MSc
2022 start September 

Cognitive and Computational Neuroscience

Department of Psychology, Faculty of Science

Train in computer simulation and mathematical modelling techniques, as well as experimental cognitive psychology and brain imaging, and develop an understanding of the biological foundations of natural and artificial intelligence.
Two students

Course description

This 12-month course will give you in-depth training in core aspects of contemporary neuroscience, from sensation and sensory processing, to understanding complex brain functions and artificial intelligence. You'll be prepared 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, including computational modelling, biomimetic robots and cutting-edge neuroimaging, and give you opportunities to apply these techniques.

Over three months you'll work on your research project in Cognitive Neuroscience with one of our world-leading experts in the Department of Psychology. 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 student-led conference. This 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. It's common for MSc research projects to form the basis of publications in peer-reviewed journals.

    Example research projects
    • 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

      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 you'll gain in-depth knowledge of the fundamentals of 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.

      Intercalation

      We accept medical students who wish to intercalate their studies. Find out more on the Medical School's website.

      Apply now

      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:

      MSc Cognitive Neuroscience and Human Neuroimaging

      MSc Systems Neuroscience

      Modules

      The modules listed below are examples from the last academic year. There may be some changes before you start your course. For the very latest module information, check with the department directly.

      Fundamentals of Neuroscience

      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
      Computational Neuroscience 1: Biologically Grounded Models

      This module starts with a primer on neuroscience and the role of computational neuroscience. The next part of the module covers abstract neuron models and introduce classic computational principles and learning rules related to neural networks. From there we move to more biologically grounded models and deal with single neuron models including leaky-integrate-and-fire and conductance-based neurons. Finally, we examine higher levels of description, in particular systems in context of reinforcement learning. While the emphasis throughout the module is on methodological issues, 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
      Computational Neuroscience 2: Theoretical Models

      The module builds on ideas developed in Computational Neuroscience 1 to explore networks of neurons, neural circuits and their dynamics, and models of complete brain systems. As in Computational Neuroscience 1, this is taught using both mechanistic (bottom-up) and theoretical (top-down) perspectives but, in this module, emphasis is placed on computational models of neuronal networks and systems. Additional topics will address learning and embodied (robotic) models.

      15 credits
      Mathematical Modelling and Research Skills

      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.

      15 credits
      Systems Neuroscience

      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 (e.g., the 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. 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

      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.

      75 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. We are no longer offering unrestricted module choice. If your course included unrestricted modules, your department will provide a list of modules from their own and other subject areas that you can choose from.

      Duration

      1 year full-time

      Teaching

      You’ll learn through hands-on laboratory sessions, problem-solving classes, lectures, seminars and individual projects.

      Assessment

      You’ll be assessed through formal examinations and coursework which may include essays, poster presentations and a dissertation.

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

      Entry requirements

      A 2:1 honours degree or equivalent qualification in either a life science (including psychology) or mathematical/physical science (including engineering). 

      We accept medical students who wish to intercalate their studies.

      Technical content

      Overall IELTS score of 6.5 with 6.0 in each component.

      Pathway programme for international students

      If you're an international student who does not meet the entry requirements for this course, you have the opportunity to apply for a pre-masters programme in Science and Engineering at the University of Sheffield International College. This course is designed to develop your English language and academic skills. Upon successful completion, you can progress to degree level study at the University of Sheffield.

      We also accept a range of other UK qualifications and other EU/international qualifications.

      If you have any questions about entry requirements, please contact the department.

      Fees and funding

      Department bursaries

      Each year we offer two bursaries to students on this course. If you're awarded a bursary you'll receive a £1,500 reduction in your tuition fees. These bursaries are awarded on a competitive basis, based on:

      • academic performance as indicated by a grade point average 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.

      Apply

      You can apply for postgraduate study using our Postgraduate Online Application Form. It's a quick and easy process.

      Apply now

      Contact

      psy-pg-admissions@sheffield.ac.uk
      +44 114 222 6533

      Any supervisors and research areas listed are indicative and may change before the start of the course.

      Our student protection plan

      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.

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