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Fig. 2. | BMC Biology

Fig. 2.

From: Detecting neural assemblies in calcium imaging data

Fig. 2.

Schematics of the different algorithms investigated All algorithms can be divided into three phases: preprocessing, core assembly detection and thresholding/optimisation. a In the ICA algorithms, PCA is applied to \(\frac {\Delta F}{F}\), followed by ICA to the significant principal components. The null model for significance is either determined from circular shifts (ICA-CS) or given as the Marčenko-Pastur distribution (ICA-MP). The resulting principal components are either thresholded directly (ICA-CS) or after a KS test (ICA-MP) in order to arrive at the assemblies. b In the Promax algorithms, \(\frac {\Delta F}{F}\) is first reduced to its significant calcium transients, before PCA is applied. The null model for significant principal components is either given as the Marčenko-Pastur distribution (Promax-MP) or determined from circular shifts (Promax-CS). These principal components are rotated by means of Promax before z-score thresholding the components to arrive at the assemblies. c In the CORE algorithm, \(\frac {\Delta F}{F}\) is deconvolved and the resulting spike probabilities are thresholded into a binary signal. The activity patterns with a high level of activity are reduced to a set of core activity patterns (or ensembles) which are clustered using k-means clustering and the activity patterns of every community are averaged to arrive at the assemblies. d In the SVD algorithm, \(\frac {\Delta F}{F}\) is deconvolved and the resulting spike probabilities are thresholded into a binary signal. From the activity patterns with a high level of activity a similarity map is constructed and thresholded before SVD is applied. The assemblies were then inferred from the activity patterns corresponding to every significant singular vector. e In the SGC algorithm, \(\frac {\Delta F}{F}\) is thresholded to a binary signal and the activity patterns with a high level of activity are arranged into a graph according to their similarity. The graph is split into its communities using spectral clustering and the activity patterns of every community are averaged to arrive at the assemblies. f In the FIM-X algorithm, \(\frac {\Delta F}{F}\) is thresholded into a binary signal and FIM is applied to find co-active neurons as frequent item sets. These frequent item sets are reduced by PSF and PSR involving some additional statistical tests to arrive at the assemblies

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