Estimation of neuronal dynamics based on sparse modeling. (January 2019)

• Record Type: Journal Article
• Authors: Otsuka, Shinya; Omori, Toshiaki
• Is Part Of: Neural networks. Volume 109(2019)
• Page Start: 137
• Page End: 146
• Publication Date: 2019-01
• Subjects: Sparse modeling -- Nonlinear neuronal dynamics -- Conductance-based neuron model -- Data-driven approach; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.10.006
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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.