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
What you'll study
You will study the following core modules:
|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.
You will choose one option from the following two modules:
|Data Programming||15 credits|
|Introduction to coding with MATLAB||15 credits|
You will also choose one of the following 4 options:
|Neural Networks||15 credits|
|Machine Learning||15 credits|
|Natural Computing||15 credits|
|Research Design and Analysis||15 credits|
Please note that due to staff research commitments not all of these modules may be available every year.
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.
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
How to apply
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
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 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.
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.
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.