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Fig 3

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ZDB-FIG-191230-1328

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Diana et al., 2019 - Bayesian inference of neuronal assemblies

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Fig 3

Comparison of Bayesian inference performance to <italic>k</italic>-means, PCA and spectral clustering.

(A) Performance comparison across levels of asynchrony. Dots correspond to independently generated data sets while solid lines show the average performance for each method over all simulated data. (B) Comparison across number of neurons. (C) Comparison across number of assemblies. (D-F) Standard deviation of the performance across simulated data per parametric condition. Unless specified otherwise, surrogate datasets were generated using 400 neurons and 1000 time frames distributed over 5 assemblies with assembly activity of 5%, synchrony 50% and asynchrony 10%. For k-means we used a number of clusters estimated according to the silhouette method. The number of significant principal components in PCA clustering was obtained by the circular shuffling method whereas for the spectral clustering we used the Newman-Reinart graph-theoretic community detection method [22] (see Materials and methods). The performance was measured according to Eq (27) by comparing each set of assignments with ground truth assignments.

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