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- W2616813501 abstract "We propose a statistical machine learning method for estimating the distributions of parameters in a neural system with multiple neurons. Extracting neural systems from observable imaging data is an important subject in machine learning, medical engineering, and computational neuroscience. In this study, we formulate the generalized state-space model based on the generative process of the observable data provided by calcium imaging. In the proposed method, we employ the particle-Gibbs algorithm in order to realize simultaneous estimation of the distributions of the latent variables representing the state of neurons and those of parameters of neuron units and network connectivity. We show that our proposed method successfully estimates not only parameters of individual neurons and but also those of network structure, simultaneously." @default.
- W2616813501 created "2017-05-26" @default.
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- W2616813501 date "2017-03-25" @default.
- W2616813501 modified "2023-09-24" @default.
- W2616813501 title "Bayesian Estimation of Neural Systems using Particle-Gibbs" @default.
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- W2616813501 doi "https://doi.org/10.1145/3059336.3059359" @default.
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