Additional file 2 of Detecting neural assemblies in calcium imaging data
Creators: name: Mölter, Jan
name: Avitan, Lilach
name: Goodhill, Geoffrey J.
Publisher: name: figshare
Year: 2018
Subjects: subject: Space Science
subject: Medicine
subject: Molecular Biology
subject: Neuroscience
subject: Pharmacology
subject: Evolutionary Biology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Plant Biology

description: Optimal performance (Optimal Best Match score) as a function of all the varied parameters. Graphing conventions as in Fig. 3 for the performance graphs. A: Varying the size of the neural array (cf. Fig. 3a, b). ICA-CS, ICA-MP and SGC showed good optimal performance. The optimal performance of all algorithms was approximately constant. B: Varying the number of embedded assemblies (cf. Fig. 3c, d). ICA-CS, ICA-MP and SGC showed good optimal performance. When increasing the number of assemblies, Promax-CS showed an increase in optimal performance. The optimal performance of all other algorithms was approximately constant. C: Varying the assembly sizes (cf. Fig. 3e, f). When increasing the assembly size, the optimal performance of ICA-CS, ICA-MP, SGC and CORE increased to good performance. While Promax-MP showed good optimal performance for small assembly sizes, Promax-CS showed it over the whole range. D: Varying the assembly overlap (cf. Additional file 1: Figure S1A,B). When increasing the overlap, the optimal performance of all algorithms except SGC decreased. E: Varying the simulation duration T (cf. Fig. 4a, b). With increasing T, the performance of ICA-CS, ICA-MP, SGC and Promax-CS increased to good optimal performance beyond T=1800s. F: Varying the temporal resolution ΔT (cf. Fig. 4c, d). ICA-CS, ICA-MP, Promax-CS and SGC showed good optimal performance. The optimal performance of all algorithms was approximately constant. G: Varying the calcium indicator half-life τ 1 2 $\tau _{\frac {1}{2}}$ (cf. Fig. 4e, f). With increasing τ 1 2 $\tau _{\frac {1}{2}}$ , all algorithms showed a decrease in optimal performance. ICA-CS, ICA-MP, SGC and Promax-CS showed good optimal performance. H: Varying the event frequency f∗ (cf. Fig. 5a, b). With increasing f∗, the optimal performance of all algorithms increased. ICA-CS, ICA-MP, SGC and Promax-CS reached good optimal performance at large f∗. I: Varying the event firing rate multiplier λ (cf. Fig. 5c, d). With increasing λ, the performance of ICA-CS, ICA-MP, SGC, Promax-MP, Promax-CS and FIM-X increased and reached good optimal performance at large λ. After an initial increase the performance of CORE decreased towards large λ. J: Varying the standard deviation σ of the Gaussian noise (cf. Fig. 5e, f). With increasing σ, the optimal performance of every algorithm decreased. For noise levels beyond σ=3 neither Promax-MP or Promax-CS yielded any results as they were not able to fit the noise model to the data. K: Varying the saturation constant κ (cf. Fig. S1c, d). ICA-CS, ICA-MP and SGC showed good optimal performance. The optimal performance of all algorithms was approximately constant. (PDF 561 kb)