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Computational Neuroscience MSc/PGDip

Learn about the course below
Code
PGB141
Start
October 2022
Duration
1 year full-time
2 years part-time
Attendance
Full-time
Part-time
Fees
£9,400 (UK) *
£14,500 (EU/INT) *
Course leader
Dr Tatiana Novoselova

This course is no longer accepting applications for October 2021 entry. The next start date will be 2022.

With the rise of big data analytics in the healthcare sector, there is an increasing demand for scientists with expertise in data mining and interpretation that can be used to inform clinical decisions. This master's course is designed to provide you with these skills, enhancing your career prospects as a computational neuroscientist, working in healthcare sector or in the field of medical research.

Why study MSc/PGDip Computational Neuroscience at Middlesex University?

This degree will give you a theoretical and practical understanding of computational neuroscience. You'll gain an advanced knowledge of neurones and their organisation into functional circuits that process information and control behaviour.

You'll develop your skills in recording, securely storing, analysing and visualising neural data to aid diagnosis and to determine the best treatment options or to answer a research question. Additionally, you'll gain hands-on experience in the use of programming methods used in computational neuroscience.

This master's course is ideal to those who want to analyse big data which informs clinical decisions or to investigate medical research questions.

Course highlights

  • Gain a theoretical and practical understanding of the nervous system in health and disease
  • Have access to specialised clinical and research equipment used to assess the function of the brain and peripheral nerves
  • Learn from a dedicated team of academics with expertise in computer science, neuroscience and psychology, and clinical practitioners, who will use a wide range of active learning styles, including enquiry-based, laboratory-based and problem-based activities
  • Apply machine learning and visual analytics to neural data.

Find out more

Sign up now to receive more information about studying at Middlesex University London.

What will you study on MSc/PGDip Computational Neuroscience?

This is a multi-discipline master's programme, covering disciplines of computer science, neuroscience, psychology, and statistics.

Your studies will focus on the collection, analysis and visualisation of neural data. You'll acquire knowledge of how the brain works at cellular, network and system levels and build on your existing knowledge and skill base to gain key understandings that will be readily applicable for a career in computational neuroscience.

Modules

  • Modules

    • Neurobiology (15 credits) - Compulsory

      This module introduces the current theories of the anatomy, molecular and cellular mechanisms of the nervous system. You'll gain knowledge of the key concepts and the latest theories in neurobiology so that you can understand neurological conditions, critique the neuroscience literature and can model neurological systems.

    • Fundamentals of Neuropsychology (15 credits) - Compulsory

      This module will introduce advanced level study of topics in neuropsychology, with a particular focus on cognitive neuropsychology. The foundations of the approach will be outlined, followed by examination of neuropsychological case studies and related research in several areas of cognition, including memory, language processing, and visual and perceptual disorders. You'll be encouraged to develop a critical awareness of the controversies that exist within this field and how these link to controversies in neuroscience.

    • Experimental Design and Statistics (15 credits) - Compulsory

      This module gives you the tools required to design effective and efficient experiments and to test scientific hypotheses. You'll also develop the necessary skills for statistical analysis in a hypothesis testing context.

    • Neuroinformatics (30 credits) - Compulsory

      This module will give you the theoretical and algorithmic basis for understanding learning systems and the associated issues with the large datasets/data dimensionalities typical of neuroscience. You'll be introduced to algorithmic and statistical approaches for training learning systems from vectorial and sequential exemplar data, learning the process of representing training data within appropriate feature spaces for the purposes of classification and related regressions. Where classifiers have a relationship to statistical theory this is fully explored. For statistical representation, data mining and visualisation, you'll be instructed in the specialised use of relevant languages such as Python/R and Matlab.

    • Analysis and Parameter Extraction of Neural Data (15 credits) - Compulsory

      This module allows you to develop specialised knowledge of current theories and concepts that are employed in acquiring and analysing signals of neurological origin. Although the focus will be on research methodologies, you will also explore applications in clinical neurology and neurophysiology. Neuroimaging and analysis methods based on fMRI (functional Magnetic Resonance Imaging) will be further examined, along with quantitative methods associated with EEG (electroencephalography), MEG and EIT (electrical impedance tomography).

    • Computational Neural Modelling (15 credits) - Compulsory

      This module will develop your understanding of modelling neurons, synapses, and neural topologies in a computer. This includes the strengths and weaknesses of models, how to use these models to perform useful computations, and ideas about how to move from these models to functioning model brains.

    • Neuropathology (15 credits) - Optional

      This module reviews our current understanding of the epidemiology, aetiology, pathology, diagnostic investigations and treatment interventions of a range of neurological conditions that are of great public interest today. You'll be enabled to interpret clinical and neuropathological data for the purpose of either research or clinical diagnosis.

    • Peripheral Neurophysiology (15 credits) - Optional

      This module will give you a specialised body of current knowledge in the field of peripheral neurophysiology, exploring the concepts of equipment, recording parameters, patient diagnosis and appropriate investigation. You'll gain confidence in the interpretation of diagnostic information in peripheral neurophysiology, leading to theories of treatment strategies, and become familiar with specialised and advanced techniques and technologies.

    • Research Project (60 credits) - Compulsory

      This module will develop your skills in the planning and execution of an analytical study and in the critical evaluation of real research results, drawing on knowledge acquired from other modules. In addition, you'll also develop your communication skills in order to communicate your findings in written and oral form.

You can find more information about this course in the programme specification. Optional modules are not offered on every course. If we have insufficient numbers of students interested in an optional module, or there are staffing changes which affect the teaching, it may not be offered. If an optional module will not run, we will advise you after the module selection period when numbers are confirmed, or at the earliest time that the programme team make the decision not to run the module, and help you choose an alternative module.

How is the MSc/PGDip Computational Neuroscience taught?

You'll gain knowledge and understanding through:

  • Attending lectures
  • Participatory seminars
  • Small group discussions
  • Directed learning
  • Group and individual exercises
  • Laboratory sessions

Assessment

Your knowledge and understanding is assessed by seminar presentations, resource design, written assignments, unseen examinations and project work.

Changes for students in 2021

If you have travel restrictions to the UK due to coronavirus, this course can be started fully online with support to learn from your home country for the first term.

We are back on campus for the majority of teaching in Autumn 2021, as long as restrictions allow. Your timetable will be built around on campus sessions using our professional facilities, with online sessions for some activities where we know being virtual will add value. We’ll use technology to enhance all of your learning and give you access to online resources to use in your own time.

In case of any changes to government guidance, we‘ll be ready to move to teaching with more restrictions in place and continue to give you an excellent learning experience. In this scenario, on campus teaching should continue although more of your course will take place online.

The table below gives you an idea of what your learning will look like across a typical week. Some weeks might be different due to how we schedule classes and arrange on campus sessions.

This information may change slightly as we receive further guidance from the government. You’ll receive final arrangements for your teaching and a full course timetable before you start.

Scenario A: Without social distancing

Live in-person on campus learning

Contact hours per week, per level:

5 hours

Live online learning

Average hours per week, per level:

5 hours

Tutor set learning activities

Average hours per week, per level:

2 hours

Scenario B: With social distancing and/or with restrictions on travel to campus

Live in-person on campus learning

Contact hours per week, per level:

3 hours

Live online learning

Average hours per week, per level:

7 hours

Tutor set learning activities

Average hours per week, per level:

2 hours

All on campus activities are only during term 2. Outside of these hours, you’ll be expected to do independent study where you read, listen and reflect on other learning activities. This can include preparation for future classes. In a year, you’ll typically be expected to commit 1800 hours to your course across all styles of learning. If you are taking a placement, you might have some additional hours.

Read more about our scenarios for returning to campus and what they might mean for your teaching and learning experience, and how you’ll be able to access student support.

Future plans for teaching

We’re developing our plans for in-person on campus teaching following government advice to keep you safe. If more restrictions are put in place in the future, or there is another lockdown, we’ll deliver your learning and support fully online for a temporary period. We’ll make alternative arrangements for any required placements if they can’t go ahead as planned. We’ll always give you notice of any changes that we make.

Definitions of terms

  • Live in-person on campus learning – This will focus on active and experiential sessions that are both:
    • Led by your tutors including seminars, lab sessions and demonstrations We’ll schedule all of this for you
    • Student-led by you and other students, like small group work and presentations.
  • Live online learning – This will include lectures, tutorials and supervision sessions led by your tutor and timetabled by us. It also includes student-led group work that takes place online

  • Tutor set learning activities – This covers activities which will be set for you by your tutor, but which you will undertake in your own time. Examples of this include watching online materials, participating in an online discussion forum, completing a virtual laboratory or reading specific texts. You may be doing this by yourself of with your course mates depending on your course and assignments. Outside of these hours, you’ll also be expected to do further independent study where you’ll be expected to learn, prepare, revise and reflect in your own time.

Support

You’ll have a strong support network available to you to make sure you develop all the necessary academic skills you need to do well on your course.

Our support services will be delivered online and on campus and you’ll have access to a range of different resources so you can get the help you need, whether you’re studying at home or have the opportunity to come to campus.

You’ll have access to one to one and group sessions for personal learning and academic support from our library and IT teams, and our network of learning experts. Our teams will also be here to offer financial advice, and personal wellbeing, mental health and disability support.

More on teaching for your subject in 2021/22

Read our guide to what you can look forward to when you study your subject with us including more information about your teaching experience this autumn.

  1. Standard entry requirements
  2. International (inc. EU)
  3. How to apply
  1. UK
  2. EU/International
  3. Additional costs

How can the MSc/PGDip support your career?

Once you graduate, you'll be prepared for a career as a computational neuroscientist in the healthcare sector, medical research or academia.

You could also consider continuing your studies to PhD level.

Dr Tatiana Novoselova
Programme leader

Dr Tatiana Novoselova is a Lecturer in Neuroscience and combines a medical school background with scientific research that is focused on neurodegenerative disorders using molecular and cellular biology techniques together with a proteomics approach to identify potential therapeutic targets. Tatiana has recently developed a particular interest in how the diagnostics and management of neurological and neuropsychiatric conditions can be improved using computational neuroscience methods and artificial intelligence.

Professor Richard Bayford
Professor of Bio-modelling and Informatics

Professor Bayford has extensive teaching and research experiences in the area of physiological measurements. He has a wide research interest, including deep brain stimulation, neuroimaging and electroencephalography (EEG) analysis.

Professor Chris Huyck
Professor of Artificial Intelligence

Professor Huyck is a world expert in artificial intelligence. His main research area is neural processing, particularly cell assemblies, and Natural Language Processing, which is a sub-field of Artificial Intelligence.

Dr David Windridge
Associate Professor in Computer Science

Dr Windridge heads the University's Data Science activities. His research interests centre on the related fields of machine-learning, cognitive systems and computer vision.


We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.

Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.

Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.

At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.

Other courses

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Duration: 1 year full-time, 2 years part-time

Code: PGY000

Cognitive and Clinical Neuroscience MSc

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Duration: 1 year full-time, 2 years part-time

Code: PGC860

Data Science MSc

Start: October 2022

Duration: 1 year full-time, 2 years part-time

Code: PGI100

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