Explore this course:
Cognitive and Computational Neuroscience
Department of Psychology,
Faculty of Science
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.
We accept medical students who wish to intercalate their studies. Find out more on the Medical School's website.
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:
- 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
You will choose two modules from one of our pathways.
You will train 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.
- Fundamentals of Cognition
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
- Modelling and Simulation of Natural Systems
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, you will explore and understand both modelling approaches. You will gain knowledge of the assumptions underlying these models, their limitations, and how they are derived. You will learn how to simulate and explore the dynamics of computational models, using a variety of examples mostly drawn from natural systems. You should be aware that there are limited places available on this course.15 credits
You 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.
- Neuroimaging 1
This module provides an overview of neuroimaging techniques and fundamental data analysis methodolo-gies employed, specifically those based around functional magnetic resonance imaging (fMRI). The two as-pects 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
- Neuroimaging 2
This module further develops on the foundational material in PSY6414 (Neuroimaging 1) and provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the functional imaging 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.15 credits
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 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.
An open day gives you the best opportunity to hear first-hand from our current students and staff about our courses. You'll find out what makes us special.
1 year full-time
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.
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.
I wanted a course in neuroscience that was approachable from an engineers standpoint
Masters student, MSc Cognitive and Computational Neuroscience
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.
Overall IELTS score of 6.5 with 6.0 in each component.
If you have any questions about entry requirements, please contact the department.
Fees and funding
You can apply for postgraduate study using our Postgraduate Online Application Form. It's a quick and easy process.
+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.