Our work within computational neuroscience uses mathematical analysis and modelling to advance our understanding of information processing in the human brain. Statistical Signal Processing methods have been developed for analysis of multivariate neural data. This includes time series data such as local field potentials (LFP), Electromyograms (EMG), Electroencephalograms (EEG), Magnetoencephalograms (MEG) as well as spike train data such as Multielectrode Array (MEA) data and single Motor Unit (MU) recordings. Analysis techniques based on multiple and partial correlation analyses have been developed and applied to these data types. A particular focus is the application of frequency domain measures (coherence, partial coherence, multiple coherence) to neurophysiological data. Techniques have also been developed to study time dependent features in neural data, this allows the study of dynamical aspects in neural recordings, and is particularly relevant to cognitive neuroscience. Time frequency methods include the use of Fourier and wavelet transforms and optimal spectral tracking techniques using Kalman filtering. A range of modelling studies is used to supplement the analysis techniques, these include the use of detailed biophysical compartmental models as well as phenomenological models, in particular Integrate and Fire (IF) and Izhikevich models. The combined approach of analysis and modelling underpins our research in computational neuroscience.
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.
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.