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  • 1975-1979  (11)
  • 1
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    In:  J. Phys. Earth, Hokkaido University, Inst. f. Geophys., Ruhr-Univ. Bochum, vol. 23, no. B5, pp. 113-125, pp. B05306, (ISSN: 1340-4202)
    Publication Date: 1975
    Keywords: Elasticity theory of dislocations ; Dislocation ; Dip-slip ; Layers ; JPE
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  • 2
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    In:  J. Phys. Earth, Amsterdam, Schweizerbart'sche Verlagsbuchhandlung, vol. 27, no. 3, pp. 285-314, pp. L09302, (ISSN 0016-8548, ISBN 3-510-50045-8)
    Publication Date: 1979
    Keywords: Dislocation ; Elasticity theory of dislocations ; JPE
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  • 3
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    In:  J. Phys. Earth, Amsterdam, Univ. Tokyo, vol. 23, no. 1, pp. 43-61, pp. L14309, (ISSN: 1340-4202)
    Publication Date: 1975
    Keywords: Waves ; Source ; JPE
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  • 4
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    In:  J. Phys. Earth, Bonn, Inst. f. Theoret. Geodäsie, vol. 23, no. 5, pp. 1-29, pp. B01401, (ISSN: 1340-4202)
    Publication Date: 1975
    Keywords: Elasticity theory of dislocations ; Dislocation ; Matsuura ; JPE
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  • 5
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract The mechanisms by which excitatory and inhibitory input impulse sequences interact in changing the spike probability in neurons are examined in the two mathematical neuron models; one is a real-time neuron model which is close to physiological reality, and the other a stochastic automaton model for the temporal pattern discrimination proposed in the previous paper (Tsukada et al., 1976), which is developed in this paper as neuron models for interaction of excitatory and inhibitory input impulse sequences. The interval distributions of the output spike train from these models tend to be multimodal and are compared with those used for experimental data, reported by Bishop et al. (1964) for geniculate neuron activity and Poisson process deleting model analyzed by Ten Hoopen et al. (1966). Special attention, moreover, should be paid to how different forms of inhibitory input are transformed into the output interval distributions through these neuron models. These results exhibit a clear correlation between inhibitory input form and output interval distribution. More detailed information on this mechanism is obtained from the computations of recurrence-time under the stationary condition to go from active state to itself for the first time, each of which is influenced by the inhibitory input forms. In addition to these facts, some resultant characteristics on interval histogram and serial correlation are discussed in relation to physiological data from the literature.
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  • 6
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract The application of stochastic automata to the input-output relations of single neurons is considered. For this, some stochastic properties of temporal pattern discrimination in single synaptic cells are used to suggest stochastic automaton models. The models have only three possible states, the active, the absolute refractory and the relative refractory states, which are sufficient for temporal pattern sensitivity. From such an application, it was found that the temporal pattern discriminating structures in the models are similar to those used for experimental data and computer simulation (real-time neuron models). Extensions related to temporal pattern learning are discussed.
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  • 7
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract This paper deals with some properties of temporal pattern discrimination performed by single digital-computer simulated synaptic cells. To clarify these properties, the Shannon's entropy method which is a basic notion in the information theory and a fundamental approach for the design of pattern classification system was applied to input-output relations of the digital computer simulated synaptic cells. We used the average mutual information per symbol as a measure for the temporal pattern sensitivity of the nerve cells, and the average response entropy per symbol as a measure for the frequency transfer characteristics. To use these measures, the probability of a post-synaptic spike as a function of the recent history of pre-synaptic intervals was examined in detail. As the results of such application, it was found that the EPSP size is closely related to the pattern of impulse sequences of the input, and the average mutual information per symbol for EPSP size is given by a bimodal curve with two maximum values. One is a small EPSP size and the other is a large EPSP size. In two maximum points, the structure of the temporal pattern discrimination reverses each other. In addition, the relation between the mean frequency, or the form of impulse sequences of the input, and the average mutual information per symbol has been examined. The EPSP size at one maximum point of average mutual information is in inverse proportion to the magnitude of input mean frequency which relates to the convergence number of input terminal, while that at the other maximum point is proportional to that of the mean frequency. Moreover, the temporal pattern discrimination is affected remarkably by whether successive interspike intervals of the input are independent or not in the statistical sense. Computer experiments were performed by the semi-Markov processes with three typical types of transition matrixes and these shuffling processes. The average mutual informations in the cases of these semi-Markov processes are in contrast to those of the shuffling processes which provide a control case. The temporal structure of successive interspike intervals of the input is thus a significant factor in pattern discrimination at synaptic level.
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  • 8
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract This paper deals with the stochastic properties of the simplest neuron population consists of two neurons. Two modes of neural coupling are discussed. One is the forward inhibition mode [FI] and the other is the backward inhibition mode [BI]. In the forward inhibition mode, the two neurons are assumed to be independent of each other. In the backward inhibition mode, the two neurons interact, but the inputs to the neurons are assumed to be independent of each other. In the analysis, we first obtain the probability density function [p.d.f.] of the interspike intervals of the output impulse trains in FI and BI. Then we define the mean rate transfer function from the mean rate of these p.d.f.'s. Finally, by comparing our analytical results with the physiological experimental data, it is clear that the difference in the stochastic properties can be accounted for by the difference in the coupling mode (i.e. FI or BI).
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  • 9
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract The Shannon's information theory in multiway channels (Shannon, 1961) is applied to multi-input-output relations of the stochastic automaton models for interaction of excitatory and inhibitory impulse sequences proposed in the previous papers (Tsukada et al., 1977). In these models, the output spike train depends upon several statistical characteristics (mean frequency, standard deviation, form, order-dependence or order-independence, etc.) of the excitatory and inhibitory input spike trains. By the use of the multiple-access channel in information theory, some stochastic properties of temporal pattern discrimination in neurons are analyzed and discussed with biological systems.
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  • 10
    ISSN: 1420-9071
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Medicine
    Notes: Summary The 2nd c-wave is a new wave of corneoretinal potential which is an on-response with a long latency (65–98 sec), and appears following the end of the c-wave of ERG. It is sugested that the 2nd c-wave is based on the tail of the late receptor potential of the retina.
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