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  • 1
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 381 (1996), S. 215-217 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] Consider the usual description of a dynamical system as a 'black box' with input xn and output yn, at time n = 1,... ,7V in multiples of the sampling time T. The discrete Volterra series is then a Taylor-like polynomial expansion of yn in terms of where K is the memory of the system. To overcome ...
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  • 2
    Electronic Resource
    Electronic Resource
    [s.l.] : Macmillan Magazines Ltd.
    Nature 389 (1997), S. 492-495 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] The electrical properties of the mammalian heart undergo many complex transitions in normal and diseased states. It has been proposed that the normal heartbeat may display complex nonlinear dynamics, including deterministic chaos,, and that such cardiac chaos may be a useful physiological ...
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  • 3
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 368 (1994), S. 297-298 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] SIR — Double jeopardy (DJ) is a per-vasive marketing phenomenon where a minor brand is often found to attract not only fewer buyers, but also ones that are less loyal. Ehrenberg1 takes the generality of DJ in many consumer settings as a first demonstration of the existence of empiri-cal ...
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 78 (1998), S. 79-86 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract. Many cognitive and sensorimotor functions in the brain involve parallel and modular memory subsystems that are adapted by activity-dependent Hebbian synaptic plasticity. This is in contrast to the multilayer perceptron model of supervised learning where sensory information is presumed to be integrated by a common pool of hidden units through backpropagation learning. Here we show that Hebbian learning in parallel and modular memories is more advantageous than backpropagation learning in lumped memories in two respects: it is computationally much more efficient and structurally much simpler to implement with biological neurons. Accordingly, we propose a more biologically relevant neural network model, called a tree-like perceptron, which is a simple modification of the multilayer perceptron model to account for the general neural architecture, neuronal specificity, and synaptic learning rule in the brain. The model features a parallel and modular architecture in which adaptation of the input-to-hidden connection follows either a Hebbian or anti-Hebbian rule depending on whether the hidden units are excitatory or inhibitory, respectively. The proposed parallel and modular architecture and implicit interplay between the types of synaptic plasticity and neuronal specificity are exhibited by some neocortical and cerebellar systems.
    Type of Medium: Electronic Resource
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  • 5
    ISSN: 1573-9686
    Keywords: Enforced smoothing ; Nonlinear parametric estimation ; Pulmonary shunt ; Pulmonary dead space ; Pulmonary gas exchange
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Technology
    Notes: Abstract The resolving powers of the enforced smoothing and log-normal parametric estimation techniques in recovering ventilation/perfusion ratio $$\dot V_A /\dot Q$$ distributions were evaluated using noisy inert gas elimination data simulated from hypothetical distribution functions representing various degrees of heterogeneity. The resolving powers were assessed in terms of the statistical recoverabilities of the shunt, dead space, modality, and modal moments characterizing the perfusion distribution. For all distributions tested, both modal mean and shunt were estimated by either technique with sufficient accuracies. Modal dispersions (σ) were consistently overestimated by up to 0.15 decade for narrow distributions, but the mean errors became negligible for σ greater than 0.2 decade. As compared with the shunt, the dead space estimates were more variable and biased, probably due to their indirect estimation from the perfusion distribution, which was imperfectly recovered. Both broad unimodal and widely separated bimodal or trimodal distributions (σ〉0.6 decade) were recovered as bimodal distributions of similar forms, so that detection of modality was difficult. The recoveries by both techniques were comparable in most cases studied, except that parametric estimation generally tended to be more sensitive to measurement errors and was computationally less efficient. These results provide a useful basis for the interpretation of $$\dot V_A /\dot Q$$ distributions obtained from empirical inert gas data.
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of computational neuroscience 5 (1998), S. 35-51 
    ISSN: 1573-6873
    Keywords: nonlinear dynamics ; respiratory rhythmogenesis ; apnea ; pacemaker ; half-center oscillator ; periodic forcing ; in-phase inputs ; out-phase inputs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Medicine , Physics
    Notes: Abstract We studied the dynamical behavior of a class of compound central pattern generator (CPG) models consisting of a simple neural network oscillator driven by both constant and periodic inputs of varying amplitudes, frequencies, and phases. We focused on a specific oscillator composed of two mutually inhibiting types of neuron (inspiratory and expiratory neurons) that may be considered as a minimal model of the mammalian respiratory rhythm generator. The simulation results demonstrated how a simple CPG model— with a minimum number of neurons and mild nonlinearities— may reproduce a host of complex dynamical behaviors under various periodic inputs. In particular, the network oscillated spontaneously only when both neurons received adequate and proportionate constant excitations. In the presence of a periodic source, the spontaneous rhythm was overriden by an entrained oscillation of varying forms depending on the nature of the source. Stable entrained oscillations were inducible by two types of inputs: (1) anti-phase periodic inputs with alternating agonist-antagonist drives to both neurons and (2) a single periodic drive to only one of the neurons. In-phase inputs, which exert periodic drives of similar magnitude and phase relationships to both neurons, resulted in varying disruptions of the entrained oscillations including magnitude attenuation, harmonic and phase distortions, and quasi-periodic interference. In the absence of significant phasic feedback, chaotic motion developed only when the CPG was driven by multiple periodic inputs. Apneic episodes with repetitive alternation of active (intrinsic oscillation) and inactive (cessation of oscillation) states developed when the network was driven by a moderate periodic input of low frequency. %and amplitudes of intermediate strength, Similar results were demonstrated in other, more complex oscillator models (that is, half-center oscillator and three-phase respiratory network model). These theoretical results may have important implications in elucidating the mechanisms of rhythmogenesis in the mature and developing respiratory CPG as well as other compound CPGs in mammalian and invertebrate nervous systems.
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Annals of biomedical engineering 21 (1993), S. 501-508 
    ISSN: 1573-9686
    Keywords: Respiration ; Muscular exercise ; Associative learning ; Hebbian synapse ; Adaptation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Technology
    Notes: Abstract An adaptive neural network model that exhibits the optimality and homeostasis characteristics of the respiratory control system is described. Based upon the Hopfield network structure and a postulated Hebb-like respiratory synapse with correlational short-term potentiation, the model is capable of mimicking the normal ventilatory responses to exercise and CO2 inputs without the need for an explicit exercise stimulus. Results suggest the possibility of an adaptive neuronal mechanism that effects optimal homeostatic regulation of respiration in mammals.
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  • 8
    Publication Date: 1998-02-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 9
    Publication Date: 2010-08-01
    Print ISSN: 0925-4005
    Electronic ISSN: 1873-3077
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Elsevier
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  • 10
    Publication Date: 1997-10-01
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Springer Nature
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