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平成29年度「身体システム論」

山本義春 (教育学研究科・身体教育学コース)
森田賢治 (教育学研究科・身体教育学コース)

平成29年6月3日

人間の活動あるいは行動に関する探究に際しては、微視的・巨視的両視点から の総合的なアプローチが必要である。本ゼミでは、「臓器と個体」「神経細胞と 脳」「個人と社会」など、「要素と全体」を繋ぐ数理モデルを中心に、複雑適応 系・創発システム・自己組織系などに関する研究論文を輪読する。また、必要に 応じて、自身の研究発表および討論を行う。

論文の選択は、(計算)神経科学、数理生物学、サイバネティクス、数理経済学、 数理社会学などの分野から、原則として担当教官が行う。一回に一名の受講者 が内容を紹介し、その後参加者全員で討論を行う。ただし理解が不十分であれ ば、複数回に渡ることもあろう。担当教官もなるべく平易な解説を心掛けるの で、参加者も「理論的に考える」ことを心掛けて欲しい。

なお本ゼミは、原則として夏期・冬季・春期休業中も継続する。また本年度は、 夏学期においては、データ解析に必要な概念や手法に関する基礎的な事項を 学習・演習する(教科書等は追って指定する)。そして、近年の科学の動向を 踏まえ、単なる理論的研究に止まらず、データとの融合を見据えた生物医学 研究の新たな方略を模索する。

参考までに、以下はこれまで取り上げられてきた論文のリスト(の一部)である。

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  9. Lorenz, E. N. Lyapunov numbers and the local structure of attractors. Physica D 17: 279-294, 1985.

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  44. Thurner, S., et al. Receiver-operating-characteristic analysis reveals superiority of scale-dependent wavelet and spectral measures for assessing cardiac dysfunction. Phys. Rev. Lett. 81: 5688-5691, 1998.

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  56. Mar, D.J., et al. Noise shaping in populations of coupled model neurons. Proc. Natl. Acad. Sci. USA 96: 10450-10455, 1999.

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  58. Rodriguez, E., et al. Perception's shadow: long-distance synchronizaion of human brain activity. Nature 397: 430-433, 1999.

  59. Chialvo, D. R. and P. Bak. Learning from mistakes. Neuroscience 90: 1137-1148, 1999.

  60. Dolan, K., et al. Surrogates for finding unstable periodic, orbits in noisy data sets. Phys Rev E 59: 5235-5241, 1999.

  61. West, B.J. and L. Griffin. Allometric control, inverse power laws and human gait. Chaos Solitons and Fractals 10: 1519-1527, 1999.

  62. West, B.J., et al. Fractal fluctuations in cardiac time series. Physica A 270: 552-566, 1999.

  63. Tomberg, C. Focal enhancement of chaotic strange attractor dimension in the left semantic (Wernicke) human cortex during reading without concomitant change in vigilance level. Neurosci. Lett. 263: 177-180, 1999.

  64. Chater, N. and G.D.A. Brown. Scale-invariance as a unifying psychological principle. Cognition 69: B17-B24, 1999.

  65. Saiz, J., et al. Influence of electrical coupling on early afterdepolarizations in ventricular myocytes. IEEE Trans. Biomed. Eng. 46: 138-147, 1999.

  66. Carlson, J. M., and J. Doyle. Highly optimized telerance: A mechanism for power laws in designed systems. Phys. Rev. E 60: 1412-1427, 1999.

  67. Neda, Z., et al. The sound of many hands clapping. Nature 403: 849-850, 2000.

  68. Tuckwell, H.C., et al. Enhancement of epidemic spread by noise and stochastic resonance in spatial network models with viral dynamics. Phys. Rev. E 61: 5611-5619, 2000.

  69. Zebrowski, J.J., et al. Symbolic dynamics and complexity in a physiological time series. Chaos, Solitons and Fractals 11: 1061-1075, 2000.

  70. Shnerg, N. M., et al. The importance of being discrete: Life always wins on the surface. Proc. Natl. Acad. Sci. USA 97: 10322-10324, 2000.

  71. Ditzinger, T., et al. Noise improves three-dimensional perception: Stochastic resonance and other impacts of noise to the perception of autostereograms. Phys. Rev. E 62: 2566-2575, 2000.

  72. Ho, N. and A. Destexhe. Synaptic background activity enhances the reponsiveness of neocortical pyramidal neurons. J. Neurophysiol. 84: 1488-1496, 2000.

  73. Anderson, J. S., et al. The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290: 1968-1972, 2000.

  74. Usher, M. and M. Feingold. Stochastic resonance in the speed of memory retrieval. Biol. Cybern. 83: L11-L16, 2000.

  75. Zeng, F., et al. Human hearing enhanced by noise. Brain Res. 869: 251-255, 2000.

  76. Daffertshofer, A., et al. Spectral analyses of event-related encephalographic signals. Phys. Lett. A 266: 290-302, 2000.

  77. Kish L.B. and S.M. Bezrukov. Flows of cars and neural spikes enhanced by colored noise. Phys. Lett. A 266: 271-275, 2000.

  78. Osipov V.V. and E.V. Ponizovskaya. Multivalued stochastic resonance in a model of an excitable neuron. Phys. Lett. A 271: 191-197, 2000.

  79. Mason, J., et al. Pulse-enhanced stochastic resonance Phys. Lett. A 277: 13-17, 2000.

  80. Stanley, H.E. Exotic statistical physics: Applications to biology, medicine, and economics Physica A 285: 1-17, 2000.

  81. Carpena, P., et al. Comparison of multifractal and thermodynamical properties of fractal and natural spectra. Physica A 287: 37-48, 2000.

  82. Effern, A., et al. Nonlinear denoising of transient signals with application to event-related potentials. Physica D 140: 257-266, 2000.

  83. Billock, V.A., et al. Fractal time and 1/f spectra in dynamic images and human vision. Physica D 148: 136-146, 2000.

  84. Pereda, E., et al. Assessment of changing interdependencies between human electroencephalograms using nonlinear methods. Physica D 148: 147-158, 2000.

  85. Huber, M.T., et al. Noise, nonlinear dynamics and the timecourse of affective disorders. Chaos, Solitons and Fractals 11: 1923-1928, 2000.

  86. Gingle, Z., et al. A self-adaptive stochastic resonator with logarithmic transfer. Chaos, Solitons and Fractals 11: 1933-1935, 2000.

  87. Stoop, R., et al. Generic origins of irregular spiking in neocortical networks. Biol. Cybern. 83: 481-489, 2000.

  88. Omberg, L., et al. Detecting the onset of bifurcations and their precursors from noisy data. Phys Rev E 61: 4848-4853, 2000.

  89. Laporte, P. Chaos, fractals, cerebral plasticity: Theroetical neuropsychology of cognitive functions. Int. J. Psychol. 35: 49-50, 2000.

  90. Kahn, D., et al. Dreaming and the self-organizing brain. J. Consciousness Stud. 7: 4-11, 2000.

  91. Yeragani, V.K., et al. Nonlinear measures of heart period variability: Decreased measures of symbolic dynamics in patients with panic disorder. Depress. Anxiety 12: 67-77, 2000.

  92. Killeen, P.R., and T.J. Taylor. How the propagation of error through stochastic counters affects time discrimination and other psychophysical judgments. Psychol. Rev. 107: 430-459, 2000.

  93. Richards, G.R. Reconciling econophysics with macroeconomic theory. Physica A 282: 325-335, 2000.

  94. Dunki, R.M., et al. Temporal patterns of human behaviour: are there signs of deterministic 1/f scaling? Physica A 276: 596-609, 2000.

  95. Kopell, N., et al. Gamma rhythms and beta rhythms have different synchronization properties. Proc. Natl. Acad. Sci. USA 97: 1867-1872, 2000.

  96. Stocks, N.G. Suprathreshold stochastic resonance: an exact result for uniformly distributed signal and noise Phys. Lett. A 279: 308-312, 2001.

  97. Rozenfeld, A.F., et al. On the influence of noise on the critical and oscillatory behavior of a predator-prey model: coherent stochastic resonance at the proper frequency of the system. Phys. Lett. A 280: 45-52, 2001.

  98. Tikhonov, D.A., et al. Chaos and fractals in fish school motion. Chaos, Solitons and Fractals 12: 277-288, 2001.

  99. Fayos, J. Emergence of order from chaos by cooperative non-biased learning between random bit-strings. Chaos, Solitons and Fractals 12: 865-873, 2001.

  100. Keeling, M.J., et al. Seasonally forced disease dynamics explored as switching between attractors. Physica D 148: 317-335, 2001.

  101. Glass, L. Synchronization and rhythmic processes in physiology. Nature 410: 277-284, 2001.

  102. Lin, D.C. and R.L. Hughson. Modeling heart rate variability in healthy humans: a turbulence analogy. Phys. Rev. Lett. 86: 1650-1653, 2001.

  103. Danzo, B.J. Hormonal chaos: The scientific and social origins of the environmental endocrine hypothesis. Am. J. Hum. Biol. 13: 285-287, 2001.

  104. Mitra, T. and K. Nishimura. Discounting and long-run behavior: Global bifurcation analysis of a family of dynamical systems. J. Econ. Theory 96: 256-293, 2001.

  105. Strogatz, S. H. Exploring complex networks. Nature 410: 268-276, 2001.

  106. Daido, H. Why circadian rhythms are circadian: Competitive population dynamics of biological oscillators. Phys. Rev. Lett. 87: 048101-1-4, 2001.

  107. Nakao, M., et al. An optimal control model of 1/f fluctuations in heart rate variability. IEEE Eng. Med. Biol. March/April, pp77-87, 2001.

  108. Yao, W., et al. Delayed stochastic differential model for quiet standing. Phys. Rev. E 63: 021902-1-8, 2001.

  109. Dafilis, M. P., et al. Robust chaos in a model of the electroencephalogram: Implications for brain dynamics. Chaos 11: 474-478, 2001.

  110. Wilson, H. R., et al. Dynamics of travelling waves in visual perception. Nature 412: 907-910, 2001.

  111. Stacey, W. C. and D. M. Durand. Synaptic noise improves detection of subthreshold signals in hippocampal CA1 neurons. J. Neurophysiol. 86: 1104-1112, 2001.

  112. Wakeling, J and P. Bak. Intelligent systems in the context of surrounding environment. Phys. Rev. E. 64: 051920-1-8, 2001.

  113. Balazsi, G., et al. Spatiotemporal stochastic resonance and its consequences in neural model systems. Chaos 11: 563-569, 2001.

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  115. Silchenko, A. and C. K. Hu. Multifractal characterization of stochastic resonance Phys. Rev. E 63: 041105-1-10, 2001.

  116. Gao, Z., et al. Stochastic resonance of small-world networks. Phys. Rev. E 65: 016209-1-4, 2001.

  117. Varela, F., et al. The brainweb: Phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2: 229-239, 2001.

  118. Ernst, M.O. and M. S. Banks. Human integrate visual and haptic information in a statistically optimal fashion. Nature 415: 429-433, 2002.

  119. Froemke, R. C. and Y. Dan. Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416: 433-438, 2002.

  120. Mazurek, M. E. and M. N. Shadlen. Limits to the temporal fidelity of cortical spike rate signals. Nature Neurosci. 5: 463-471, 2002.

  121. Neiman, A. B. and D. F. Russell. Synchronization of noise-induced bursts in noncoupled sensory neurons. Phys. Rev. Lett. 88: 138103-1-4, 2002.

  122. Quiroga, R. G., et al. Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Phys. Rev. E 65: 041903-1-14, 2002.

  123. Copelli, M., et al. Physics of psychophysics: Stevens and Weber-Fechner laws are transfer functions of excitable media. Phys. Rev. E 65: 060901-1-4, 2002.

  124. Chialvo, D. R., et al. Subharmonic stochastic synchronization and resonance in neuronal systems Phys. Rev. E 65: 050902-1-4, 2002.

  125. Kim, B. J., et al. Dynamic instabilities induced by asymmetric influence: Prisoners' dilemma game in small-world networks Phys. Rev. E 66: 021907-1-4, 2002.

  126. Makeig, S., et al. Dynamic brain sources of visual evoked responses. Science 295: 690-694, 2002.

  127. Gabbiani, F., et al. Multiplicative computation in a visual neuron sensitive to looming. Nature 420: 320-324, 2002.

  128. Monterola, C. and C. Saloma. Noise-driven manifestation of learning in mature neural networks. Phys. Rev. Lett. 89: 188102-1-4, 2002.

  129. Rao, C. V., et al. Control, exploitation and tolerance of intracellular noise. Nature 420: 231-237, 2002.

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  131. Guerra, P. G., et al. A mathematical model of human atrioventricular nodal function incorporating concealed conduction. Bull. Math. Biol. 64: 1083-1099, 2002.

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  133. Cabrera, J. L. and J. G Milton. On-off intermittency in a human balancing task. Phys. Rev. Lett. 89: 158702-1-4, 2002.

  134. Conradt, L. and T. J. Roper. Group decision-making in animals. Nature 421: 155-158, 2003.

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  145. Haruno, M., et al. A neural correlate of reward-based behavioral learning in caudate nucleus: A functional magnetic resonance imaging study of a stochastic decision task. J. Neurosci. 24: 1660-1665, 2004.

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  148. Halley, J. D., et al. Rapid self-organized criticality: Fractal evolution in extreme environments. Phys. Rev. E 70: 036118-1-8, 2004.

  149. Squires, T. M. Optimizing the vertebrate vestibular semicircular canal: Could we balance any better?. Phys. Rev. Lett. 93: 198106-1-4, 2004.

  150. Cabrera, J. L. and J. G Milton. Human stick balancing: Tuning Levy flights to improve balance control. Chaos 14: 691-698, 2004.

  151. Buzsaki, G. and A. Draguhn. Neuronal oscillations in cortical networks. Science 304: 1926-1929, 2004.

  152. Kahneman, D., et al. A survey method for characterizing daily life experience: The day reconstruction method. Science 306: 1776-1780, 2004.

  153. Smith, P. L. and R. Ratcliff. Psychology and neurobiology of simple decisions. Trends Neurosci. 27: 161-168, 2004.

  154. Eguiluz, V. M., et al. Scale-free brain functional networks. Phys. Rev. Lett. 94: 018102-1-4, 2005.

  155. Song, C., et al. Self-similarity of complex networks. Nature 433: 392-395, 2005.

  156. Haldeman, C. and J. M. Beggs. Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys. Rev. Lett. 94: 058101-1-4, 2005.

  157. Bunde, A., et al. Long-term memory: A natural mechanism for the clustering of extreme events and anomalous residual times in climate records. Phys. Rev. Lett. 94: 048701-1-4, 2005.

  158. Guimera, R. and L. A. N. Amaral. Functional cartography of complex metabolic networks. Nature 433: 895-900, 2005.

  159. Venegas, J. G., et al. Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature 434: 777-782, 2005.

  160. Yu, Y., et al. Preference of sensory neural coding for 1/f signals. Phys. Rev. Lett. 94: 108103-1-4, 2005.

  161. Smelyanskiy, V. N., et al. Inference of a nonlinear stochastic model of the cardiovascular interaction. Phys. Rev. Lett. 94: 098101-1-4, 2005.

  162. Roxiin, A., et al. Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks. Phys. Rev. Lett. 94: 238103-1-4, 2005.

  163. Copelli, M. and O. Kinouchi. Intensity coding in two-dimensional excitable neural networks. Physica A 349: 431-442, 2005.

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平成29年度「身体システム論」

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Yoshiharu Yamamoto 平成29年6月3日