Course description
Programme highlights
Multi-disciplinary programme
Unique training scheme to students in two streams: physical sciences and life sciences
Excellent training for those wishing to pursue an academic or industrial career
You will participate in an interdisciplinary, new, and unique programme to study fundamental principles of information processing in the brain
You will develop confidence and competence in the theoretical and practical dimensions of modelling in neuroscience, through taught modules, practical work, exercises and project development
You will become experienced in brain-inspired algorithms and neuromorphic techniques of finding solution of complex problems
You will be taught by recognised experts with worldwide links to research organisations and projects
Further study, research and employment opportunities
The programme will provide an excellent training for those wishing to pursue an academic or industrial research career on the basis of new and exciting discoveries in brain research. In particular, it will provide a broad multidisciplinary foundation for students wishing to undertake further research training at PhD level.
The acquired computational knowledge, skills, and brain-inspired algorithms can be successfully used for a career at industrial research centres and in other business areas and organisations which require deep analytical skills and use of effective computational methods of information processing (finance, commerce, consultancy, medicine, etc). Also, this programme will be attractive for medical professionals wishing to continue their professional development.
General programme structure
The aim of this multidisciplinary programme is to foster a new generation of scientists who have been trained in both mathematical /computational skills and neuroscientific methodologies.
Neuroscience is one of the most intensively developing and important sciences of the 21st century. Huge progress has been made in experimental approaches and techniques. In particular the imaging and recording of brain activity is providing extensive experimental data about different aspects of brain functioning. Theoretical neuroscience provides the solid basis necessary to understand the data and shed fresh light on the basic mechanisms underpinning brain function at the cellular, circuit and systems levels. The programme is developed in the Centre for Theoretical and Computational Neuroscience (CTCN) at the University of Plymouth. The CTCN is one of the leading centres in the field of theoretical neuroscience. The Centre has bought together a range of international experts from various backgrounds with expertise in mathematical and computational techniques and their application in neuroscience. This rich mix of computational, mathematical and neuroscience expertise provides a unique opportunity for students to acquire multidisciplinary training.
Detailed programme structure
Core modules
-Foundations of neurobiology
-Foundations of theoretical neuroscience
-Functional neuroanatomy
-Neural computation
-Research skills
-The neurobiology and modelling of the sensory-motor system
-The neurobiology and modelling of vision
-The neurobiology and modelling of the auditory system
-Stochastic models and statistical methods in neuroscience
-Computational models of cognitive function
-Biophysical models of neural dynamins and cognition
-Research project
The training programme in theoretical and computational neuroscience offers a wide range of theoretical techiniques which are under intensive use in neuroscience. These methods include the development and study of mathematical and computational models of neural activity, brain structures, cognitive functions etc as well as probabilistic and statistical techniques for analysing different types of experimental neuroscience data. The core taught modules integrate the neurobiological, cognitive, mathematical, and computational knowledge and skills needed to theoretically investigate fundamental issues concerning brain function.
In addition to the taught modules, students will work individually with one or more research advisors to develop research projects and learn how to carry out advanced interdisciplinary research in their chosen research areas. In addition, students will have access to the CTCN "journal club" offering opportunities for individual and team presentations. Also, students participate in research seminars.
Learning and assessment
The programme offers a unique training scheme to students in two streams:
-Physical Sciences Stream
-Life Sciences Stream
Students in the maths stream will have a strong mathematical background and will acquire knowledge and understanding in fundamental principles of neurobiology and in theoretical and computational neuroscience. Applying maths skills in the new and exciting area of brain studies, students will acquire new career opportunitites and broaden their horizons. Students will apply mathematical and computational methods to modelling of neural activity, brain circuits and cognitive function.
Students in the biology stream with a strong, scientific background but without deep mathematical training will acquire knowledge and skills in theoretical methods and computational techniques for studying the brain. Students will deepen their understanding of neurobiology, study new ideas and methods of computational neuroscience, use specialised software to develop and analyse neuronal models and apply gained knowledge in neuroscience.
In recent years many new techniques, algorithms and tools have been developed in the field of computational neuroscience and these now provide experimental neuroscience with powerful support both in data manipulation and analysis, and in computational modelling and simulation. Mathematical and computational models provide valuable insights which both help to clarify the mechanisms of neurobiological function, and provide test beds for various hypotheses about information processing in living systems. Use of these models is also proving to be an invaluable aid in guiding the design of new experiments, on both humans and animals, thus contributing to the worthy and important aim of reducing the level of and need for animal experiments
