Estimating Membrane Resistance over Dendrite Using Markov Random Field

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  • Kitazono Jun
    Graduate School of Frontier Sciences, The University of Tokyo
  • Omori Toshiaki
    Graduate School of Engineering, Kobe University RIKEN Brain Science Institute
  • Aonishi Toru
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • Okada Masato
    Graduate School of Frontier Sciences, The University of Tokyo RIKEN Brain Science Institute

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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.

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