Statistical Estimation Algorithm for Phase Response Curves(Cross-disciplinary physics and related areas of science and technology)

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概要

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• Phase response curves (PRCs), which correspond to the Green function, represent an impulse response in oscillatory systems to capture the essence of non-equilibrium dynamics induced by small disturbances. While it is important to measure PRCs to bridge single neuron dynamics and network dynamics, methods of estimating PRCs have not yet been established. We proposed a Bayesian approach to estimating PRCs from noisy data measured through perturbation-response experiments. First, we analyzed the stochastic process describing the observation process in perturbation-response experiments, and obtained a probability distribution for the deterioration process in measuring PRCs. Then, by introducing prior generating PRCs, we derived a maximum a posteriori (MAP) estimation algorithm for PRCs, and finally derived the free energy to estimate hyper-parameters.

• 社団法人日本物理学会の論文
• 2006-11-15

著者

• Ota Keisuke

  Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology

• AONISHI Toru

  Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology

• Aonishi Toru

  Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology

関連論文

• Statistical Estimation Algorithm for Phase Response Curves(Cross-disciplinary physics and related areas of science and technology)

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