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- W2920131403 abstract "Quantifying the causal associations between Electrocorticography (ECoG) recordings has multiple applications in neuroscience. In this work we study the Markov properties of ECoG recordings and their impact on estimating causal influences between these signals. We show that accurate estimation of the causal influence requires knowledge of the Markov order of the considered recordings. Since in general the Markov orders are unknown, they must be estimated from the data before the causal associations are estimated. To address this challenge we propose a data-driven method for estimating these Markov orders." @default.
- W2920131403 created "2019-03-11" @default.
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- W2920131403 date "2018-10-01" @default.
- W2920131403 modified "2023-09-23" @default.
- W2920131403 title "Using Markov Properties of ECoG Signals to Infer Neuron Connectivity" @default.
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- W2920131403 doi "https://doi.org/10.1109/acssc.2018.8645499" @default.
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