Estimating Membrane Resistance over Dendrite Using Markov Random Field
スポンサーリンク
概要
- 論文の詳細を見る
With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that over a dendrite was investigated. Membrane resistance, however, is actually not constant over a dendrite. In a previous study, a method was proposed in which membrane resistance value is expressed as a non-constant function of position on dendrite, and parameters of the function are estimated. Although this method is effective, it is applicable only when the appropriate function is known. We propose a statistical method, which does not express membrane resistance as a function of position on dendrite, for estimating membrane resistance over a dendrite from observed membrane potentials. We use the Markov random field (MRF) as a prior distribution of the membrane resistance. In the MRF, membrane resistance is not expressed as a function of position on dendrite, but is assumed to be smoothly varying along a dendrite. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate function, our method can accurately estimate the membrane resistance.
- 2012-09-28
著者
-
Masato Okada
Graduate School Of Frontier Science The University Of Tokyo|brain Science Institute Riken|japan Scie
-
Masato Okada
Graduate School Of Frontier Sciences The University Of Tokyo|riken Brain Science Institute
-
Jun Kitazono
Graduate School of Frontier Sciences, The University of Tokyo
-
Toshiaki Omori
Graduate School of Engineering, Kobe University|RIKEN Brain Science Institute
-
Toru Aonishi
Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
-
Toru Aonishi
Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology
-
Jun Kitazono
Graduate School Of Frontier Sciences The University Of Tokyo
-
Toshiaki Omori
Graduate School Of Engineering Kobe University|riken Brain Science Institute
-
Masato Okada
Graduate School of Frontier Sciences, The University of Tokyo|RIKEN Brain Science Institute
-
Masato Okada
Graduate School of Frontier Science, The University of Tokyo
関連論文
- Statistical Mechanics of On-line Node-perturbation Learning
- Statistical Mechanics of On-Line Node-Perturbation Learning
- Estimating Membrane Resistance over Dendrite Using Markov Random Field
- A numerical analysis of learning coefficient in radial basis function network
- Exhaustive Search of Feature Subsets for Support Vector Machine Classification
- Sparse Estimation of Spike-Triggered Average
- Poisson Observed Image Restoration using a Latent Variational Approximation with Gaussian MRF
- Instabilities in Associative Memory Model with Synaptic Depression and Switching Phenomena among Attractors
- A Numerical Analysis of Learning Coefficient in Radial Basis Function Network
- Properties of Associative Memory Model with the β-th-order Synaptic Decay
- Sparse Estimation of Spike-Triggered Average