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Fig. 5. | BMC Biology

Fig. 5.

From: Detecting neural assemblies in calcium imaging data

Fig. 5.

Performance as a function of the event frequency, event firing rate multiplier, and the standard deviation of the noise Graphing conventions as in Fig. 3. a, b Varying the event frequency f∗. With increasing f∗, the performance of ICA-CS, ICA-MP, SGC, Promax-CS and CORE increased and they detected a constant number of assemblies beyond f∗=5mHz. At low frequencies the performance of Promax-CS exceeded that of ICA-CS, ICA-MP and SGC. Promax-MP and FIM-X both overestimated the number of assemblies. c, d Varying the event firing rate multiplier λ. With increasing λ, the performance of ICA-CS, ICA-MP, SGC, and Promax-CS increased and, when λ exceeded about 4, they detected a constant number of assemblies and showed good performance. Promax-MP and FIM-X both overestimated the number of assemblies. The performance of CORE first increased but then consequently decreased as it underestimated the total number of assemblies. e, f Varying the standard deviation σ of the Gaussian noise. With increasing σ, every algorithm (except FIM-X, which instead showed a peak, and SVD) detected a decreasing number of assemblies and, consequently, their performance decreased. For noise levels beyond σ=3 neither Promax-MP or Promax-CS yielded any results since they were not able to fit the noise model to the data

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