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Supervisor: Prof ZHOU, Changsong (Tel: (852) 3411-5089; email: - Accepted Student ID: 17214343

Neural systems in the brain are excellent information processing and functioning dynamical complex networks. Huge number of excitatory and inhibitory neurons coupled through dynamical synapses to form nonlinear complex networks where the firing of individual neurons is transmitted to activate or inhibit the other neurons for information coding, computation, memory, learning and other functions. Due to strong nonlinearity of individual neurons, the competition between excitation and inhibition and the interaction from complex network connectivity, biological neural networks are characterized by nontrivial emergent activities, such as irregular individual spikes, neuronal avalanches, and synchronized oscillations. Understanding complex dynamical neural activities and their functional role in information processing is a grand challenge not only for neuroscience, but also for physics of nonlinear complex systems.  

Neural network dynamics express information by generating various spatiotemporal patterns of spikes, which costs metabolic energy. The prominent features of neural dynamics may reflect a cost-efficiency trade-off to support efficient information processing with low energy cost. We are interest in studying the following prominent questions related to the dynamical mechanisms and the neurobiological conditions to realize and implement the cost-efficient dynamical modes in neural networks.

  1. How are the prominent dynamical modes related to each other? 
  2. Can a generic neural network account for these dynamical modes? When and how can they emerge altogether?
  3. What kind of dynamical states are cost-efficient for information processing?
  4. What are the neurobiological foundation and dynamical mechanisms for the cost-efficient dynamical modes?
  5. How are these dynamical modes actually used in information processing?

Research projects along these lines including computer simulations and statistical physics of neural network models, analysis and modelling of brain imaging data in normal and disease brain, and the analysis of functional EEG data from cognitive experiments.