To understand mechanisms underlying neurophysiological phenomena observed in experiment over scales ranging from the molecular, single cellular, neuronal systems, to that of the whole brain, theoretical and computational neuroscience has experienced explosive growth over last twenty years. We have seen fast infusions of new ideas and rapid expansions of emerging research directions in its developments.


My research interests focus on mathematical modeling and scientific computation for scientific problems arising from neuroscience. In particular, I am interested in understanding of the relation between structures and functions of biological neuronal network dynamics, development of new efficient computational methods for modeling large-scale cortical networks, discovery of potential mechanisms underlying information processing in the brain, and investigation of new mathematical structures and tools to extract useful information from neurophysiological data measured in experiment. Below are some directions of my research work.


Modeling, Analysis and Simulation to Explain Experimental Observations


      Modeling and Simulation of Large-scale Cortical Network Dynamics


      Dendritic Integration of  Synaptic Inputs in Biological Neurons


      Compressed-Sensing Coding Mechanism for Sparse Signals in Sensory Systems


      Maximum Entropy Principle for Neuronal Network Dynamics


      Measuring Conductance of Neurons in Experiment



Develop New Methodology for Analyzing Data in Experiment


      Impact of Sampling Effects in Granger Causality Inference


      Coarse-grained Event Tree Analysis to Quantify Neuronal Dynamics


      Compressive-Sensing Image Processing Through Network Dynamics


      Network Structure Characterization Using Low-rank Decomposition


      Nonuniform Sampling Framework for Granger Causality Analysis


      Localized-random Sampling for Compressive-Sensing Image Processing


      Functional Connectivity Inference Using Time-delayed Mutual Information



Develop New Mathematical Structures and Theoretical Tools


      Characterization of Dynamical Stability of Neuronal Networks


      Structural and Functional Connectivities of Neuronal Networks


      Extension of Kinetic Theory to Incorporate Correlated Firing Events


      Characterization of Amalgamated Networks in Natural Systems


      Compressive-Sensing Reconstruction of Network Connectivity


      Network Synaptic Connectivity Reconstruction by Spike-triggered Regression


      Network Structure Shaped by Spike-timing Dependent Plasticity


      Balanced State in Heterogeneous Neuronal Networks



Design Fast Numerical Algorithms for Scientific Computing


      Library-based Numerical Algorithms for Hodgkin-Huxley Neuronal Networks



   Other Research Topics


      Renormalized Dispersive Relations in Wave Turbulence


      Phase Separation Dynamics of Polymer Systems



Douglas Zhou (周栋焯)


Institute of Natural Sciences and School of Mathematical Sciences

Shanghai Jiao Tong University, Shanghai, China



Office:    Pao Yue-Kong Library,Room 523

Tel:         86-21-54747359

Fax:        86-21-54747359

Email:    zdz{At}sjtu{dot}edu{dot}cn