Douglas Zhou (周栋焯) - 上海交通大学

个人简介

Education B.S. in Computational Mathematics, 1998 — 2002 Department of Mathematics, Peking University, Beijing, China Ph.D. in Computational Mathematics, 2002 — 2007 Department of Mathematics, Peking University, Beijing, China Advisors: Prof. Pingwen Zhang & Prof. Weinan E PhD thesis ( in Chinese ): Study of Phase Separation in Complex Fluids: Modeling and Numerical Simulation Academic Affiliation Professor, 2016.02 — present Institute of Natural Sciences & School of Mathematical Sciences, Shanghai Jiao Tong University Visiting Professor, 2016.02 — 2016.05 Courant Institute of Mathematical Sciences, New York University Distinguished Research Fellow, 2010.01 — 2016.01 Institute of Natural Sciences & Department of Mathematics, Shanghai Jiao Tong University Visiting Member, 2013.09 — 2013.12 Courant Institute of Mathematical Sciences, New York University Courant Instructor, 2007.09 — 2009.08 Courant Institute of Mathematical Sciences, New York University Research Scientist, 2007.02 — 2009.12 Courant Institute of Mathematical Sciences, New York University

研究领域

Mathematical modeling and scientific computing for scientific problems in physical and biological sciences In particular, I’m interested in understanding of the relation between structure and functions of biological neuronal networks, 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 data measured in experiment.

近期论文

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Mathematical Modeling and Analysis of Spatial Neuron Dynamics: Dendritic Integration and Beyond Songting Li, David W. McLaughlin, and Douglas Zhou Commun. Pure Appl. Math., 2021 Abs Bib HTML PDF Neurons compute by integrating spatiotemporal excitatory (E) and inhibitory (I) synaptic inputs received from the dendrites. The investigation of dendritic inte- gration is crucial for understanding neuronal information processing. Yet quan- titative rules of dendritic integration and their mathematical modeling remain to be fully elucidated. Here neuronal dendritic integration is investigated by using theoretical and computational approaches. Based on the passive cable theory, a PDE-based cable neuron model with spatially branched dendritic structure is introduced to describe the neuronal subthreshold membrane potential dynam- ics, and the analytical solutions in response to conductance-based synaptic in- puts are derived. Using the analytical solutions, a bilinear dendritic integration rule is identified, and it characterizes the change of somatic membrane poten- tial when receiving multiple spatiotemporal synaptic inputs from the dendrites. In addition, the PDE-based cable neuron model is reduced to an ODE-based point-neuron model with the feature of bilinear dendritic integration inherited, thus providing an efficient computational framework of neuronal simulation in- corporating certain important dendritic functions. The above results are further extended to active dendrites by numerical verification in realistic neuron sim- ulations. Our work provides a comprehensive and systematic theoretical and computational framework for the study of spatial neuron dynamics. Dendritic computations captured by an effective point neuron model Songting Li, Nan Liu, Xiaohui Zhang, David W. McLaughlin, Douglas Zhou, and David Cai Proc. Natl. Acad. Sci., 116(30), 15244–15252, 2019 Abs Bib HTML PDF Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework. Mechanisms underlying contrast-dependent orientation selectivity in mouse V1 Wei P. Dai, Douglas Zhou, David W. McLaughlin, and David Cai Proc. Natl. Acad. Sci., 115(45), 11619–11624, 2018 Abs Bib HTML PDF Recently, sophisticated optogenetic tools for mouse have enabled many detailed studies of the neuronal circuits of its primary visual cortex (V1), providing much more specific information than is available for cat or monkey. Among various other differences, they show a striking contrast dependency in orientation selectivity in mouse V1 rather than the well-known contrast invariance for cat and monkey. Constrained by the existing experiment data, we develop a comprehensive large-scale model of an effective input layer of mouse V1 that successfully reproduces the contrast-dependent phenomena and many other response properties. The model helps to probe different mechanisms based on excitation-inhibition balance that underlie both contrast dependencies and invariance, and it provides implications for future studies on these circuits. Causal and Structural Connectivity of Pulse-Coupled Nonlinear Networks Douglas Zhou, Yanyang Xiao, Yaoyu Zhang, Zhiqin Xu, and David Cai Phys. Rev. Lett., 111(5), 054102, 2013 Abs Bib HTML PDF We study the reconstruction of structural connectivity for a general class of pulse-coupled nonlinear networks and show that the reconstruction can be successfully achieved through linear Granger causality (GC) analysis. Using spike-triggered correlation of whitened signals, we obtain a quadratic relationship between GC and the network couplings, thus establishing a direct link between the causal connectivity and the structural connectivity within these networks. Our work may provide insight into the applicability of GC in the study of the function of general nonlinear networks. \textcopyright 2013 American Physical Society. Spatiotemporal dynamics of neuronal population response in the primary visual cortex Douglas Zhou, Aaditya V. Rangan, David W. McLaughlin, and David Cai Proc. Natl. Acad. Sci., 110(23), 9517–9522, 2013 Abs Bib HTML PDF One of the fundamental questions in system neuroscience is how the brain encodes external stimuli in the early sensory cortex. It has been found in experiments that even some simple sensory stimuli can activate large populations of neurons. It is believed that information can be encoded in the spatiotemporal profile of these collective neuronal responses. Here, we use a large-scale computational model of the primary visual cortex (V1) to study the population responses in V1 as observed in experiments in which monkeys performed visual detection tasks. We show that our model can capture very well spatiotemporal activities measured by voltage-sensitive-dye-based optical imaging in V1 of the awake state. In our model, the properties of horizontal long-range connections with NMDA conductance play an important role in the correlated population responses and have strong implications for spatiotemporal coding of neuronal populations. Our computational modeling approach allows us to reveal intrinsic cortical dynamics, separating them from those statistical effects arising from averaging procedures in experiment. For example, in experiments, it was shown that there was a spatially antagonistic center-surround structure in optimal weights in signal detection theory, which was believed to underlie the efficiency of population coding. However, our study shows that this feature is an artifact of data processing.