Estimating nonlinear spatiotemporal membrane dynamics in active dendrites

Toshiaki Omori

doi:10.1007/978-3-319-12637-1_4

Estimating nonlinear spatiotemporal membrane dynamics in active dendrites

Toshiaki Omori

SCI - Mathematics

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Recent advances in measurement technology enables us to obtain spatotemporal data from neural systems as imaging data. In this study, we propose a statistical method to estimate nonlinear spatiotemporal membrane dynamics of active dendrites. We formulate generalized state space model of active dendrite, based on multi-compartment model. Membrane dynamics and its underlying electrical properties are simultaneously estimated by using sequential Monte-Carlo method and EM algorithm. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendritic can be extracted from partially observable data.

Original language	English
Pages (from-to)	27-34
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8834
DOIs	https://doi.org/10.1007/978-3-319-12637-1_4
Publication status	Published - 2014

Keywords

Dendrite
Multi-compartment model
Probabilistic time-series analysis
Spatiotemporal dynamics

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-319-12637-1_4

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title = "Estimating nonlinear spatiotemporal membrane dynamics in active dendrites",

abstract = "Recent advances in measurement technology enables us to obtain spatotemporal data from neural systems as imaging data. In this study, we propose a statistical method to estimate nonlinear spatiotemporal membrane dynamics of active dendrites. We formulate generalized state space model of active dendrite, based on multi-compartment model. Membrane dynamics and its underlying electrical properties are simultaneously estimated by using sequential Monte-Carlo method and EM algorithm. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendritic can be extracted from partially observable data.",

keywords = "Dendrite, Multi-compartment model, Probabilistic time-series analysis, Spatiotemporal dynamics",

author = "Toshiaki Omori",

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year = "2014",

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AU - Omori, Toshiaki

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - Recent advances in measurement technology enables us to obtain spatotemporal data from neural systems as imaging data. In this study, we propose a statistical method to estimate nonlinear spatiotemporal membrane dynamics of active dendrites. We formulate generalized state space model of active dendrite, based on multi-compartment model. Membrane dynamics and its underlying electrical properties are simultaneously estimated by using sequential Monte-Carlo method and EM algorithm. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendritic can be extracted from partially observable data.

AB - Recent advances in measurement technology enables us to obtain spatotemporal data from neural systems as imaging data. In this study, we propose a statistical method to estimate nonlinear spatiotemporal membrane dynamics of active dendrites. We formulate generalized state space model of active dendrite, based on multi-compartment model. Membrane dynamics and its underlying electrical properties are simultaneously estimated by using sequential Monte-Carlo method and EM algorithm. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendritic can be extracted from partially observable data.

KW - Dendrite

KW - Multi-compartment model

KW - Probabilistic time-series analysis

KW - Spatiotemporal dynamics

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SN - 0302-9743

VL - 8834

SP - 27

EP - 34

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

ER -

Estimating nonlinear spatiotemporal membrane dynamics in active dendrites

Abstract

Keywords

ASJC Scopus subject areas

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