Information Technology Reference
In-Depth Information
68. D. Gonze, J. Halloy, J.C. Leloup, A. Goldbeter, Stochastic models for circadian rhythmes:
effect of molecular noise on periodic and chaotic behaviour. C. R. Biol.
326
, 189-203 (2003)
69. B.F. Grewe, D. Langer, H. Kasper, B.M. Kampa, F. Helmchen, High-speed in vivo calcium
imaging reveals neuronal network activity with near-millisecond precision. Nat. Methods
7
(5), 399-405 (2010)
70. S. Grillner, Biological pattern generation: the cellular and computational logic of networks in
motion. Neuron
52
, 751-766 (2006)
71. K. Gröchenig, D. Han, C. Heil, G. Kutyniak, The Balian-Low theorem for symplectic lattices
in higher dimensions. Appl. Comput. Harmon. Anal.
13
, 169-176 (2002)
72. B.Z. Guo, C.Z. Xu, H. Hammouri, Output feedback stabilization of a one-dimensional
wave equation with an arbitrary time-delay in boundary observation, in
ESAIM: Control,
Optimization and Calculus of Variations
, vol. 18, pp. 22-25, 2012
73. S. Hagan, S.R. Hameroff, J.A. Tuzyinski,
Quantum Computation in Brain Microtubules:
Decoherence and Biological Feasibility
. Phys. Rev. E
65
, 1-11 (2002)
74. G. Haine, Observateurs en dimension infinie. Application à l étude de quelques problèmes
inverses, Thèse de doctorat, Institut Elie Cartan Nancy, 2012.
75. L.M. Harisson, K. David, K.J. Friston, Stochastic models of neuronal dynamics. Phil. Tran.
R. Soc. B
360
, 1075-1091 (2005)
76. R. Haschke, J.J. Steil, Input-space bifurcation manifolds of recurrent neural networks.
Neurocomputing
64
, 25-38 (2005)
77. M. Havlicek, K.J. Friston, J. Jan, M. Brazdil, V.D. Calhoun. Dynamic modeling of neuronal
responses in fMRI using cubature Kalman Filtering. Neuroimage
56
, 2109-2128 (2011)
78. S. Haykin,
Neural Networks: A Comprehensive Foundation
(McMillan, New York, 1994)
79. R. Héliot, B. Espiau, Multisensor input for CPG-based sensory-motor coordination. IEEE
Trans. Robot.
24
, 191-195 (2008)
80. T. Heimburg, A.D. Jackson, On soliton propagation in biomembranes and nerves. Proc. Natl.
Acad. Sci.
102
(28), 9790-9795 (2005)
81. Z. Hidayat, R. Babuska, B. de Schutter, A. Nunez, Decentralized Kalman Filter comparison
for distributed parameter systems: a case study for a 1D heat conduction process, in
Proceedings of the 16th IEEE International Conference on Emerging Technologies and
Factory Automatio (ETFA 2011)
, Toulouse, 2011
82. J.J. Hopfield, Neural networks as physical systems with emergent computational abilities.
Proc. Natl. Acad. Sci. USA
79
, 2444-2558 (1982)
83. S. Hu, X. Liao, X. Mao, Stochastic Hopfield neural networks. J. Phys. A Math. Gen.
35
, 1-15
(2003)
84. J.H. Huggins, L. Paninski, Optimal experimental design for sampling voltage on dendritic
trees in the low-SNR regime. J. Comput. Neurosci.
32
(2), 347-66 (2012)
85. A.J. Ijspeert, Central pattern generator for locomotion control in animals and robots: a review.
Neural Netw.
21
, 642-653 (2008)
86. V.G. Ivancevic, T.T. Ivancevic,
Quantum Neural Computation
(Springer, Netherlands, 2010)
87. I. Iwasaki, H. Nakasima, T. Shimizu, Interacting Brownian particles as a model of neural
network. Int. J. Bifurcat. Chaos
8
(4), 791-797 (1998)
88. B.S. Jackson, Including long-range dependence in integrate-and-fire models of the high
interspike-interval variability of the cortical neurons. Neural Comput.
16
, 2125-2195 (2004)
89. P. Jaming, A.M. Powell, Uncertainty principles for orthonormal sequences. J. Funct. Anal.
243
, 611-630 (2007)
90. F. Jedrzejewski,
Modèles aléatoires et physique probabiliste
(Springer, Paris, 2009)
91. Y. Katory, Y. Otsubo, M. Okada, K. Aihara, Stability analysis of associative memory network
composed of stochastic neurons and dynamic synapses. Front. Comput. Neurosci.
7
, 6 (2013)
92. H. Khalil,
Nonlinear Systems
(Prentice Hall, Englewood Cliffs, 1996)
93. F.C. Klebaner,
Introduction to Stochastic Calculus with Applications
(Imperial College Press,
London, 2005)
94. M. Kolobov,
Quantum Imaging
(Springer, New York, 2007)
