Research

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)

Professor

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