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BACKGROUND Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing(More)
The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent [Formula: see text]. Models at criticality have been employed to mimic avalanche propagation and explain the statistics(More)
We study learning from single presentation of examples (incremental or on-line learning) in single-layer perceptrons and tree committee machines (TCMs). Lower bounds for the perceptron generalization error as a function of the noise level in the teacher output are calculated. We nd that optimal local learning in a TCM with K hidden units is simply related(More)
We study the transient regime of type-II biophysical neuron models and determine the scaling behavior of relaxation times tau near but below the repetitive firing critical current, tau approximately or equal to C(I(c)-I)(-Delta). For both the Hodgkin-Huxley and Morris-Lecar models we find that the critical exponent is independent of the numerical(More)
BACKGROUND Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be(More)
Sensory arrays made of coupled excitable elements can improve both their input sensitivity and dynamic range due to collective nonlinear wave properties. This mechanism is studied in a neural network of electrically coupled (e.g., via gap junctions) elements subject to a Poisson signal process. The network response interpolates between a Weber-Fechner(More)
We consider the signal compression problem in the peripheral nervous system. We propose a simple mechanism by which the narrow dynamical range observed experimentally in isolated sensory neurons translates into a wide dynamical range as a result of excitable waves and synchronization phenomena which provides self-limited amplification. The mechanism is(More)
We study the response properties of d-dimensional hypercubic excitable networks to a stochastic stimulus. Each site, modeled either by a three-state stochastic susceptible-infected-recovered-susceptible system or by the probabilistic Greenberg-Hastings cellular automaton, is continuously and independently stimulated by an external Poisson rate h. The(More)