MSc in Computational Cognitive Neuroscience

  • Length
    1 Year Full Time
  • Department
    Computing, Psychology

Course overview

Understanding the relationship between brain, cognition and behaviour is one of the biggest challenges the scientific community is currently working on. Computational cognitive neuroscience is a young and exciting discipline that tackles these long-standing research questions by integrating computer modelling with experimental research.

This Masters programme will foster a new generation of scientists who will be trained in both neuro-computational modelling as well as cognitive neuroscience. Its core topics include:

  • Creating computational/mathematical models of neurons, circuits and cognitive functions
  • The fundamentals of cognitive neuroscience (brain mechanisms and structures underlying cognition and behaviour)
  • Advanced data analysis and neuroimaging techniques

The programme is suitable for students from a variety of disciplines including - but not limited to - psychology, computing, neuroscience, engineering, biology, maths and physics. Students with no prior programming experience are welcome.

Graduates of this Masters will acquire a unique set of complementary skills that will make them extremely competitive in securing research or analyst positions in both academia and industry.

Why study this course?

  • This cutting-edge programme is at the forefront of a new, rapidly emerging field of research.
  • It is multidisciplinary, conveying the theory and practice of computational and cognitive neurosciences.
  • Graduates of this programme will gain a competitive edge in the job market over graduates of other, standard programmes in related fields.

Contact the department

If you have specific questions about the degree, contact Max Garagnani or Maria Herrojo-Ruiz

What you'll study

You will study the following core modules:

Module title Credits
  Foundations of Neuroscience 15 credits
  Statistical Methods 15 credits
  Cortical Modelling 15 credits
  Cognitive Neuroscience 15 credits
  Modelling Cognitive Processes and Higher Order Brain Functions 15 credits
  Advanced Quantitative Methods 15 credits

You will also undertake a 60 credit research project investigating an aspect of cognitive neuroscience using computational modelling, advanced data analysis methods, or a combination of these techniques. Culminating in a 10,000 word dissertation, the project will be carried out by combining the computational, experimental and data analysis skills that students will acquire over Term 1 and 2.

Option modules

You will choose one option from the following two modules:

Module title Credits
  Data Programming 15 credits
  Introduction to coding with MATLAB 15 credits

You will also choose one of the following 4 options:

Module title Credits
  Neural Networks 15 credits
  Machine Learning 15 credits
  Natural Computing 15 credits
  Research Design and Analysis 15 credits

Download the programme specification for the 2018-19 intake. If you would like an earlier version of the programme specification, please contact the Quality Office.

Please note that due to staff research commitments not all of these modules may be available every year.

Entry requirements

First or upper second-class honours degree (or equivalent undergraduate degree) in a relevant discipline. Applicants might also be considered if they aren’t a graduate or their degree is in an unrelated field, but have relevant experience and can demonstrate the ability to work at postgraduate level.

A-levels in Science, Computer Science or Mathematics

Applications will be reviewed on a case-by-case basis. Depending on previous background and experience, applicants may be required to take one or more pre-sessional courses (for example in programming, statistics, or maths) prior to the start of the programme. These courses will be free to MSc offer holders.

International qualifications

We accept a wide range of international qualifications. Find out more about the qualifications we accept from around the world.

If English isn’t your first language, you will need an IELTS score of 6.5 with a 6.5 in writing and no element lower than 6.0 to study this programme. If you need assistance with your English language, we offer a range of courses that can help prepare you for postgraduate-level study.

Fees, funding & scholarships

Find out more about tuition fees.

Find out more about funding opportunities for home/EU applicants, or funding for international applicants. If you're applying for funding, you may be subject to an application deadline.

How to apply


Graduates of this programme will have the following assets in their portfolio:

  • A sound understanding of brain mechanisms and structures underlying cognition and behaviour
  • Knowledge or experience of experimental cognitive neuroscience methods
  • Skills in statistical data analysis
  • Knowledge of theory and practice of biologically constrained neural models of human brain function
  • Computer programming skills.

Such a cross-disciplinary profile will make graduates of this Masters particularly competitive on the job market, especially when applying for positions that require complementary expertise and skills.

The course prepares students for employment in areas including cognitive neuroscience,  IT consultancy, cognitive robotics, as well as large enterprises developing software systems inspired by human cognition (e.g., web-search engines, systems for natural language processing, information extraction, data mining and human-computer interaction).

The course is also ideal preparation for further study at PhD level.


Programme leaders

Dr Maria Herrojo-Ruiz

Maria’s research focuses on the processes and brain mechanisms mediating learning and monitoring of sensorimotor sequences, both in healthy human subjects and in patients with movement disorders.  She uses electroencephalography (EEG), magnetoencephalography (MEG) and intra-craneal recordings to investigate the brain activity along cortico-basal ganglia-thalamocortical circuits.

Dr Max Garagnani

Max’s research lies at the intersection of computational and cognitive neuroscience. He focuses on the implementation of biologically-realistic neural network models closely mimicking the structure, connectivity, and physiology of the human cortex. These models are applied to simulate and explain the cortical mechanisms underlying the spontaneous emergence of cognitive function - especially, language, but also, memory, attention, and “free” decisions.


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