Computational Neuroscience and Neuroinformatics MSc
ObjectivesThe aim of the MSc in Computational Neuroscience is to prepare a breed of high quality interdisciplinary researches armed with the knowledge and skills to face up the scientific challenge of understanding the brain. To achieve this aim there are four main objectives: -To provide a broad interdisciplinary introduction to Neuroscience and Computational neuroscience, receiving this training first hand from well established researchers in the field. -To develop state of the art knowledge of the latest advanced data analysis and modelling methods used to understand brain functions at scales that span the single neuron to whole brain areas. -To provide extensive experience in devising, carrying out, and writing up research projects.
Entry requirementsEntry requirements: Applicants should hold (or be about to obtain) a first or upper second class honours degree, or the overseas equivalent, in a biological, medical or physical sciences subject.
Academic titleComputational Neuroscience and Neuroinformatics MSc
Course descriptionCourse description
Computational Neuroscience represents a new and exciting interdisciplinary field where ideas and methods from Physics, Engineering, Computer Science, Mathematics, and Biology are synergistically applied to understand brain function. The MSc in Computational Neuroscience at Manchester is designed to enable top students from numerate and biological sciences to gain the necessary tools and training to advance our knowledge about the brain.
Quantitative methods are becoming increasingly important at the cutting edge of research in biology, in general, and neuroscience, in particular. Complex experimental data sets demand new mathematical methods for their analysis. Mathematical and computer modelling are powerful tools for bridging levels of investigation from molecules, cells, and tissues, through to the whole brain.
You will be based in the top-rated faculty of Life Sciences at the University of Manchester, working as part of the Computational Neuroscience and Neuroinfomatics Group. The teaching staff is highly interdisciplinary with backgrounds spanning Neuroscience, Physics, Mathematics, Psychology, Biology, and Computer Science.
Module details
The MSc programme course is strongly based on hands-on research. Due to the considerable interdisciplinary research strengths that exist within the University of Manchester, we are able to offer an interdisciplinary programme with depth of expertise throughout Computational Neuroscience and Neuroinformatics. The course has three components:
Introduction to core concepts
Taught courses in Computational Neuroscience and Methods in Neuroinformatics will cover the fundamental biology of neural function, basic neuronal modelling and data analysis. Since the programme is designed for a wide interdisciplinary audience, there are optional courses to complement and strengthen the required skills: optional courses in computational techniques are available to strengthen the numerical skills of students from biological backgrounds, and optional courses in Neuroscience are available to students from computational backgrounds.
Development of advanced skills
Students will be exposed to research methods in computational neuroscience and Neuroinformatics by active participation in technical tutorials given by first rank researchers. Research skills will be further promoted and developed by practical work as part of the tutorials and mini-projects.
Research projects
Engaging in full-time research in active laboratories is the key part of this programme. Each student will undertake 2 full-time research projects, of 12 and 18 weeks duration. All projects will apply quantitative methods (data analysis or modelling) to stimulating Neuroscience research problems. The strength and breadth of research in Manchester allows us to offer students an exceptionally broad and exciting range of options. Most projects will involve co-supervision between a neuroscientist and a theoretician.
Course content for year 1
Section 1: Weeks 1-12
Induction, meeting with personal tutor and course team. Students take compulsory taught programme to introduce core concepts: "Computational Neuroscience" and "Methods in Neuroinformatics".
Students also take one optional course to acquire interdisciplinary skills: those from physical science background are encouraged to take a course in fundamental Neuroscience, those from a biological background are encouraged to take one in mathematics. Starting in week 7, students have the first opportunity to develop research skills with a 6-week "mini-project".
Section 2: Weeks 13-24
Development of advanced skills through full time research project, working closely with academic supervisor. First research project (12 weeks) to be submitted in April/May.
Section 3: Weeks 25-45
Further development of skills through second research project (18 weeks). To get a wide skill set, this project will normally be on a different topic and with a different supervisor to the first. The second project is to be submitted in September.