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2. Computational Neuroscience: modelling, data analysis and experiment
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We have used biophysical model (several to a few thousand compartment models)
or abstract model to explore the computational principles in single neuron and neuronal networks.
Some examples are
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3. Granger Causality Analysis, both theory and applications
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We have developed algorithms and apply them to various datasets including LFP, EEG, spike trains and fMRI etc.
Some interesting publications are
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4. Machine learning
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We are working on analyzing and developing various algorithms such as supervised learning,
unsupervised learning and community detection algorithms with applications. Some publications
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5. Mathematical physics**

We have learnt a lot from analysing some models such as Ising model, Hopfiled model and hydrodynamical limit
of interacting particle systems.
Some publications
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