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. 2018 Jun 5;7:e34467. doi: 10.7554/eLife.34467

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© 2018, Maboudi et al

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Figure 5. — (a) Eight examples from one session show that Bayesian decoding and HMM model-congruence can differ in labeling of significant replay events. For each event, spike rasters (ordered by the location of each neuron’s place field) and the Bayesian decoded trajectory are shown. ‘+' (‘-') label corresponds to significant (insignificant) events. (left) Both methods can fail to label events that appear to be sequential as replay and (right) label events replay that appear non-sequential. (b) We recruited human scorers to visually inspect Bayesian decoded spike trains and identify putative sequential replay events. Using their identifications as labels, we can define an ROC curve for both Bayesian and HMM model-congruence which shows how detection performance changes as the significance threshold is varied. (inset) Human scorers identify 24% of PBEs as replay. Setting thresholds to match this value results in agreement of 70% between Bayesian and HMM model-congruence. (c) Using the same thresholds, we find $\approx 70$ % agreement between algorithmic and human replay identification. (All comparison matrices, $p < 0.001$ , Fisher’s exact test two-tailed.).

Figure 5—figure supplement 1. — (a) Eight examples from one session show that Bayesian decoding and HMM model-congruence can differ in labeling of significant replay events. For each event, spike rasters (ordered by the location of each neuron’s place field) and the Bayesian decoded trajectory are shown. ‘+' (‘-') label corresponds to significant (insignificant) events. (left) Both methods can fail to label events that appear to be sequential as replay and (right) label events replay that appear non-sequential. (b) We recruited human scorers to visually inspect Bayesian decoded spike trains and identify putative sequential replay events. Using their identifications as labels, we can define an ROC curve for both Bayesian and HMM model-congruence which shows how detection performance changes as the significance threshold is varied. (inset) Human scorers identify 24% of PBEs as replay. Setting thresholds to match this value results in agreement of 70% between Bayesian and HMM model-congruence. (c) Using the same thresholds, we find $\approx 70$ % agreement between algorithmic and human replay identification. (All comparison matrices, $p < 0.001$ , Fisher’s exact test two-tailed.).