References - Advanced Models of Neural Networks - page 261

43. G. Deco, E.T. Rolls, Attention, short-memory and action selection: a unifying theory. Prog.
Neurobiol. 76 , 236-256 (2005)
44. G. Deco, D. Marti, A. Ledberg, R. Reig, M.V. Sanchez Vives, Effective reduced diffusion-
models: A data driven approach to the analysis of neuronal dynamics. PLoS Comput. Biol.
5 (12), e1000587 (2009)
45. G. Deco, E.T. Rolls, R. Romo, Stochastic dynamics as a principle of brain function. Prog.
Neurobiol. 88 , 1-16 (2009)
46. M.A. Demetriou, Design of consensus and adaptive consensus filters for distributed parameter
systems. Automatica 46 , 300-311 (2010)
47. M.J. Denham, Characterising neural representation in terms of the dynamics of cell membrane
potential activity: a control theoretic approach using differential geometry. Biosystems 79 ,
53-60 (2005)
48. D. Deutsch, Quantum computational networks. Proc. R. Soc. Lond. A 425 , 73 (1989)
49. R.O. Dorak, Control of repetitive firing in Hodgkin-Huxley nerve fibers using electric fields.
Chaos Solitons Fract. 52 , 66-72 (2013)
50. H. Duan, C. Cai, C. Han, Y. Che, Bifurcation control in Morris-Lecar neuron model via
washout filter with a linear term based on filter-aided dynamic feedback. Adv. Mater. Res.
485 , 600-603 (2012)
51. D. Dubois, L. Foulloy, G. Mauris and H. Prade, Probability-possibility transformations,
triangular fuzzy sets and probabilistic inequalities. Reliab. Comput. 10 (4), 273-297 (2004)
52. M. Duflo, Algorithmes Stochastiques . Mathématiques et Applications, vol. 23 (Springer,
Berlin, 1996)
53. K.L. Eckel-Mahan, Cirdadian oscillations within the hippocampus support memory formation
and persistence. Front. Mol. Neurosci. 5 , 46 (2012)
54. D.V. Efimov, A.L. Fradkov, Adaptive tuning to bifurcation for time-varying nonlinear
systems. Automatica 42 , 417-425 (2006)
55. R.T. Faghih, K. Savla, M.A. Dahleh, E.N. Brown, The FitzHugh-Nagumo model: Firing
modes with time-varying parameters and parameter estimation, in IEEE 32nd International
Conference of the Engineering in Biology and Medicine Society , Buenos Aires, 2010
56. W.G. Faris, Diffusion, Quantum Theory, and Radically Elementary Mathematics (Princeton
University Press, Princeton, 2006)
57. M. Fliess, Probabilités et fluctuations quantiques. Comp. Rendus Math. (Phys. Math.), C. R.
Acad. Sci. Paris 344 , 663-668 (2007)
58. M. Fliess, H. Mounier, Tracking control and -freeness of infinite dimensional linear systems,
in Dynamical Systems, Control, Coding and Computer Vision , vol. 258, ed. by G. Picci,
D.S. Gilliam (Birkhaüser, Boston, 1999), pp. 41-68
59. R. Feynman, Quantum mechanical computers. Found. Phys. 16 , 507-531 (1986)
60. R.F. Fox, Y. Lu, Emergent collective behavior in large numbers of globally coupled
independently stochastic ion channels. Phys. Rev. E 49 (4), 3421-3431 (1994)
61. P. Francois, A model for the neurospora circadian clock. Biophys. J. 88 , 2369-2383 (2005)
62. Y. Fukuoka, H. Kimura, A. Cohen, Adaptive dynamic walking of a quadruped robot on
irregular terrain based on biological concepts. Int. J. Robot. Res. 3-4 , 187-202 (2003)
63. H. Fujii and I. Tsuda, Neocortical gap junction coupled interneuron systems may induce
chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony. Neurocom-
puting 58-60 , 151-157 (2004)
64. V. Gazi, K. Passino, Stability analysis of social foraging swarms. IEEE Trans. Syst. Man
Cybern. B Cybern. 34 , 539-557 (2004)
65. W. Gerstner, W.M. Kistler, Spiking Neuron Models: Simple Neurons, Populations, Plasticity
(Cambridge University Press, Cambridge, 2002)
66. M. Gitterman, The Noisy Oscillator: The First Hundred Years, From Einstein Until Now
(World Scientific, Singapore, 2005)
67. D. Gonze, J.C. Leloup, A. Goldbeter, Theoretical models for circadian rhythms in Neurospora
and Drosophila. C. R. Acad. Sci. III 323 , 57-67 (2000)