于玉国 (Yu, Yuguo)

发布者:孙毅 发布时间:2021-03-01 浏览次数:1488

于玉国  教授  博士生导师


  • 计算神经科学实验室

  • 智能复杂体系基础理论与关键技术实验室(2020-)

  • 复旦大学生命科学学院(2012-2020)

  • 复旦大学医学神经生物学国家重点实验室(2013-)

  • 上海市东方学者特聘教授(2013)跟踪计划(2017)

复旦大学智能复杂体系基础理论与关键技术实验室、类脑智能科学与技术研究院, 教授,南京大学凝聚态物理学博士(2001),美国卡耐基梅隆大学计算神经科学博士后(2004),耶鲁大学医学院研究科学家(2010),上海高校特聘教授‘东方学者’(2013)和跟踪计划(2017)。上海生物物理学会理事,中国计算神经科学学会委员、自动化控制和生物工程协会委员,Frontiers in Computational Neuroscience副主编, Cognitive Neurodynamics编委。研究方向包括大脑神经信号动力学特性和生物物理机制、神经信息处理和神经计算、神经元和神经网络计算模型,仿生智能等。近年来构建了哺乳动物和人脑能量连接图谱三维数学模型,揭示脑皮层普适性信号传导和基础代谢依赖的大脑耗能规律和机制。从离子通道层次到神经元、神经网络和神经环路等多个尺度,研究了高等动物大脑如何实现低功耗神经网络线路连接、和高效节能处理信息和神经计算机制。在Nature, PNAS, Neuron, Physical Review Letters, J Neurosci, PLoS Comp Biol, J Cereb Blood Flow & Metabolism等学术期刊发表SCI论文60余篇,主持/承担国家基金项目10余项。

教育经历

  • 兰州大学 物理学 学士 (1995)

  • 南京大学凝聚态物理博士 (2001)

  • 卡耐基梅隆大学 计算神经科学 博士后 (2004)

  • 耶鲁大学神经生物学研究科学家(2011)

研究领域

类脑智能和计算神经科学

  • 神经计算模型

  • 神经编码理论

  • 网络拓扑分析

  • 感知融合机制

  • 脑联接网络图谱

  • 自组织学习算法

  • 多感知融合模型

  • 人脑低功耗机制

代表成果

  1. 课题组通过构建构建人眼视觉搜索时空概率模型揭示人眼目标搜索过程规律,遵循低能耗和高效率贝叶斯信息最大化的均衡原则
    Zhou Y and Yu Y, (2021)
    Human Visual Search Follows Suboptimal Bayesian Strategy Revealed by a Spatiotemporal Computational Model and Experiment.
    Communications Biology, 4:34.

  2. 同纽约州立大学石溪分校葛少宇教授团队合作,构建了海马齿状回DG区能量计算模型,揭示新颖环境海马产生更多新生神经元的抑制神经活动调控网络能量机制
    Wang X, Liu H, Morstein J, Novak AJE, Trauner DH, *Xiong Q, *Yu Y, and *Ge S, (2020)
    Metabolic tuning of inhibition regulates hippocampal neurogenesis in the adult brain.
    Proc Natl Acad Sci (PNAS), 117(41):25818-25829.

  3. 通过认知脑电实验研究揭示人脑在自然环境比在城市建筑环境具有更强alpha-theta振荡活动和网络联通性能
    Chen Z, He Y, *Yu Y,(2020)
    Attention restoration during environmental exposure via alpha-theta oscillations and synchronization,
    Journal of Environmental Psychology, 68, 101406.

  4. 课题组通过电生理和计算模型综合交叉研究揭示哺乳动物脑皮层神经元和网络在生理温度具有最佳的信息编码和传输效率
    Fu X and *Yu Y. (2019)
    Reliable and efficient processing of sensory information at body temperature by rodent cortical neurons.
    Nonlinear Dynamics, 98(1):215-31.

  5. 课题组和耶鲁大学核磁共振成像中心合作,构建人脑能量模型揭示皮层所有脑区能量分布图谱
    *Yu Y, Herman P, Rothman DL, Agarwal D, & Hyder F. (2019).
    Human brain gray matter energy map computed on the basis of cellular staining from BigBrain.
    Journal of Cerebral Blood Flow & Metabolism, 39, 205.

  6. 课题组通过电生理和计算模型综合交叉研究揭示哺乳动物脑皮层神经元轴突纳钾离子通道电导密度决定了椎体神经元和中间神经元的放电特性
    Zhang W, Fan, B, Agarwal D, Li T, & *Yu Y. (2019).
    Axonal sodium and potassium conductance density determines spiking dynamical properties of regular-and fast-spiking neurons.
    Nonlinear Dynamics, 95(2), 1035-1052.

  7. 构建神经元-星形胶质细胞网络计算模型,揭示胶质细胞调控皮层神经放电动力学的重要性,以及产生自发癫痫的胶质细胞Kir4.1通道和缝隙链接通道的机制
    Du MM, *Yu Y and *Wu Y, (2018)
    Astrocytic Kir4.1 channels and gap junctions account for spontaneous epileptic seizure,
    PLoS Comp. Biol. 14(3): e1005877

  8. 构建了大鼠和人脑皮层能量预算计算模型,揭示了哺乳动物大脑能量分配的普适性规律
    *Yu Y, Herman P,  Rothman DL, Agarwal D and Hyder F, (2018)
    Evaluating the gray and white matter energy budgets of human brain function,
    Journal of Cerebral Blood Flow & Metabolism, 38(8):1339-1353.

  9. 和中山医院钟春玖教授合作,构建艾兹海默症血液维生素代谢物的生物标志物数学模型
    Pan XL. et al, ... *Yu Y and *Zhong CJ. (*Co-PI) (2016).
    Measurement of Blood Thiamine Metabolites for Alzheimer’s disease Diagnosis,
    EBioMedicine, 26(3):155-162.

  10. 构建嗅球大尺度神经网络模型,解析生神经网络通过突触可塑性调控兴奋和抑制突触链接达到最佳比例实现了稀疏编码和侧向抑制功能
    *Yu Y, Migliore M, Hines ML, and Shepherd GM (2014)
    Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition.
    Journal of Neuroscience 34: 13701-13713.

  11. 构建嗅球大尺度神经网络模型,研究了嗅球网络通过突触可塑性自组织演化出稀疏放电机制
    *Yu Y, McTavish TS, Valenti C, Hines ML, Shepherd GM and Migliore M. (2013)
    Sparse distributed representation of odors in a large-scale olfactory bulb circuit.   
    PLoS Comp. Biol., 9(3): e1003014.

  12. 构建哺乳动物皮层神经元计算模型,揭示了生理温度条件使高等动物皮层神经元实现动作电位放电的高效节能生物物理机制
    Yu Y, Hill A. and McCormick DA. (2012)
    Warm body temperature facilitates energy efficient cortical action potentials.
    PLoS Comp. Biol., 8(4) e1002456.

  13. 率先发现皮层神经元轴突存在大量P/Q和N型钙离子通道亚型的实验证据
    Yu Y, Maureira C and McCormick DA (2010)
    P/Q and N channels control baseline and spike-triggered calcium levels in neocortical axons and synaptic boutons,
    Journal of Neuroscience, 30: 118580-11869

  14. 通过实验和皮层神经元Hodgkin-Huxley三维计算模型相互验证,进一步证实作电位传输过程产生的轴向电流可导致动作电位快速上升相和阈值离散现象。得到国际同行普遍认可
    Yu Y, Shu Y, Duque A, Haider B and McCormick DA (2008)
    Cortical Action Potential Back-propagation Explains Spike Threshold Variability and Rapid-Onset Kinetics.
    Journal of Neuroscience, 28: 7260-7272. PMCID: 2664555

  15. 构建皮层神经元Hodgkin-Huxley三维轴突和树突计算模型,在国际上首先提出动作电位传输过程产生轴向电流导致动作电位快速上升相和阈值离散的机制。在专业顶级期刊NATURE发表
    McCormick DA, Shu Y and Yu Y (2007)
    Hodgkin and Huxley model—still standing?
    Nature. 445: E1-E2.

  16. 首次揭示皮层神经元对自然界信号的高度适应性,来自于皮层神经元对自然界信号1/f统计特性的适应性
    *Yu Y, Richard RD and Lee TS (2005)
    Preference of sensory neural coding for 1/f signals.
    Phys. Rev. Lett., 94, 1081031-4

  17. 通过构建计算模型揭示视觉神经元对输入信号对比度适应的信息最大化原则和调控机制
    *Yu Y, Richard RD and Lee TS. (2005)
    Adaptive contrast gain control and information maximization.
    Neurocomputing, 65-66 111-116

  18. 通过计算模型和数学分析揭示神经元响应曲线的阈值和饱和值在编码信号过程的调制机制
    *Yu Y and Lee TS. (2003)
    Adaptation of the transfer function of the Hodgkin-Huxley (HH) neuronal model.
    Neurocomputing 52-54, 441-446

全部SCI学术论文

  1. Zhou Y and *Yu Y, (2021)
    Human Visual Search Follows Suboptimal Bayesian Strategy Revealed by a Spatiotemporal Computational Model and Experiment.
    Communications Biology, 4,34.

  2. Lu Y, Qi X, Zhao Q, et al, *Yu Y and *Zhou C (2020)
    Effects of exercise programs on neuroelectric dynamics in drug addiction.
    Cognitive Neurodynamics, 1-16.

  3. Wang X, Liu H, Morstein J, Novak AJE, Trauner DH, *Xiong Q, *Yu Y, and *Ge S, (2020)
    Metabolic tuning of inhibition regulates hippocampal neurogenesis in the adult brain,
    Proc Natl Acad Sci (PNAS), 117(41):25818-25829.

  4. Lei Y, Song B, Chen L, et al. Yu Y and *Gu Y.(2020)
    Reconfigured functional network dynamics in adult moyamoya disease: a resting-state fMRI study.
    Brain Imaging and Behavior. 14:715–727

  5. Chen Z, He Y, *Yu Y,(2020)
    Attention restoration during environmental exposure via alpha-theta oscillations and synchronization.
    Journal of Environmental Psychology, 68, 101406.

  6. Yang L, Jin Y, Wang X, Yu B, Chen R, Zhang C, Yu Y & Wei, D. (2020)
    Antifouling Field‐Effect Transistor Sensing Interface Based on Covalent Organic Frameworks.
    Advanced Electronic Materials, 6(5), 1901169.

  7. Fu X and *Yu Y. (2019)
    Reliable and efficient processing of sensory information at body temperature by rodent cortical neurons.
    Nonlinear Dynamics, 98(1):215-31.

  8. YZ Li, GX Zheng, XY Qi, *Yu Y. (2019)
    Critical Dynamics Characteristics of Human Brain EEG Activity During Music-listening State and Resting State,
    Journal of Fudan University 05-0586-10.

  9. Yu B, Jin Y, Shen Y, Yang Y, Wang G, Zhu H, *Yu Y & *Wang J. (2019)
    Loss of homeoprotein Msx1 and Msx2 leading to athletic and kinematic impairment related to the increasing neural excitability of neurons in aberrant neocortex in mice.
    Biochemical and Biophysical Research Communications. 516 (1), 229-235.

  10. *Yu Y, Herman P, Rothman DL, Agarwal D, & Hyder F. (2019).
    Human brain gray matter energy map computed on the basis of cellular staining from BigBrain.
    Journal of Cerebral Blood Flow & Metabolism, 39, 205.  

  11. Qu G, Fan B, Fu X, & *Yu Y. (2019)
    The impact of frequency scale on the response sensitivity and reliability of cortical neurons to 1/fβ input signals.
    Frontiers in cellular neuroscience, 13, 311.

  12. Zhang W, Fan, B, Agarwal D, Li T, & *Yu Y. (2019)
    Axonal sodium and potassium conductance density determines spiking dynamical properties of regular-and fast-spiking neurons.
    Nonlinear Dynamics, 95(2), 1035-1052.

  13. Yu L, Shen Z, Wang C, & *Yu Y (2018)
    Efficient coding and energy efficiency are promoted by balanced excitatory and inhibitory synaptic currents in neuronal network.
    Frontiers in cellular neuroscience, 12, 123.

  14. Du MM, *Yu Y and *Wu Y, (2018)
    Astrocytic Kir4.1 channels and gap junctions account for spontaneous epileptic seizure,
    PLoS Comp. Biol. 14(3): e1005877

  15. Zheng G, Qi X, Li Y, Zhang W, *Yu Y. (2018)
    A comparative study of standardized infinity reference and average reference for EEG of three typical brain states.
    Frontiers in Neuroscience. 2018;12:158.

  16. Liu Y, Yue Y, Yu Y, Liu L, and Yu LC, (2018)
    Effects of channel blocking on information transmission and energy efficiency in squid giant axons.
    Journal of Computational Neuroscience. 44(2):1-13.

  17. Zhou SL and *Yu Y, (2018)
    Synaptic E-I balance underlies efficient neural coding,
    Frontiers in Neuroscience. 12:46.

  18. Wang YY, Wang PX and *Yu Y, (2018)
    Decoding English alphabet letters using EEG phase information,
    Frontiers in Neuroscience. 12:62

  19. *Yu Y, Herman P, Rothman DL, Agarwal D and Hyder F, (2018)
    Evaluating the gray and white matter energy budgets of human brain function,
    Journal of Cerebral Blood Flow & Metabolism, 38(8):1339-1353.

  20. Yu LC and *Yu Y, (2017)
    Energy-Efficient Neural Information Processing in Individual Neurons and Neuronal Networks,
    Journal of Neuroscience Research. 95(11):2253-2266.

  21. Li J, Xie Y, Yu Y, Wu Y, (2017)
    A neglected GABAergic astrocyte: Calcium dynamics and involvement in seizure activity.
    Sci. China Technol. Sci. 60: 1003.

  22. Fan, BQ, He Y, *Yu Y (2016)
    1/f Characteristic in Natural Signals and Sensory Neural Response Properties.
    SCIENTIA SINICA Vitae, 46(4), 374-384.

  23. Chen Z, He Y, *Yu Y (2016)
    Enhanced functional connectivity properties of human brains during in- situ nature experience,
    PeerJ, 4:e2210

  24. Ye W, Liu S, Liu X and Yu Y (2016)
    A neural model of the frontal eye fields with reward-based learning.
    Neural Networks 81:39–51.

  25. Ju H, Hines ML, *Yu Y (2016)
    Cable energy function of cortical axons,
    Scientific Report. 6:29686.

  26. Yu LC, Zhang C, Liu YW and *Yu Y (2016)
    Energy-efficient population coding constrains network size of a neuronal array system,
    Scientific Report. 6:193691-8

  27. Zhou S, Migliore M and *Yu Y (2016)
    Odor experience facilitates sparse representations of new odors in a large-scale olfactory bulb model.
    Front in neuroanat. 10:10.

  28. Pan XL. et al, ... *Yu Y and *Zhong CJ. (*Co-PI) (2016)
    Measurement of Blood Thiamine Metabolites for Alzheimer’s disease Diagnosis,
    EBioMedicine, 26(3):155-162.

  29. Zhang W, Yang HL, Song JJ, Chen M, Dong Y, Lai B, Yu Y, Ma L and Zheng P, (2015)
    AMGO depresses inhibitory synaptic transmission via different downstream pathways of μ opioid receptors in ventral tegmental area and periaqueductal gray.
    Neuroscience 301, 144-154

  30. Li JJ, Liu SB, Liu WM, Yu Y, Wu Y. (2015)
    Suppression of firing activities in neuron and neurons of network induced by electromagnetic radiation,
    Nonlinear Dynamics, 1-10

  31. *Yu Y, Migliore M, Hines ML, and Shepherd GM (2014)
    Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition,
    Journal of Neuroscience 34: 13701-13713.

  32. *Yu Y, Karbowski J, Sachdev RN, Feng J.  (2014)
    Effect of temperature and glia in brain size enlargement and origin of allometric body-brain size scaling in vertebrates.
    BMC Evol Biol, 2014, 14:71801-71814.

  33. *Yu Y. (2014)
    Constant Warm Body Temperature Ensures High Response Reliability of Neurons in Endothermic Brains.
    Austin J Comput Biol Bioinform, 1(1): 5.

  34. *Yu Y, McTavish TS, Valenti C, Hines ML, Shepherd GM and Migliore M. (2013)
    Sparse distributed representation of odors in a large-scale olfactory bulb circuit.   
    PLoS Comp. Biol., 2013 9(3): e1003014.

  35. Yu Y, Hill A. and McCormick DA. (2012)
    Warm body temperature facilitates energy efficient cortical action potentials.
    PLoS Comp. Biol., 8(4) e1002456.

  36. Foust AJ, Yu Y, Popovic M, Zecevic D and McCormick DA (2011)
    Somatic Membrane Potential and Kv1 Channels Control Spike Repolarization in Cortical Axon Collaterals and Presynaptic Boutons.
    Journal of Neuroscience, 31(43):15490-15498.

  37. Yu Y, Maureira C and McCormick DA (2010)
    P/Q and N channels control baseline and spike-triggered calcium levels in neocortical axons and synaptic boutons,  
    Journal of Neuroscience, 30: 118580-11869

  38. Haider B, Krause MR, Duque A, Yu Y, Touryan J, Mazer JA, McCormick DA (2010)
    Synaptic and network mechanisms of sparse and reliable visual cortical activity during non-classical receptive field stimulation.
    Neuron, 65: 107-121

  39. Ros H, Sashdev R, Yu Y, Sestan N and McCormick D.A.
    Neocortical Networks Entrain Neuronal Circuits in Cerebellar Cortex.
    Journal of Neuroscience, 29: 10309-20, (2009)

  40. Yu Y, Shu Y, Duque A, Haider B and McCormick DA (2008)
    Cortical Action Potential Back-propagation Explains Spike Threshold Variability and Rapid-Onset Kinetics.
    Journal of Neuroscience, 28: 7260-7272. PMCID: 2664555

  41. McCormick DA, Shu Y and Yu Y (2007)
    Hodgkin and Huxley model—still standing?
    Nature. 445: E1-E2.

  42. Shu Y, Yu Y, Yang J and McCormick DA (2007)
    Selective control of cortical axonal action potentials by a  slowly inactivating K+ current.
    Proc Natl Acad  Sci USA (PNAS) 27: 11453-11458.

  43. Shu Y, Duque A, Yu Y, Haider B and McCormick DA (2007)
    Properties of action potential initiation in neocortical pyramidal cells: evidence from whole cell axon recordings.
    Journal of Neurophysiology 97: 746-760.

  44. Haider B., Duque A., Hasenstaub A.R., Yu Y.G., McCormick D.A. (2007)
    Enhancement of visual responsiveness by spontaneous local network activity in vivo.
    Journal of Neurophysiology 97: 4186-4202

  45. Shu Y, Hasenstaub A, Duque A, Yu Y and McCormick DA (2006)
    Modulation of intracortical synaptic potentials by presynaptic somatic membrane potential.
    Nature 441(7094):761-5.

  46. *Yu Y, Richard RD and Lee TS (2005)
    Preference of sensory neural coding for 1/f signals.
    Phys Rev Lett, 94, 1081031-4

  47. *Yu Y, Potetz B and Lee TS (2005)
    The role of spiking nonlinearity in contrast gain control and information transmission.
    Vis Res 45(5) 583-92.

  48. Yu Y, Richard RD and Lee TS. (2005)
    Adaptive contrast gain control and information maximization.
    Neurocomputing, 65-66 111-116

  49. *Yu Y, Liu F, Wang W. and Lee TS.(2004)
    Optimal synchrony state for maximal information transmission.
    NeuroReport, 15(10):1605-10

  50. Wang S, Liu F, Wang W and Yu Y (2004)
    Impact of Spatially correlated noise on neuronal firing.
    Phys Rev E, 69, 0119091-0119097.

  51. Yu Y, Liu F, Wang J. and Wang W. (2003)   
    Synchronized Rhythmic Oscillation in a Noisy Neural Network.
    Journal of the Physical Society of Japan. 72, 3291-3296

  52. *Yu Y and Lee TS (2003)
    Dynamical mechanisms underlying contrast gain control in single neurons.
    Phys Rev E 68, 0119011-0119017.

  53. Yu Y and Lee TS. (2003)
    Adaptation of the transfer function of the Hodgkin-Huxley (HH) neuronal model.
    Neurocomputing 52-54, 441-446

  54. Romero R, Yu Y, Afshar P and Lee TS. (2003)
    Adaptation of the temporal receptive fields of Macaque V1 neurons.
    Neurocomputing 52-54, 135-140.

  55. Yu Y, Wang W, Wang J, and Liu, F. (2001)
    Resonance-enhanced signal detection and transduction in Hodgkin-Huxley neuronal systems.
    Physical Review E, 63, 0219071-02190712

  56. Yu Y, Liu F and Wang W. (2001)
    Spike timing precision for a neuronal array with periodic signal.
    Physics Letters A, 282, 23-30

  57. Liu F, Yu Y and Wang W. (2001)
    Signal-to-noise gain in nervous systems.
    Physical Review E, 63, 0519121-0519124

  58. Yu Y Liu F and Wang W. (2001)
    Frequency sensitivity in Hodgkin-Huxley systems.
    Biological Cybernetics, 84, 227-233

  59. Yu Y and Wang W. (2001)
    Generation of Spontaneous Synchronized Rhythm and its Role in Information Processing.
    Chinese Physics Letters, 18, 295-298

  60. Liu F, Yu Y, and Wang W,(2000)
    Information Representation and Its Application to Stochastic Resonance.
    Journal of the Physical Society of Japan, 69, 4107-4111