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- W4301606150 abstract "Functional microcircuits model the coordinated activity of neurons and play an important role in physiological computation and behaviors. Most existing methods to learn microcircuit structures are correlation-based and often generate dense microcircuits that cannot distinguish between direct and indirect association. We treat microcircuit structure learning as a Markov blanket discovery problem and propose Bayesian Coherence Analysis (BCA) which utilizes a Bayesian network architecture called Bayesian network with inverse-tree structure to efficiently and effectively detect Markov blankets for high-dimensional neural activity data. BCA achieved balanced sensitivity and specificity on simulated data. For the real-world anterior lateral motor cortex study, BCA identified microcircuit subtypes that predicted trial types with an accuracy of 0.92. BCA is a powerful method for microcircuit structure learning." @default.
- W4301606150 created "2022-10-05" @default.
- W4301606150 creator A5063514061 @default.
- W4301606150 date "2022-10-05" @default.
- W4301606150 modified "2023-09-30" @default.
- W4301606150 title "Bayesian Coherence Analysis for Microcircuit Structure Learning" @default.
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- W4301606150 doi "https://doi.org/10.1007/s12021-022-09608-0" @default.
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