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- W62391946 abstract "Much attention has been given to directly interpreting neural firing in the primary motor cortex as a force signal, i.e., a signal that correlates with force production in muscles.How to robustly predict EMG patterns from M1 firing and which M1 neurons contributeto a particular muscle behaviour are interesting questions that arise under this hypothesis.From a statistical point of view, this question corresponds to analyzing datasets with alarge number of input dimensions to detect which inputs contribute the most to theoutputs. This is, at worst, a computationally exhausting combinatorial task.We present a Bayesian Backfitting algorithm that automatically determines the relevantinput dimensions in a regression problem. We compare this algorithm to a brute-forceapproach that considers combinations of relevant input dimensions. The dataset (Sergio &Kalasha, 1998) consists of neuronal firing of M1 neurons and the corresponding muscleEMG data. Bayesian Backfitting successfully determines the correlations between inputsand outputs and closely matches results from the brute-force analysis, performing the taskin orders of magnitude faster. In addition to demonstrating that M1 neurons are goodpredictors of EMG traces, our work shows that Bayesian Backfitting can be used as anew, statistically sound tool to replace traditional tools in biological data analysis. Suchnew Bayesian methods enable data analyses that previously could only have beenconducted on supercomputing facilities." @default.
- W62391946 created "2016-06-24" @default.
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- W62391946 date "2004-05-15" @default.
- W62391946 modified "2023-09-27" @default.
- W62391946 title "Predicting EMG Activity from Neural Firing in M1with Bayesian Backfitting" @default.
- W62391946 hasPublicationYear "2004" @default.
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