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Department

Computing
Psychology

Length

1 year full-time or 2 years part-time

Course overview

Understanding the relationship between brain, cognition and behaviour is one of the biggest challenges for the scientific community. This established Masters course, integrating computer modelling with experimental research, equips students with a solid theoretical basis and practical experience of advanced data analysis and experimental techniques in computational and cognitive neuroscience.

Computational cognitive neuroscience is a young and exciting discipline that attempts to understand how the human brain works by integrating computer modelling with experimental cognitive neuroscience research.

Building on the multi-disciplinary and strong research profiles of academics from our departments of Computing and Psychology, this degree will develop a new generation of scientists, trained in both neuro-computational modelling and cognitive neuroscience.

Core topics you’ll study 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

We welcome applicants from a variety of disciplines including psychology, computing, neuroscience, engineering, biology, maths and physics. There is no need to have prior experience in programming to apply.

Why study MSc Computational Cognitive Neuroscience

This Masters will help you develop a unique set of complementary skills, making you extremely competitive in securing research or data analyst positions in both academia and industry:

  • This cutting-edge programme is at the forefront of a new, rapidly emerging field 
  • It is highly multidisciplinary, covering the theory and practice of computational and cognitive neurosciences; areas of application range from machine learning to brain-computer interfaces, to research in cognitive and clinical neuroscience
  • We have strong links with industry. You can decide to carry out your final research project in collaboration with one of our industry partners and collaborators, paving the way for employment and post-Masters internship opportunities (see Careers information below)

For examples of student work and alumni stories, visit the Computational Cognitive Neuroscience programme's blog.

Contact the department

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

What you'll study

Compulsory modules

You will study the following compulsory modules.

Module title Credits
Foundations of Neuroscience

Foundations of Neuroscience

15 credits

This module covers brain anatomy and functions and modern experimental techniques to study the neural basis of behaviour.

Tutors: Professor Joydeep Bhattacharya and Dr Gianna Cocchini

 15 credits
Statistical Methods

Statistical Methods

15 credits

This module covers the primary statistical analyses used in psychology including:

  • multivariate data
  • screening and cleaning
  • power and sample size determination
  • factor analysis
  • multiple regression
  • analysing contrasts
  • univariate and multivariate repeated measures
  • ANCOVA and MANCOVA.
 15 credits
Cortical Modelling

Cortical Modelling

15 credits

This module covers the theory and practice of computational neuroscience, including computational models of neurons, synapses, simple cortical circuits and networks. Students will learn how to implement simple models of biologically-realistic neural systems.

 15 credits
Cognitive Neuroscience

Cognitive Neuroscience

15 credits

This module aims to explore the neural basis of cognitive functions such as attention, motor control, personality and social cognition. The lectures will cover the basic principles and methods that are used to study the links between brain and behaviour.

In the first week the methods used in Cognitive Neuroscience will be introduced. The lectures will then explore nine topic areas in more detail, guided by three main questions:

  1. Which areas in the brain are important for the behaviour studied?
  2. When are these areas activated?
  3. How are these brain areas causally involved?

Every 2-hour lecture will include approximately 90 minutes of teaching, followed by discussion of a target paper or chapter. For every lecture, a small group of students will read the target paper or book chapter, prepare a short presentation and discussion points, and lead the discussion.



 15 credits
Modelling Cognitive Functions

Modelling Cognitive Functions

15 credits

This module covers the fundamental principles of current computational models of cognitive functions and their emergence. This includes perception and attention, working and long-term memory, decision making, and language.

Tutor: Dr Max Garagnani

 

 

 15 credits
Advanced Quantitative Methods

Advanced Quantitative Methods

15 credits

This module builds on the introductory level of statistics and probability theory and aims to provide students with theory and practice in the application of advanced quantitative methods across multiple areas of psychology and neuroscience.

Topics include: using R and Matlab in psychological research, logistic regression, resampling and Monte Carlo methods, psychological model fitting, Bayesian inference, structural equation modelling, time series analyses, open science tools.

 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 Terms 1 and 2.

Optional modules

You'll then have the option to take one, or both of the following optional modules. If you choose just one of these modules, then you can also choose an additional option from a list which is published on an annual basis.

Module title Credits
Introduction to coding with MATLAB

Introduction to coding with MATLAB

15 credits

This module aims to provide students with a comprehensive introduction to MATLAB, a widely-used software package for data analysis. The module will start in the Autumn term of 2018 and will be suitable for third year undergraduate students in psychology as well as for students in other programmes.

Each 1 hour lecture will introduce the topic, associated functions, and theory and will be followed by a 2 hour lab session with hands-on training and exercises using MATLAB. Weekly homework will help to further consolidate the material.

Tutors: Devin B Terhune and Maria Herrojo Ruiz

 15 credits
Data Programming

Data Programming

15 credits

This module aims to provide you with the programming skills you will need to carry out for programming tasks you will encounter in the other modules in this programme. You'll learn about general programming techniques such as variables, functions and control flow. You'll explore how to work with different types of data structures such as arrays and dictionaries.

You will develop data processing pipelines, which allow you to convert raw data into data that you can analyse. You will apply mathematical and statistical procedures to data. You'll learn how to plot graphs of various types. You'll also familiarise yourself with an industry-standard data science programming environment which you can use throughout the programme.

The following is an indicative list of topics:

  1. Data structures
  2. Reading and writing data on the filesystem
  3. Retrieving data from the web
  4. Retrieving data from databases using query languages
  5. Cleaning and restructuring data, part 1
  6. Cleaning and restructuring data, part 2
  7. Data plotting
  8. Version control systems
  9. Unit tests
  10. Data processing pipelines
 15 credits

Download the programme specification. 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.

For 2021-22 and 2020–21, we have made some changes to how the teaching and assessment of certain programmes are delivered. To check what changes affect this programme, please visit the programme changes page.

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 (or equivalent English language qualification) 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

Annual tuition fees

These are the fees for students starting their programme in the 2022/2023 academic year.

  • Home - full-time: £11200
  • Home - part-time: £5600
  • International - full-time: £16600

If your fees are not listed here, please check our postgraduate fees guidance or contact the Fees Office, who can also advise you about how to pay your fees.

It’s not currently possible for international students to study part-time under a student visa. If you think you might be eligible to study part-time while being on another visa type, please contact our Admissions Team for more information.

If you are looking to pay your fees please see our guide to making a payment.

Additional costs

In addition to your tuition fees, you'll be responsible for any additional costs associated with your course, such as buying stationery and paying for photocopying. You can find out more about what you need to budget for on our study costs page.

There may also be specific additional costs associated with your programme. This can include things like paying for field trips or specialist materials for your assignments. Please check the programme specification for more information.

Funding opportunities

Find out more about postgraduate fees and explore funding opportunities. If you're applying for funding, you may be subject to an application deadline.

How to apply

Apply now

Find out more about applying.

Careers

Links with industry

You'll benefit from ongoing collaborative links with several companies having headquarters in UK, USA, Italy,  Poland, Japan and Spain; currently, our industry partners and collaborators include representatives from Sony CSL Japan, BrainControl, Selvita, Square Enix, Anywyse and Tecnalia (note that this list may change and that the implementation of a joint project with an industry partner is subject to their availability).

Carrying out your final research project with one of our collaborators in industry will enable you to gain cutting-edge skills much in demand in today's competitive job market,  providing a ‘fast track’ route towards post-Masters internships  and employment.

Take a look at previous examples of research projects, career paths followed by our graduates, and feedback from our alumni on the MSc Computational Cognitive Neuroscience blog.

Skills

Graduates of this programme will gain the following skills and knowledge:

  • A sound understanding of brain mechanisms and structures underlying cognition and behaviour
  • Understanding of how memory, attention and decision-making work
  • 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
  • Research, analytical, communication and software skills 

Careers

While studying your programme you will have access to the Goldsmiths Careers Service, who can give you tailored advice according to your own skills and interests. You can also seek advice from the tutors on your course.

When researching career opportunities, you may find it useful to visit the websites of professional bodies such as the Federation of European Neuroscience Societies, National Bernstein Network Computational Neuroscience, Cognitive Neuroscience Society Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) and the Organisation for Computational Neurosciences.

Computational cognitive neuroscience graduates have a range of career options including:

  • Academic – take part in teaching and academic research in the field
  • Artificial intelligence – in a range of roles such as machine learning engineer or data scientist
  • Clinical engineer – designing, developing and maintaining equipment for diagnosing illnesses and treating patients
  • Communications – developing communications technologies as a communications engineer
  • Data analyst – providing insight and analysis of data in a range of sectors
  • Linguistics analysis – in areas such as speech recognition, text-to-speech synthesis, natural language processing, user research and computer-mediated language learning 
  • Programming, Systems Analysis and Software – in areas such as games design, Human-Computer Interface (HCI) design and Software design and development
  • Scientific research – in a variety of fields such as health and pharmaceutical research, and neural network applications
  • Various roles in the IT sector

You may also choose to extend and deepen your academic study by undertaking a PhD in computational cognitive neuroscience or a related field.

Staff

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.

 

Student work

Take a look at more examples of student projects, course news, and comments from our Alumni on the Computational Cognitive Neuroscience department page.

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