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  1.  19
    On the Existence and the Role of Chaotic Processes in the Nervous System.B. Doyon - 1992 - Acta Biotheoretica 40 (2-3):113-119.
    Chaos theory is a rapidly growing field. As a technical term, chaos refers to deterministic but unpredictable processes being sensitively dependent upon initial conditions. Neurobiological models and experimental results are very complicated and some research groups have tried to pursue the neuronal chaos. Babloyantz's group has studied the fractal dimension (d) of electroencephalograms (EEG) in various physiological and pathological states. From deep sleep (d=4) to full awakening (d>8), a hierarchy of strange attractors paralles the hierarchy of states of consciousness. In (...)
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  2.  27
    Mean-Field Equations, Bifurcation Map and Chaos in Discrete Time, Continuous State, Random Neural Networks.B. Doyon, B. Cessac, M. Quoy & M. Samuelides - 1995 - Acta Biotheoretica 43 (1-2):169-175.
    The dynamical behaviour of a very general model of neural networks with random asymmetric synaptic weights is investigated in the presence of random thresholds. Using mean-field equations, the bifurcations of the fixed points and the change of regime when varying control parameters are established. Different areas with various regimes are defined in the parameter space. Chaos arises generically by a quasi-periodicity route.
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  3.  15
    Preface.B. Doyon - 1994 - Acta Biotheoretica 42 (2-3):iii-iii.
  4.  26
    On Bifurcations and Chaos in Random Neural Networks.B. Doyon, B. Cessac, M. Quoy & M. Samuelides - 1994 - Acta Biotheoretica 42 (2-3):215-225.
    Chaos in nervous system is a fascinating but controversial field of investigation. To approach the role of chaos in the real brain, we theoretically and numerically investigate the occurrence of chaos inartificial neural networks. Most of the time, recurrent networks (with feedbacks) are fully connected. This architecture being not biologically plausible, the occurrence of chaos is studied here for a randomly diluted architecture. By normalizing the variance of synaptic weights, we produce a bifurcation parameter, dependent on this variance and on (...)
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