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Computational Neuroscience MSc/PGDip

Learn about the course below
October 2020
1 year full-time
2 years part-time
£9,400 (UK) *
£14,500 (EU/INT) *
Course leader
Dr Tatiana Novoselova

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

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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

    • 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


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

  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.

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Code: PGI100

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