+ scholarships worth £10,000 each for home students starting a taught postgraduate course in 2019 that can be used towards fees or living expenses.

Find out more

International scholarships

International scholarships image

We're offering 100 International Postgraduate Taught Merit Scholarships, each worth 25% of the original tuition fee for a postgraduate taught programme starting in September 2019.

Find out more

Alumni rewards

Alumni rewards

If you’ve previously graduated from the University and intend to pursue further studies with us then you may be eligible to receive a 10% discount on your tuition fees.

Find out more

Up-to-date fees can be found on the University of Sheffield's webpages for postgraduate students:

Tuition fees

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:

  • Academic performance as indicated by GPA and transcript.
  • Other relevant skills and knowledge (for example, programming courses outside the degree, or relevant work experience).
  • Research activity (co-authoring papers, conference presentations, etc).
  • Personal statement, which should include information on why you want to do the course you have applied for, and how it fits with your aspirations.

To be considered for a bursary in the year that you intend to start your course, submit your application to study with us by 31 May. All applications received before this deadline will automatically be considered for a bursary.

Jake Toth

Jake studied BEng Mechatronic and Robotic Engineering at Sheffield before choosing to pursue the MSc Cognitive and Computational Neuroscience.

Due to my engineering background, I wanted a course in neuroscience that was approachable from an engineers standpoint. The Cognitive and Computational Neuroscience course has every aspect of neuroscience that I am interested, from the biological side to imaging or simulation.

jake toth - READ MORE

Laura Edmondson 500

Laura chose the MSc because it gave her the opportunity to learn about computational modelling methods alongside imaging techniques such as fMRI.

During my project I was able to apply the theoretical knowledge and programming techniques I learned in the modules to create a cortical model of touch processing. Presenting my MSc work at two scientific conferences was a great experience as I had the opportunity to network with other students and academics in my field.

Laura edmondson - READ MORE

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OUR RESEARCH

The University of Sheffield Neuroscience Institute

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We aim to deepen our understanding of function and dysfunction of the nervous system, through translational neuroscience, computational and systems neuroscience and sensory and developmental neuroscience to improve  the lives of patients and families affected by devastating disorders of the nervous system.

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Current modules

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.

Assessment: Examination

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.

Assessment: Examination

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.

Assessment: Coursework

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.

Pathway 1:

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.

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, 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.