Inverse Density Modulation of Multiple Markov Sequences of Interpoint Intervals

Torao YANARU; Yasuyuki ISO

Inverse Density Modulation of Multiple Markov Sequences of Interpoint Intervals

Torao YANARU, Yasuyuki ISO

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Multiple Markov sequences of interpoint intervals are generated by computer simulation. A kind of rational transition probability density function presented here is found to be satisfactory to simulate wide varieties of neuronal spike trains. Three examples of the Markov sequences are examined and investigated from a viewpoint of point density, two of which are to simulate the actual spike trains obtained from the central neurons of the cat. The third one has exponential type interval histogram. The generated Markov sequences as well as the neuronal spike trains usually show slow fluctuations of point densities. When a point sequence of this kind is smoothed by an appropriate filter, the approximate point density fluctuation is extracted. By an inverse procedure of integral density modulation with this point density as the modulating signal, the point sequence is transformed into another sequence of which the slow component of point density is eliminated. It is found that the transformed point sequence can be approximately regarded as a non-correlative sequence, if a right kind of filter is selected. This shows that both the simulated Markov sequences and the neuronal sequences tested here are almost regarded as the density modulated point sequences of the non-correlative sequences of intervals.

Publication: IEICE TRANSACTIONS on transactions Vol.E61-E No.10 pp.796-803

Publication Date: 1978/10/25

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Type of Manuscript: PAPER

Category: Communication Theory

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Torao YANARU
Yasuyuki ISO

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Torao YANARU, Yasuyuki ISO, "Inverse Density Modulation of Multiple Markov Sequences of Interpoint Intervals" in IEICE TRANSACTIONS on transactions, vol. E61-E, no. 10, pp. 796-803, October 1978, doi: .
Abstract: Multiple Markov sequences of interpoint intervals are generated by computer simulation. A kind of rational transition probability density function presented here is found to be satisfactory to simulate wide varieties of neuronal spike trains. Three examples of the Markov sequences are examined and investigated from a viewpoint of point density, two of which are to simulate the actual spike trains obtained from the central neurons of the cat. The third one has exponential type interval histogram. The generated Markov sequences as well as the neuronal spike trains usually show slow fluctuations of point densities. When a point sequence of this kind is smoothed by an appropriate filter, the approximate point density fluctuation is extracted. By an inverse procedure of integral density modulation with this point density as the modulating signal, the point sequence is transformed into another sequence of which the slow component of point density is eliminated. It is found that the transformed point sequence can be approximately regarded as a non-correlative sequence, if a right kind of filter is selected. This shows that both the simulated Markov sequences and the neuronal sequences tested here are almost regarded as the density modulated point sequences of the non-correlative sequences of intervals.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e61-e_10_796/_p

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@ARTICLE{e61-e_10_796,
author={Torao YANARU, Yasuyuki ISO, },
journal={IEICE TRANSACTIONS on transactions},
title={Inverse Density Modulation of Multiple Markov Sequences of Interpoint Intervals},
year={1978},
volume={E61-E},
number={10},
pages={796-803},
abstract={Multiple Markov sequences of interpoint intervals are generated by computer simulation. A kind of rational transition probability density function presented here is found to be satisfactory to simulate wide varieties of neuronal spike trains. Three examples of the Markov sequences are examined and investigated from a viewpoint of point density, two of which are to simulate the actual spike trains obtained from the central neurons of the cat. The third one has exponential type interval histogram. The generated Markov sequences as well as the neuronal spike trains usually show slow fluctuations of point densities. When a point sequence of this kind is smoothed by an appropriate filter, the approximate point density fluctuation is extracted. By an inverse procedure of integral density modulation with this point density as the modulating signal, the point sequence is transformed into another sequence of which the slow component of point density is eliminated. It is found that the transformed point sequence can be approximately regarded as a non-correlative sequence, if a right kind of filter is selected. This shows that both the simulated Markov sequences and the neuronal sequences tested here are almost regarded as the density modulated point sequences of the non-correlative sequences of intervals.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - Inverse Density Modulation of Multiple Markov Sequences of Interpoint Intervals
T2 - IEICE TRANSACTIONS on transactions
SP - 796
EP - 803
AU - Torao YANARU
AU - Yasuyuki ISO
PY - 1978
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E61-E
IS - 10
JA - IEICE TRANSACTIONS on transactions
Y1 - October 1978
AB - Multiple Markov sequences of interpoint intervals are generated by computer simulation. A kind of rational transition probability density function presented here is found to be satisfactory to simulate wide varieties of neuronal spike trains. Three examples of the Markov sequences are examined and investigated from a viewpoint of point density, two of which are to simulate the actual spike trains obtained from the central neurons of the cat. The third one has exponential type interval histogram. The generated Markov sequences as well as the neuronal spike trains usually show slow fluctuations of point densities. When a point sequence of this kind is smoothed by an appropriate filter, the approximate point density fluctuation is extracted. By an inverse procedure of integral density modulation with this point density as the modulating signal, the point sequence is transformed into another sequence of which the slow component of point density is eliminated. It is found that the transformed point sequence can be approximately regarded as a non-correlative sequence, if a right kind of filter is selected. This shows that both the simulated Markov sequences and the neuronal sequences tested here are almost regarded as the density modulated point sequences of the non-correlative sequences of intervals.
ER -