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Order reduction for a signaling pathway model of neuronal synaptic plasticity

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Order reduction for a signaling pathway model of neuronal synaptic plasticity. / Lehtimäki, Mikko; Paunonen, Lassi; Pohjolainen, Seppo; Linne, Marja-Leena.

20th IFAC World Congress. IFAC, 2017. s. 7687-7692 (IFAC-PapersOnLine; Vuosikerta 50).

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Bibtex - Lataa

@inproceedings{c80f9772911542fa93e29d8dfba5fb72,
title = "Order reduction for a signaling pathway model of neuronal synaptic plasticity",
abstract = "In this study a nonlinear mathematical model of plasticity in the brain is reduced using the Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. Such methods are remarkably useful for connecting reduced small scale models via the inputs and outputs to form optimally performing large scale models. Novel results were obtained as mathematical model order reduction has not been applied in neuroscience without linearization of the mathematical model and never to the model presented here. The reduced order model consumes considerably less computational resources than the original while maintaining a low root mean square error between the original and reduced model.",
keywords = "cell signaling, Discrete Empirical Interpolation Method, model reduction, nonlinear models, Proper Orthogonal Decomposition, synaptic plasticity",
author = "Mikko Lehtim{\"a}ki and Lassi Paunonen and Seppo Pohjolainen and Marja-Leena Linne",
note = "jufoid=55187 INT=mat,{"}Lehtim{\"a}ki, Mikko{"}",
year = "2017",
month = "7",
day = "1",
doi = "10.1016/j.ifacol.2017.08.1143",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "IFAC",
pages = "7687--7692",
booktitle = "20th IFAC World Congress",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Order reduction for a signaling pathway model of neuronal synaptic plasticity

AU - Lehtimäki, Mikko

AU - Paunonen, Lassi

AU - Pohjolainen, Seppo

AU - Linne, Marja-Leena

N1 - jufoid=55187 INT=mat,"Lehtimäki, Mikko"

PY - 2017/7/1

Y1 - 2017/7/1

N2 - In this study a nonlinear mathematical model of plasticity in the brain is reduced using the Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. Such methods are remarkably useful for connecting reduced small scale models via the inputs and outputs to form optimally performing large scale models. Novel results were obtained as mathematical model order reduction has not been applied in neuroscience without linearization of the mathematical model and never to the model presented here. The reduced order model consumes considerably less computational resources than the original while maintaining a low root mean square error between the original and reduced model.

AB - In this study a nonlinear mathematical model of plasticity in the brain is reduced using the Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. Such methods are remarkably useful for connecting reduced small scale models via the inputs and outputs to form optimally performing large scale models. Novel results were obtained as mathematical model order reduction has not been applied in neuroscience without linearization of the mathematical model and never to the model presented here. The reduced order model consumes considerably less computational resources than the original while maintaining a low root mean square error between the original and reduced model.

KW - cell signaling

KW - Discrete Empirical Interpolation Method

KW - model reduction

KW - nonlinear models

KW - Proper Orthogonal Decomposition

KW - synaptic plasticity

U2 - 10.1016/j.ifacol.2017.08.1143

DO - 10.1016/j.ifacol.2017.08.1143

M3 - Conference contribution

T3 - IFAC-PapersOnLine

SP - 7687

EP - 7692

BT - 20th IFAC World Congress

PB - IFAC

ER -