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Friday 22 October 2021
02:00 PM - 04:00 PM
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This event forms part of the Centre for Computational Science and Mathematical Modelling's spotlight series.
Neuronal information processing is complex at different levels, from the microscopic pre- and post-synaptic cellular interactions to the macroscopic interactions between large populations of neurons in, for instance, sensory processing and motor response. Various functional and effective connectivity measures have been developed to characterise such functional integration in neural networks, from neurophysiological signals (such as EEG, MEG). In current literature, linear modelling, connectivity and causality analysis are still the dominant approaches. Nevertheless, recent studies indicate that linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this talk, we will present how nonlinear dynamic modelling, frequency-domain analysis and network analysis can be integrated to study complex neural interactions; and applied to characterise neurological disorders and human visual system.