Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • 1975-1979  (4)
Source
Material
Years
Year
  • 1
    Electronic Resource
    Electronic Resource
    Temporal pattern discrimination of impulse sequences in the computer-simulated nerve cells (1975)
    Springer
    Biological cybernetics 17 (1975), S. 19-28 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00326706
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Statistical dependency as a measure to evaluate Markov properties of stochastic point processes (1975)
    Springer
    Biological cybernetics 18 (1975), S. 191-208 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Neuronal spike trains are regarded as stochastic point processes. To estimate the order and value of Markov processes of the interspike interval sequences with small number of samples, we have proposed a new measure of simplified statistical dependencyd m. This measure is derived from statistical dependencyd m (T=τ) in the case of Gaussian process, and is obtained by the standard deviation and the matrices of the serial correlation coefficients. Sinced m is a parametric measure, it is calculated from the interval sequence transformed into the aormal distribution. We designate this as normalized simplified statistical dependencyNd m. The order and value of the maintained spike sequences recorded from the mesencephalic reticular formation, red nucleus, optic tract, and lateral geniculate nucleus neurons in cats have been estimated. It is indicated that there is a considerable correspondence between the value ofNd m and that ofd m (T=τ). This suggests thatNd m is useful in practice to estimate the order and value of Markov process with small number of samples.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00326689
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Stochastic automaton models for the temporal pattern discrimination of nerve impulse sequences (1976)
    Springer
    Biological cybernetics 21 (1976), S. 121-130 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00337419
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Dependency as a measure to estimate the order and the values of Markov processes (1977)
    Springer
    Biological cybernetics 25 (1977), S. 209-226 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract To evaluate the order and the values of Markov properties of the time series of events, we have proposed a statistical measure “dependency”:D m = (H 0 −H m )/H 0 , whereH 0 andH m are Shannon's entropy and them-th order conditional entropy, respectively. It is indicated that $$\tilde D_m = \sum\limits_{v = 1}^m {(\hat D_v - \bar D_v^{sh} } )$$ is a better point estimator ofD m, giving a total value of them-th order Markov process. Here $$\hat D_m $$ and $$\bar D_m^{sh} $$ are the estimate ofD m and the arithmetic mean of $$\hat D_m^{sh} $$ when them-th order shuffling is made many times for a given observed series, respectively. The value $$\hat D_m - \bar D_m^{sh} = d_m $$ represents Markov value of the orderm. Under the assumption that the series has continuous variables and the normal distribution, simplified dependency is defined by , where |S m | is the determinant of serial correlation coefficients. It is shown that is practically useful for the estimation of the order and the values of Markov processes with small sample size. It is also indicated that analysis is basically equivalent to the least mean-square analysis of autoregressive models.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01885639
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...