Computational neuroscience uses mathematical analysis and modeling to advance our understanding of information processing in the human brain. Statistical signal processing methods, including Fourier, wavelet and Kalman filter based optimal spectral tracking techniques, have been developed and applied to brain signals. Modeling studies supplement this work, using detailed biophysical and phenomenological models. Application areas include neuroimaging, clinical studies and control of voluntary movements. Computational immunology is the application of computational modeling and simulation to address issues within experimental immunology. Our work focuses on the development of agent-based simulations of immune system function and we collaborate extensively with experimental immunologists at York and further afield in the USA, and have developed simulations for autoimmune diseases and early formation of immune organ function. We make use of extensive statistical analysis of our simulations, in an effort to increase confidence in the usefulness of the results when interpreted into an immunological context.
Members: Jon Timmis, Andy Tyrrell, James Hilder, Nick Owens
The Robotic Sniffer Dog project is a project funded by DSTL which attempts to improve the accuracy and reliability of automatic chemical agent detection using a robotic platform. The project was initial funded due to the success of the Receptor Density Algorithm (RDA), an immune-inspired algorithm developed by Dr. Nick Owens as part of his PhD research project, at detecting anomalies within a Proton-Transfer Reaction Mass Spectrometry data-set in a competition organised by DSTL at the ICARIS 2009 Conference. The RDA is inspired by the signalling mechansims found within T-cells, and is developed from models of the immunological T-cell and the T-cell recport's ability to contribute to T-cell discrimination.
Members: David M. Halliday, Luis R. Peraza, Aziz Asghar
Modelling and simulation of neurones in the central nervous system provides a powerful tool to study how information is represented and processed in the human brain and central nervous system. A particular interest is large scale synaptic integration in single neurones, using models in which both the complex spatial and temporal dynamics of large scale synaptic integration in single neurones are accurately modelled. Results from this work have provided new insight into the functional role of correlated neuronal activity, suggesting an important role for weak temporal correlation amongst pre-synaptic inputs in determining the output firing times.
Members: Jon Timmis, Andy Tyrrell
Our project will build capacity in generic modelling tools and simulation techniques for complex systems, to support the modelling, analysis and prediction of complex systems, and to help design and validate complex systems. Drawing on our state-of-the-art expertise in many aspects of computer systems engineering, we will develop CoSMoS, a modelling and simulation process and infrastructure specifically designed to allow complex systems to be explored, analysed, and designed within a uniform framework.
ARTIST: A Network for Artificial Immune Systems
Members: Jon Timmis, Andy Tyrrell
The field of Artificial Immune Systems (AIS) is a new and exciting area of research, whose implications to the design and implementations of systems in the future are manifold. This is not limited to the obvious virus detection in computer systems, but could extend from fault-tolerant hardware design to machine learning. However, to allow this new area to develop and for the UK to continue to lead the world in such activities, a more structured approach is needed to co-ordinate and support researchers in this area. This network is designed to help bolster these researchers in the UK, stimulate and extend the community of AIS practitioners within the UK, and provide the necessary infrastructure and financial support for them to pursue further interactions between themselves and international collaborators in order to drive forward this area of research.
BIASPROFS: Artificial Immune Systems for Bioinformatics
Members: Jon Timmis
BIASPROFS is a joint computing and biology research project with the aim of producing novel algorithms inspired by immunology for predicting the functions of GPCRs (G-protein-coupled receptors), an important kind of protein which interacts with many medical drugs. We are developing computational models of immunological processes that will ultimately lead to the development of novel immune inspired algorithms for the classification of GPCRs. This is in collaboration with University of Kent and University of Oxford (Jenner Institute for Vaccine Research).
nEUro-IT.net Research Directory
Members: Michael Lones, Andy Tyrrell
The aim of nEUro-IT.net, the EU Neuro-IT Network of Excellence, is to build a critical mass of new interdisciplinary research excellence at the interface between neuroscience, information technology and engineering. As part of this initiative, we maintain a database of people, organisations, applications and funding opportunities within the nEUro-IT.net remit.