Estimation of neuronal dynamics based on sparse modeling.
Conductance-based neuron model
Data-driven approach
Nonlinear neuronal dynamics
Sparse modeling
Journal
Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Titre abrégé: Neural Netw
Pays: United States
ID NLM: 8805018
Informations de publication
Date de publication:
Jan 2019
Jan 2019
Historique:
received:
19
02
2018
revised:
13
07
2018
accepted:
09
10
2018
pubmed:
20
11
2018
medline:
10
1
2019
entrez:
20
11
2018
Statut:
ppublish
Résumé
Elucidating neural dynamics is one of the important subjects in neuroscience. To elucidate nonlinear dynamics of single neurons, it is important to extract nonlinear membrane currents from many types of membrane current candidates. In this study, we propose a sparse modeling method for estimating a conductance-based neuron model from observed data, by extracting necessary membrane currents from multiple candidates. We show using simulated data that our proposed sparse modeling approach with different sparsity levels for distinct membrane currents extracts only necessary membrane currents from candidates more accurately, compared with least-squares method and sparse method with uniform sparsity level.
Identifiants
pubmed: 30453159
pii: S0893-6080(18)30291-0
doi: 10.1016/j.neunet.2018.10.006
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
137-146Informations de copyright
Copyright © 2018 Elsevier Ltd. All rights reserved.