MSc Cognitive and Computational Neuroscience
Programme code: PSYT12
PhD with Integrated Studies: PSYT18 – find out more
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.
Course Director: Dr Stuart Wilson
If you would like to know anything else about this course, contact our Postgraduate Taught Administrator, Linsey Chaplin.
Telephone: +44 (0)114 222 6534
You can also visit us throughout the year:
|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:
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 an upper second class (2:1) degree or equivalent in either a life science, a physical science or mathematics. 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 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.
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. We usually ask for:
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.
|Fees and funding||
Up-to-date fees, funding and postgraduate loan opportunities can be found on the University of Sheffield's webpages for postgraduate students. These may include scholarships for home and international students and a 10% discount for University of Sheffield graduates.
Departmental Taught Postgraduate Bursaries
Each year we offer seven bursaries to students on one of our masters courses. 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 2017, submit your application by Monday 1 May 2017. All applications received before this deadline will be considered for a bursary.
|What our graduates say||
The modules listed below are examples from the current academic year. There may be some changes before you start your course.
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.
|PSY6305: Fundamentals of Cognitive Neuroscience (15 credits)||
Module leader: Professor Rod Nicolson
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.
|PSY6306: Fundamentals of Neuroscience (15 credits)||
Module leader: Professor Paul Overton
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.
|PSY6307: Computational Neuroscience 1: Biologically Grounded Models (15 credits)||
Module leader: Professor Kevin Gurney
This module starts by outlining some major methodological principles in computational neuroscience including the difference between approaches which are more bottom-up (biologically grounded) and those which are more top-down (algorithm-based). The rest of the module is devoted to the more bottom-up view and deals first with single neuron models including: leaky-integrate-and-fire, conductance-based, and reduced model neurons. It then goes on to deal with other structural levels of description - microcircuits, systems, and embodied or robotic models. While specific brain systems are used as a vehicle for exposition, the emphasis is always on methodological issues - how models can be built, tested and validated at each level.
|PSY6309: Mathematical Modelling and Research Skills||
Module leader: Dr Hannes Saal
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.
|PSY6308: Computational Neuroscience 2: Theoretical Models (15 credits)||
Module leader: Dr Stuart Wilson
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 idieas will be explored in the context of neuronal information processing.
|PSY6310: Brain Imaging and its Physical Foundations (15 credits)||
Module leader: Dr Liat Levita
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 in parallel 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. One complementary neuroimaging technique will also be introduced, specifically optical imaging spectroscopy, etc. 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 introduced.
|PSY6315: Current Issues in Systems Neuroscience (15 credits)||
Module leader: Dr Chris Martin
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.
|PSY6316: Current Issues in Cognitive Neuroscience (15 credits)||
Module leader: Dr Tom Stafford
This module is designed to enable students to explore areas of active contention in cognitive neuroscience. For example: what causes dyslexia? Do music and language utilise common systems? Where does the brain calculate reward? What role do different methodologies have in addressing these topics? The module includes a number of specific topics. Each topic is introduced by an expert in the field, but the bulk of the module is based around student-led collaborative investigation, debate and discussion in seminars. This format provide the opportunity for students to develop a position on some contemporary and unresolved issues in cognitive neuroscience, and encourages the development of the core scientific skills of team-work, communication, sceptical enquiry and critical appraisal of research findings.
|PSY6321: Research Project (60 credits)||
Module leader: Dr Stuart Wilson
The module allows students to work on a research project within computational neuroscience, cognitive neuroscience or nalysis of brain imaging data. Students design and conduct an empirical investigation motivated by a review of relevant literature on the topic. With the support of a member of the teaching staff they will design and carry out an empirical investigation, analyse and interpret the results, and write up the research using recognised journal frameworks.