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Deutsches Institut für Urbanistik
Oldenbourg Wissenschaftsverlag
Walter de Gruyter
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H. Mino

Estimation of Parameters in Shot-Noise-Driven Doubly Stochastic Poisson Processes Using the EM Algorithm Modeling of Pre- and Postsynaptic Spike Trains

Keywords: Random point process, EM algorithm, computer simulation, fluctuation, neural spike trains

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.

Methods of Information in Medicine, Schattauer

Print ISSN: 0026-1270
Volume: 46, 01/2007
Pages: 151 - 154

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