Estimation of Parameters in Shot-Noise- Driven Doubly Stochastic Poisson Processes Using the EM Algorithm

H. Mino

doi:10.1055/s-0038-1625397

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(02): 151-154
DOI: 10.1055/s-0038-1625397

Original Article

Schattauer GmbH

Estimation of Parameters in Shot-Noise- Driven Doubly Stochastic Poisson Processes Using the EM Algorithm

Modeling of Pre- and Postsynaptic Spike Trains

Authors

H. Mino

¹Department of Electrical and Computer Engineering, Kanto Gakuin University, Yokohama, Japan

Abstract

Summary

Objectives : To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise- Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs.

Methods : An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm.

Results : The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions.

Conclusions : The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

Keywords

Random point process - EM algorithm - computer simulation - fluctuation - neural spike trains

Full Text

References

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