[1] 黄秉宪. 脑的高级功能和神经网络[M]. 科学出版社, 2000. [2] 郭爱克. 计算神经科学[M]. 上海科技教育出版社, 2000. [3] 李继硕. 神经科学基础[M]. 高等教育出版社, 2002. [4] 杨建刚. 人工神经元网络实用教程[M]. 浙江大学出版社, 2001. [5] 周志华,曹存根. 神经网络及其应用[M]. 清华大学出版社, 2004. [6] 阮晓钢. 神经计算科学——在细胞的水平上模拟脑功能[M]. 国防工业出版社, 2006. [7] 叶世伟,史忠植译. Neural Networks[M]. 机械工业出版社, 2004. [8] 赵志奇,陈军主译. Modern Techniques in Neuroscience Research[M]. 科学出版社, 2006. [9] 吴俊芳,刘忞主编. 现代神经科学研究方法[M]. 中国协和医科大学出版社, 2006. [10] Hubel D H. Tungsten microelectrode for recording from single units[J]. Science, 1957, 125:549-550. [11] Cole K S, Moore J W. Ionic current measurements in the squid giant axon membrane[J]. The Journal of general physiology, 1960, 44:123-169. [12] Crick F. The impact of molecular biology on neuroscience[J]. Philosophical Transactions of the Royal Society, 1999, 354:2021-2025. [13] Lauterbur P C. Image formation by induced local interactions:examples employing nuclear magnetic resonance[J]. Nature, 1973, 242:190-191. [14] Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. Journal of Physiology, 1952, 117:500-544. [15] Rall W. Theory of physiological properties of dendrites[J]. Annals of the New York Academy of Sciences, 1962, 96(4):1071-1092. [16] Schwartz E L. Computational Neuroscience[M]. Cambridge:MIT Press, 1990. [17] Dayan P, Abbott L. Theoretical neuroscience[M]. Cambridge:MIT Press, 2001. [18] Sejnowski T J, Koch C, Churchland P S. Computational neuroscience[J]. Science, 1988, 241:1299-1306. [19] Abbott L. Theoretical neuroscience rising[J]. Neuron, 2008, 60:489-495. [20] 汪小京. 21世纪的中国计算神经科学展望[M]. 科学时报, 2010. [21] Wang X J, et al. Computational Neuroscience:A Frontier of the 21st Century[J]. National Science Review, 2020, 7:1418-1422. [22] Zhou D, Xiao Y, Zhang Y, Xu Z, Cai D. Causal and Structural Connectivity of Pulse-Coupled Nonlinear Networks[J]. Physical Review Letters, 2013, 111:054102. [23] Zhou D, Xiao Y, Zhang Y, Xu Z, Cai D. Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems[J]. PLOS ONE, 2014, 9:e87636. [24] Zhou D, Rangan A V, McLaughlin D W, Cai D. Spatiotemporal dynamics of neuronal population response in the primary visual cortex[J]. Proceedings of the National Academy of Sciences, USA, 2013, 110:9517-9522. [25] Rosenblatt F. Principles of Neurodynamics[M]. New York:Spartan, 1962. [26] Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-Inspired Artificial Intelligence[J]. Neuron, 2017, 95:245-258. [27] Richards B A, Lillicrap T P, Kording K P. A deep learning framework for neuroscience[J]. Nature Neuroscience, 2019, 22:1761-1770. [28] Poo M M, Du J, Ip N Y, Xiong Z, Xu B, Tan T. China Brain Project:Basic neuroscience, brain diseases, and brain-inspired computing[J]. Neuron, 2016, 92:591-596. [29] Lillicrap T P, Santoro A, Marris L, Akerman C J, Hinton G. Backpropagation and the brain[J]. Nature Reviews Neuroscience, 2020, 21:335-346. [30] Zhou D, Rangan A V, Sun Y, Cai D. Network-induced chaos in integrate-and-fire neuronal ensembles[J]. Physical Review E, 2009, 80:031918. [31] Zhou D, Sun Y, Rangan A V, Cai D. Spectrum of Lyapunov exponents of non-smooth dynamical systems of integrate-and-fire type[J]. Journal of Computational Neuroscience, 2010, 28:229-245. [32] Sompolinsky H, Crisanti A, Sommers H J. Chaos in random neural networks[J]. Physical Review Letters, 1988, 61:259-262. [33] Newhall K, Kovacic G, Kramer P, Zhou D, Rangan A V, Cai D. Dynamics of current-based, Poisson driven, integrate-and-fire neuronal networks[J]. Communications in Mathematical Sciences, 2010, 8:541-600. [34] Gu Q L, Tian Z K, Kovacic G, Zhou D, Cai D. The dynamics of balanced spiking neuronal networks under Poisson drive is not chaotic[J]. Frontiers in Computational Neuroscience, 2018, 12:47. [35] van Vreeswijk C, Sompolinsky H. Chaos in neuronal networks with balanced excitatory and inhibitory activity[J]. Science, 1996, 274:1724-1726. [36] van Vreeswijk C, Sompolinsky H. Chaotic balanced state in a model of cortical circuits[J]. Neural Computation, 1998, 10:1321-1371. [37] Wang X J. Neurophysiological and computational principles of cortical rhythms in cognition[J]. Physiological Review, 2010, 90:1195-1268. [38] Xu Z J, Bi G, Zhou D, Cai D. A dynamical state underlying the second order maximum entropy principle in neuronal networks[J]. Communications in Mathematical Sciences, 2017, 15:665-692. [39] Xu Z J, Crodelle J, Zhou D, Cai D. Maximum entropy principle analysis in network systems with short-time recordings[J]. Physical Review E, 2019, 99:022409. [40] Xu Z J, Gu X, Li C, Cai D, Zhou D, McLaughlin D W. Neural networks of different species, brain areas and states can be characterized by the probability polling state[J]. European Journal of Neuroscience, 2020, 52:3790-3802. [41] Li S, McLaughlin D W, Zhou D. Mathematical modeling and analysis of spatial neuron dynamics:dendritic integration and beyond[J]. Communications on Pure and Applied Mathematics, accepted. [42] Sun Y, Zhou D, Rangan A V, Cai D. Library-based numerical reduction of the Hodgkin-Huxley neuron for network simulation[J]. Journal of Computational Neuroscience, 2009, 27:369-390. [43] Tian Z K, Crodelle J, Zhou D. A combined offline-online algorithm for Hodgkin-Huxley neural networks[J]. Journal of Scientific Computing, 2020, 84:10. [44] Tian Z K, Zhou D. Exponential time differencing algorithm for pulse-coupled Hodgkin-Huxley neural networks[J]. Frontiers in Computational Neuroscience, 2020, 14:40. [45] Zhou D, Zhang Y, Xiao Y, Cai D. Reliability of the Granger causality inference[J]. New Journal of Physics, 2020, 14:40. [46] Zhou D, Zhang Y, Xiao Y, Cai D. Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics[J]. Frontiers in Computational Neuroscience, 2014, 8:75. [47] Zhang Y, Xiao Y, Zhou D, Cai D. Granger causality analysis with nonuniform sampling and its application to pulse-coupled nonlinear dynamics[J]. Physical Review E, 2016, 93:042217. [48] Barranca V J, Kovacic G, Zhou D, Cai D. Improved compressive sensing of natural scenes using localized random sampling[J]. Scientific Reports, 2016, 6:31976. [49] Barranca V J, Zhou D, Cai D. A Novel characterization of amalgamated networks in natural systems[J]. Scientific Reports, 2015, 5:10611. [50] Barranca V J, Zhou D, Cai D. Low-rank network decomposition reveals structural characteristics of small-world networks[J]. Physical Review E, 2015, 92:062822. [51] Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks[J]. Neuroimage, 2011, 56:2068-2079. [52] Li S, Liu N, Zhang X, Zhou D, Cai D. Bilinearity in spatiotemporal dendritic integration of synaptic conductance inputs[J]. PLoS Computational Biology, 2014, 10:e1004014. [53] Li S, Liu N, Zhang X, Zhou D, Cai D. Determination of effective synaptic conductances using somatic voltage clamp[J]. PLoS Computational Biology, 2019, 15:e1006871. [54] Barranca V J, Kovacic G, Zhou D, Cai D. Sparsity and compressed coding in sensory systems[J]. PLoS Computational Biology, 2014, 10:e1003793. [55] Barranca V J, Kovacic G, Zhou D, Cai D. Efficient image processing via compressive sensing of integrate-and-fire neuronal network dynamics[J]. Neurocomputing, 2016, 171:1313-1322. [56] Ganguli S, Sompolinsky H. Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis[J]. Annual Review of Neuroscience, 2012, 35:485-508. [57] Zhou D, Li S, Zhang X, Cai D. Phenomenological incorporation of nonlinear dendritic integration using integrate-and-fire neuronal frameworks[J]. PLoS ONE, 2013, 8:e53508. [58] Li S, Liu N, Zhang X, McLaughlin D W, Zhou D, Cai D. Dendritic computations captured by an effective point neuron model[J]. Proceedings of the National Academy of Sciences, USA, 2019, 116:15244-15252. [59] Dai W P, Zhou D, McLaughlin D W, Cai D. Mechanisms underlying contrast-dependent orientation selectivity in mouse V1[J]. Proceedings of the National Academy of Sciences, USA, 2018, 115:11619-11624. [60] Newman M E J. The structure and function of complex networks[J]. SIAM Review, 2003, 45:167-256. [61] Sporns O, Chialvo D R, Kaiser M, Hilgetag C. Organization, development and function of complex brain networks[J]. TRENDS in Cognitive Sciences, 2004, 8:418-425. [62] Honey C J, Thivierge J, Sporns O. Can structure predict function in the human brain?[J]. NeuroImage, 2010, 52:766-776. [63] Wiener N. The theory of prediction[J]. In:Beckenbach E, (Ed.) Modern mathematics for engineers. McGraw-Hill, New York, 1956. [64] Granger C. Investigating causal relations by econometric models and crossspectral methods[J]. Econometrica, 1969, 37:424-438. [65] Geweke J. Measurement of linear dependence and feedback between multiple time series[J]. Journal of the American Statistical Association, 1982, 77:304-313. [66] Geweke J. Measures of conditional linear dependence and feedback between time series[J]. Journal of the American Statistical Association, 1984, 79:907-915. [67] Ding M, Chen Y, Bressler S L. Granger causality:basic theory and application to neuroscience[J]. In Handbook of time series analysis, Schelter S, Winterhalder M, Timmer J (eds). Wiley-VCH:Berlin, 2006, 437-460. [68] Bressler S L, Seth A K. Wiener-Granger causality:a well established methodology[J]. NeuroImage, 2011, 58:323-329. [69] Seth A K. A MATLAB toolbox for Granger causal connectivity analysis[J]. Journal of Neuroscience Methods, 2010, 186:262-273. [70] Sun Y, Zhou D, Rangan A V, Cai D. Pseudo-Lyapunov exponents and predictability of HodgkinHuxley neuronal network dynamics[J]. Journal of Computational Neuroscience, 2010, 28:247-266. [71] Somers D C, Nelson S B, Sur M. An emergent model of orientation selectivity in cat visual cortical simple cells.[J]. Journal of Neuroscience, 1995, 15:5448-5465. [72] Troyer T W, Krukowski A E, Priebe N J, Miller K D. Contrast-invariant orientation tuning in cat visual cortex:Thalamocortical input tuning and correlation-based intracortical connectivity.[J]. Journal of Neuroscience, 1998, 18:5908-5927. [73] Tao L, Shelley M, McLaughlin D W, Shapley R. An egalitarian network model for the emergence of simple and complex cells in visual cortex.[J]. Proceedings of National Academy of Sciences of the United States of America, 2004, 101:366-371. [74] Cai D, Rangan A V, McLaughlin D W. Architectural and synaptic mechanisms underlying coherent spontaneous activity in V1.[J]. Proceedings of National Academy of Sciences of the United States of America, 2005, 102:5868-5873. [75] Rangan A V, Cai D, McLaughlin D W. Modeling the spatiotemporal cortical activity associated with the line-motion illusion in primary visual cortex.[J]. Proceedings of National Academy of Sciences of the United States of America, 2005, 102:18793-18800. [76] Rangan A V, Cai D. Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks[J]. Journal of Computational Neuroscience, 2007, 22:81-100. [77] McLaughlin D W, Shapley R, Shelley M, Wielaard D J. A neuronal network model of macaque primary visual cortex (V1):Orientation selectivity and dynamics in the input layer 4C alpha.[J]. Proceedings of National Academy of Sciences of the United States of America, 2000, 97:8087-8092. [78] Komatsu Y, Nakajima S, Toyama K, Fetz E E. Intracortical connectivity revealed by spiketriggered averaging in slice preparations of cat visual cortex[J]. Brain Research, 1988, 442:359-362. [79] Matsumura M, Chen D, Sawaguchi T, Kubota K, Fetz E E. Synaptic interactions between primate precentral cortex neurons revealed by spike-triggered averaging of intracellular membrane potentials in vivo[J]. The Journal of Neuroscience, 1996, 16:7757-7767. [80] de Boer F, Kuyper P. Triggered Correlation[J]. IEEE Transactions on Biomedical Engineering, 1968, 15:169-179. [81] Bhalla U S. How To Record a Million Synaptic Weights in a Hippocampal Slice[J]. PLoS Computational Biology, 2008, 4:e1000098. [82] Pandit S, Wu S. Time series and system analysis with applications[M]. New York:Wiley, 1983. [83] McQuarrie A, Tai C L. Regression and time series model selection[M]. New Jersey:World Scientific, 1998. [84] Busse L, Wade A R, Carandini M. Representation of concurrent stimuli by population activity in visual cortex[J]. Neuron, 2009, 64(6):931-942. [85] Felleman D J, Essen D C V. Distributed hierarchical processing in the primate cerebral cortex[C]. In Cereb cortex. Citeseer, 1991. [86] Pouget A, Dayan P, Zemel R. Information processing with population codes[J]. Nature Reviews Neuroscience, 2000, 1(2):125-132. [87] Frostig R D. In vivo optical imaging of brain function[M]. CRC press, 2009. [88] Blasdel G G, Salama G. Voltage-sensitive dyes reveal a modular organization in monkey striate cortex[J]. Nature, 1986, 321(6070):579-585. [89] Grinvald A, Hildesheim R. VSDI:a new era in functional imaging of cortical dynamics[J]. Nature Reviews Neuroscience, 2004, 5(11):874-885. [90] Chen Y, Geisler W S, Seidemann E. Optimal decoding of correlated neural population responses in the primate visual cortex[J]. Nature neuroscience, 2006, 9(11):1412-1420. [91] Palmer C, Cheng S Y, Seidemann E. Linking neuronal and behavioral performance in a reactiontime visual detection task[J]. Journal of Neuroscience, 2007, 27(30):8122-8137. [92] Chen Y, Geisler W S, Seidemann E. Optimal temporal decoding of neural population responses in a reaction-time visual detection task[J]. Journal of neurophysiology, 2008, 99(3):1366-1379. [93] Sit Y F, Chen Y, Geisler W S, Miikkulainen R, Seidemann E. Complex dynamics of V1 population responses explained by a simple gain-control model[J]. Neuron, 2009, 64(6):943-956. [94] Eysel U. Turning a corner in vision research[J]. Nature, 1999, 399(6737):641-643. [95] Gilbert C D. Horizontal integration and cortical dynamics[J]. Neuron, 1992, 9(1):1-13. [96] Huntley G W, Vickers J, Janssen W, Brose N, Heinemann S, Morrison J. Distribution and synaptic localization of immunocytochemically identified NMDA receptor subunit proteins in sensory-motor and visual cortices of monkey and human[J]. Journal of Neuroscience, 1994, 14(6):3603-3619. [97] Myme C I, Sugino K, Turrigiano G G, Nelson S B. The NMDA-to-AMPA ratio at synapses onto layer 2/3 pyramidal neurons is conserved across prefrontal and visual cortices[J]. Journal of neurophysiology, 2003, 90(2):771-779. [98] Mariño J, Schummers J, Lyon D C, Schwabe L, Beck O, Wiesing P, Obermayer K, Sur M. Invariant computations in local cortical networks with balanced excitation and inhibition[J]. Nature neuroscience, 2005, 8(2):194-201. [99] Angelucci A, Levitt J B, Walton E J, Hupe J M, Bullier J, Lund J S. Circuits for local and global signal integration in primary visual cortex[J]. Journal of Neuroscience, 2002, 22(19):8633-8646. [100] Koch C. Biophysics of computation:information processing in single neurons[M]. Oxford university press, 2004. [101] DeAngelis G C, Ohzawa I, Freeman R D. Receptive-field dynamics in the central visual pathways[J]. Trends in neurosciences, 1995, 18(10):451-458. [102] Shoham D, Glaser D E, Arieli A, Kenet T, Wijnbergen C, Toledo Y, Hildesheim R, Grinvald A. Imaging cortical dynamics at high spatial and temporal resolution with novel blue voltage-sensitive dyes[J]. Neuron, 1999, 24(4):791-802. [103] Arieli A, Sterkin A, Grinvald A, Aertsen A. Dynamics of ongoing activity:explanation of the large variability in evoked cortical responses[J]. Science, 1996, 273(5283):1868-1871. [104] Tolhurst D J, Movshon J A, Dean A F. The statistical reliability of signals in single neurons in cat and monkey visual cortex[J]. Vision research, 1983, 23(8):775-785. [105] Geisler W S, Albrecht D G. Visual cortex neurons in monkeys and cats:detection, discrimination, and identification[J]. Visual neuroscience, 1997, 14(5):897-919. [106] Sompolinsky H, Yoon H, Kang K, Shamir M. Population coding in neuronal systems with correlated noise[J]. Physical Review E, 2001, 64(5):051904. [107] Abbott L F, Dayan P. The effect of correlated variability on the accuracy of a population code[J]. Neural computation, 1999, 11(1):91-101. [108] Duda R O, Hart P E, Stork D G. Pattern classification[M]. John Wiley & Sons, 2012. [109] Carandini M, Heeger D J. Summation and division by neurons in primate visual cortex[J]. Science, 1994, 264(5163):1333-1336. [110] Albrecht D G. Visual cortex neurons in monkey and cat:effect of contrast on the spatial and temporal phase transfer functions[J]. Visual neuroscience, 1995, 12(6):1191-1210. [111] Zhang J, Newhall K A, Zhou D, Rangan A V. Distribution of correlated spiking events in a population-based approach for integrate-and-fire networks[J]. Journal of Computational Neuroscience, 2014, 36:279-295. [112] Zhang J, Zhou D, Cai D, Rangan A V. A coarse-grained framework for spiking neuronal networks:between homogeneity and synchrony[J]. Journal of Computational Neuroscience, 2014, 37:81-104. [113] Shao Y, Zhang J, Tao L. Dimensional reduction of emergent spatiotemporal cortical dynamics via a maximum entropy moment closure[J]. PLoS Computational Biology, 2020, 16:e1007265. [114] Cai D, Tao L, Rangan A V, McLaughlin D W. Kinetic theory for neuronal network dynamics[J]. Communications in Mathematical Sciences, 2006, 4:97-127. [115] Rangan A V, Cai D, Tao L, McLaughlin D W. Numerical methods for solving moment equations in kinetic theory of neuronal network dynamics[J]. Journal of Computational Physics, 2007, 22:781-798. [116] Sejnowski T J, Churchland P, Movshon J A. Putting big data to good use in neuroscience[J]. Nature Neuroscience, 2014, 17:1440-1441. [117] W. L J, Livet J, Sanes J R. A technicolour approach to the connectome[J]. Nature Reviews Neuroscience, 2008, 9:417-422. [118] Gautrais J, Thorpe S. Rate coding versus temporal order coding:a theoretical approach[J]. Biosystems, 1998, 48:57-65. |