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Fig. 3. | BMC Biology

Fig. 3.

From: Detecting neural assemblies in calcium imaging data

Fig. 3.

Performance as a function of size of the neural array, number of embedded assemblies, and assembly size In every graph and for every algorithm the mean is depicted by a solid line together with the region of one standard deviation above and below. In the graphs for the performance, measured in terms of the Best Match score, the black dashed line indicates chance level of the Best Match score. a, b Varying the size of the neural array. With an increasing size of the neural array, Promax-MP and FIM-X detected an increasing number of assemblies and, consequently, their performance decreased. ICA-CS, ICA-MP, SGC, CORE, and SVD detected a constant number of assemblies and except for SVD showed good performance. Promax-CS performed well for smaller neural arrays, but slightly underestimated the number of assemblies in larger arrays. c, d Varying the number of embedded assemblies. Promax-MP detected an almost constant number of assemblies. ICA-CS, ICA-MP, SGC, Promax-CS, and FIM-X detected an increasing number of assemblies as the number of embedded assemblies increased, although FIM-X overestimated the total number. When there were only few assemblies embedded, Promax-CS underestimated the total number, while when there were more assemblies embedded, CORE overestimated and SVD underestimated the total number. e, f Varying the assembly sizes. ICA-CS, ICA-MP, SGC, Promax-CS, CORE, and SVD detected a constant number of assemblies except when the embedded assemblies were particularly small. Promax-MP and FIM-X overestimated the number of assemblies

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