Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement
Patent
1999-06-04
2000-10-17
Hoff, Marc S.
Data processing: measuring, calibrating, or testing
Measurement system
Statistical measurement
702 89, 702181, G06F 1718, G06F10114
Patent
active
061345105
DESCRIPTION:
BRIEF SUMMARY
BACKGROUND OF THE INVENTION
The invention relates to a method for detecting synchronicity between several digital measurement series with the aid of a computer.
When presented with measurement data given by time series, which for example originate from different neurons, then it is of great interest to know with what probability synchronicity occurs between the various possible combinations of the binary signals of the time series.
FIG. 1 serves as an illustration. It shows three time series ZR1, ZR2 and ZR3 by way of example. These time series may for example originate from different neurons or, in general, represent arbitrary binary frequency data. The order O is defined as being how many times series are taken into account for the synchronicity. In FIG. 1, the order O is equal to 3. For all possible combinations of the 3.sup.rd order binary groupings occurring in the example, a probability of synchronicity should thus in each case be given. Thus, FIG. 1 indicates by way of example a combination Pat1 (011) which occurs two times.
The reference by T. M. Cover, "Elements of Information Theory, John Wiley & Sons, 1976, pp. 18-23, ISBN 0-471-06259-6, discloses a method for determining a discriminating statistic.
SUMMARY OF THE INVENTION
The object of the invention is to provide a method for detecting synchronicity between several digital measurement series, the method being suitable in particular for detecting neuronal coding patterns in the brain.
The method formulates a nonparametric statistical approach for detecting redundancy between several digital measurement series. A relevant concrete example involves the synchronous firing of several neurons. The synchronicity is detected by measuring a discriminating significance, which tests a null hypothesis of independent firing with a surrogate method. The surrogate data used correspond to the null hypothesis of a noncausal relation between the firing or the nonfiring of several neuron groups. Rejection of the null hypothesis is based on the calculation of the discriminating statistic .LAMBDA. of the original data record and of the discriminating statistic D.sub.si of the i-th surrogate data record which has been produced by making the null hypothesis.
As mentioned above, one refinement example of the method according to the invention may be the firing of several neurons, the number of which determines the order O, lying at the basis of the measurement series. The method according to the invention can therefore be used to solve the difficult problem of detecting neuronal coding patterns in the brain.
BRIEF DESCRIPTION OF THE DRAWINGS
The features of the present invention which are believed to be novel, are set forth with particularity in the appended claims. The invention, together with further objects and advantages, may best be understood by reference to the following description taken in conjunction with the accompanying drawings, in the several Figures of which like reference numerals identify like elements, and in which:
FIG. 1 shows illustrative measurement data of various time series;
FIG. 2 shows a block diagram which represents the steps of the method according to the invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
FIG. 1 indicates three different time series ZR1, ZR2 and ZR3 by way of example.
Generally, the order O is determined by the maximum number of "1s" occurring. In relation to an application example, the "1s" may be interpreted as the firing of a neuron. There are generally 2.sup.O different combinations in the case of O binary measurement series. In the example, the order is equal to three and there are eight different binary combinations. This example which is represented in FIG. 1 is continued in FIG. 2 in order to explain the method according to the invention.
FIG. 2 represents a block diagram which contains the individual steps of the method according to the invention. With the aid of different measurement series, which may originate from neurons, a real occurrence probability for each different binary combination is calcul
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Laura Martignon et al, "Learning exact patterns of quasi-synchronization among spiking neurons from data on multi-unit recordings" Proceedings of the 1996 Conference, Advances in Neural Information Processing Systems 9,ISBN 0-262-10065-7, pp. 76-82.
Gustavo Deco et al, "Training Data Selection by Detecting Predictability in Non-Stationary Time Series by a Surrogate-Cumulant Based Approach", Proceedings International Workshop on Neural Networks for Identification Control, Robotics, and Signal/Image Processing, Aug. 1996, XP000676844, pp. 11-19.
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T. M. Cover, Elements of Information Theory, John Wiley & sons, 1976, pp. 18-23, ISBN 0-471-06259-6.
Deco Gustavo
Martignon Laura
Bui Bryan
Hoff Marc S.
Max-Planck-Gesellschaft zur Forderung de Wissenschaften
Siemens Aktiengesellschaft
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