Triangle

Course overview

This unique interdisciplinary course combines aspects of psychology, mathematics and computer science. It uses artificial intelligence to further the understanding of the brain.

You will learn how:

  • the brain is believed to work on the cellular, network and systems level
  • to develop mathematical models of brain function and use them in simulations
  • cognitive phenomena relate to brain activity
  • current AI algorithms are based on neuroscience findings
  • a range of experimental approaches are used to measure and analyse brain function.

There will be particular focus on how:

  • memories are stored and organised in the brain
  • networks of neurons perform computations
  • visual illusions find their origins in neural circuits.

Our research covers many aspects of computational study on the brain, from the changes at a single synapse through to the behaviour of large populations.

Why choose this course?

Top 10

in the UK for research power

Research Excellence Framework 2014

More than £1 million

annual research income

from research councils, the EU, Government, charities and companies.

Research project

in a wide range of fascinating topics

Interdisciplinary

combining aspects of psychology, mathematics and computer science

Gain experience

of applying a variety of mathematical modelling approaches

Modules

Machine Learning in Science – Part 1

This module will provide an introduction to the main concepts and methods of machine learning. It introduces the basics of supervised, unsupervised and reinforcement learning as applied to regression, classification, density estimation, data generation, clustering and optimal control. It will be taught via two sessions per week through a combination of fundamental concepts and hands-on applications.

Computational Cognitive Psychology

The aim of this module is to teach you cognitive psychology but also how cognition can be understood in computational terms, simulation and how it compares to AI approaches.

Content includes:

  • cognitive psychology
  • computational approaches
  • connectionist networks
  • deep nets for vision audition and language
  • memory networks
Neural Computation

The aim of this module is to teach you how neural processes can be understood in computational terms and how they can be analysed using mathematical and computational methods.

Topics included:

  • biophysical and reduced models of neurons
  • models of networks (eg Hopfield networks, ring-attractors and rate networks)
  • models of synaptic plasticity and memory
  • perceptrons
  • unsupervised learning
  • neural coding
  • visual system
  • model fitting
Research project

In a typical research project, you will either:

(a) develop an experimental design, prepare stimuli, and to run a study in a small group of subjects, with technical support provided depending on the complexity of the measurement methods, or

(b) evaluate an existing set of, for example, fMRI, MEG, EEG or TMS data and interpret the results

Practical Biomedical Modelling

This module involves the application of mathematical modelling to practical problems in biology and medicine, typical of those that mathematicians and systems biologists encounter in academia and industry.

Specific projects are tackled through workshops and group activities. You will gain experience in applying a variety of mathematical modelling approaches to a range of biomedical problems.

Examples include:

  • modelling cell signalling pathways using Ordinary Differential Equations
  • fitting models to data
  • modelling spatial patterning using Partial Differential Equations
  • modelling biological tissues using individual-based models
  • modelling tissue growth, including cancer.

The course will include training in report writing and giving presentations.

Optional modules

Computer Vision 20 credits

You will examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You will learn a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, image segmentation, pose estimation, recovery of three-dimensional shape and analysis of motion. These problems will be approached with both traditional and modern computer vision approaches, including deep learning.

Applied Nonlinear Dynamics

During this module you will enhance your understanding of nonlinear oscillations, including the linear stability of limit cycles (Floquet theory), the Mathieu equation, and relaxation oscillators (using geometric singular perturbation theory). Synchronisation by periodic forcing will be introduced using the notion of isochrons and phase-response curves, as well as Poincaré sections, circle-maps, mode-locking, and Arnold tongues.

You will explore the treatment of chaos covering tests for chaos (Liapunov exponents and spectral analysis), strange and chaotic attractors, fractal boundaries, and routes to chaos in nonlinear dynamical systems. 

You will also learn about spatially extended systems, covering pattern formation (in both PDE and integral equation models), and weakly nonlinear analysis (amplitude equations and pattern selection).

The Physics of Deep Learning

This module will teach you how to think about artificial neural networks and deep learning from the point of view of physic. It will apply concepts and methods of statistical mechanics to deep learning problems. It will show how a physical science perspective allows you to understand better the workings of neural networks to make them more efficient and expand their range of applications.

Advanced Methods in Psychology

The module provides an insight into some more advanced or specialised techniques of data collection, organisation and analysis in psychological research (eg eye-tracking, EEG, fMRI, TMS, computational modeling, diary methodologies and workshops). Lectures will include implementation of analytical procedures in, for example, specialised data management and statistical packages and on specialised data-gathering equipment and software.

Machine Learning in Science – Part 2

This module will cover more advanced topics following from Machine Learning in Science Part 1, specifically the concepts and methods of modern deep learning. Topics include deep neural networks, CNNs, RNNs, GANs, RBMs and deep RBMs, autoencoders, transfer learning, reinforcement learning and Markov decision processes, cleaning data and handling large data sets. The main project for the module is the self-driving PiCar, as seen in this video.

Professional Skills for Psychology

You will cover general research skills and personal development skills. The module includes a number of workshops including presentation and writing skills, careers, understanding the wider context of research, consultancy, and practical and ethical issues, along with appropriate Graduate School courses.

Research Internship 20 credits

This module enables students to obtain practical research experience, to include a range of activities such as literature searching, study design, ethics, obtaining participants, data collection and analysis, and writing reports. The actual content will depend on the individual Internship.

This module enables students to obtain practical research experience, to include a range of activities such as literature searching, study design, ethics, obtaining participants, data collection and analysis, and writing reports. The actual content will depend on the individual Internship.

Data Analysis for Neuroimaging

Experience a brain imaging session at our on-campus MRI centre. You will then analyse one of the data sets in further lab classes. 

You will be introduced to some of the standard tools used across many labs (including FSL, the FMRIB Software Library from Oxford).

Analytical Research Methods

A selection of workshops on advanced statistics for the neurosciences.

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Friday 15 October 2021.

Learning and assessment

How you will learn

  • Lectures
  • Seminars
  • Project work

Gain a hands-on experience in computational neuroscience research through a blend of traditional modules, individual and group projects.

Teaching is provided by academic staff within the relevant School.

You will be taught in classes of around 20 students.

How you will be assessed

  • Exams
  • Coursework
  • Project work

Your final degree result will be calculated from the taught module marks and the project mark. 

Contact time and study hours

The course is full-time and will require you to be present on most days of the week.

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2022 entry.

Undergraduate degree2:1 (or international equivalent); 2:2 (or international equivalent) may be considered provided the applicant has at least one year of relevant work experience or another supporting factor; for quantitatively minded students with a background in psychology, neuroscience, or biosciences as well as those with training in physics, engineering, mathematics, or computer science; no specific biology or computer knowledge required. In their final two years of study applicants must have achieved a 2.1 (60%) in 2 module(s) covering at least two of the following subjects: mathematics, statistics, physics, data analysis, computer science. If such skills were gained independently, evidence should be provided.
Work experience

This course requires mathematical and programming skills. You can download this test (pdf) to see if your knowledge is suitable for the course.

Applying

Our step-by-step guide covers everything you need to know about applying.

How to apply

Fees

All listed fees are per year of study.

Qualification MSc
Home / UK £11,050
International £26,500

Additional information for international students

If you are a student from the EU, EEA or Switzerland, you will pay international tuition fees in most cases. If you are resident in the UK and have 'settled' or 'pre-settled' status under the EU Settlement Scheme, you will be entitled to 'home' fee status.

Irish students will be charged tuition fees at the same rate as UK students. UK nationals living in the EU, EEA and Switzerland will also continue to be eligible for ‘home’ fee status at UK universities until 31 December 2027.

For further guidance, check our information for applicants from the EU.

These fees are for full-time study. If you are studying part-time, you will be charged a proportion of this fee each year (subject to inflation).

Additional costs

We do not anticipate any extra significant costs, alongside your tuition fees and living expenses. You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget. Personal laptops are not compulsory as we have computer labs that are open 24 hours a day but you may want to consider one if you wish to work at home.

Due to our commitment to sustainability, we don’t print lecture notes. It costs 4p to print one black and white page.

Funding

There are many ways to fund your postgraduate course, from scholarships to government loans.

We also offer a range of international masters scholarships for high-achieving international scholars who can put their Nottingham degree to great use in their careers.

Check our guide to find out more about funding your postgraduate degree.

Postgraduate funding

Careers

We offer individual careers support for all postgraduate students.

Expert staff can help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.

Each year 1,100 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

International students who complete an eligible degree programme in the UK on a student visa can apply to stay and work in the UK after their course under the Graduate immigration route. Eligible courses at the University of Nottingham include bachelors, masters and research degrees, and PGCE courses.

Graduate destinations

This course provides an ideal preparation for a PhD in computational neuroscience, psychology or artificial intelligence.

Those who take up a postgraduate research opportunity with us will receive support in terms of regular contact with supervisors and specific training. You will also benefit from dedicated advice from our Careers and Employability Service.

Other careers include:

  • biomedical modelling
  • artificial intelligence
  • data science
  • brain imaging.

Career progression

80.0% of undergraduates from the School of Psychology secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £32,000.*

* HESA Graduate Outcomes 2020. The Graduate Outcomes % is derived using The Guardian University Guide methodology. The average annual salary is based on graduates working full-time within the UK.

Two masters graduates proudly holding their certificates
" The study of the brain as a computational device has recently been revolutionised by advances in AI and neuroscience. I wish this programme existed when I studied! "
Mark van Rossum, programme director. Mark directed the UK's first Doctoral Training Centre, has written over 70 papers, and has supervised over 20 PhD students.

Related courses

This content was last updated on Friday 15 October 2021. Every effort has been made to ensure that this information is accurate, but changes are likely to occur given the interval between the date of publishing and course start date. It is therefore very important to check this website for any updates before you apply.