TY - JOUR
T1 - Reduced model and simulation of neuron with passive dendritic cable
T2 - An eigenfunction expansion approach
AU - Woo, Bomje
AU - Shin, Donggyun
AU - Yang, Daeryook
AU - Choi, Jinhoon
N1 - Funding Information:
This work is supported by the Korea Science and Engineering Foundation. The authors are very grateful to the anonymous reviewers for many decisive comments that lead to marked improvement of the manuscript.
PY - 2005/12
Y1 - 2005/12
N2 - The neuron models with passive dendritic cables are often used for detailed cortical network simulations (Protopapas et al., 1998; Suarez et al., 1995). For this, the compartment model based on finite volume or finite difference discretization was used. In this paper, we propose an eigenfunction expansion approach combined with singular perturbation and demonstrate that the proposed scheme can achieve an order of magnitude accuracy improvement with the same number of equations. Moreover, it is also shown that the proposed scheme converges much faster to attain a given accuracy. Hence, for a network simulation of the neurons with passive dendritic cables, the proposed scheme can be an attractive alternative to the compartment model, that leads to a low order model with much higher accuracy or that converges faster for a given accuracy.
AB - The neuron models with passive dendritic cables are often used for detailed cortical network simulations (Protopapas et al., 1998; Suarez et al., 1995). For this, the compartment model based on finite volume or finite difference discretization was used. In this paper, we propose an eigenfunction expansion approach combined with singular perturbation and demonstrate that the proposed scheme can achieve an order of magnitude accuracy improvement with the same number of equations. Moreover, it is also shown that the proposed scheme converges much faster to attain a given accuracy. Hence, for a network simulation of the neurons with passive dendritic cables, the proposed scheme can be an attractive alternative to the compartment model, that leads to a low order model with much higher accuracy or that converges faster for a given accuracy.
KW - Eigenfunction expansion
KW - Neuron model with passive dendritic cables
KW - Reduced model
KW - Singular perturbation
UR - http://www.scopus.com/inward/record.url?scp=33644517243&partnerID=8YFLogxK
U2 - 10.1007/s10827-005-3284-5
DO - 10.1007/s10827-005-3284-5
M3 - Article
C2 - 16284711
AN - SCOPUS:33644517243
SN - 0929-5313
VL - 19
SP - 379
EP - 397
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
IS - 3
ER -