Gast ::
| Hauptseite > Publikationsdatenbank > Surrogate methods for spike pattern detection in non-Poisson data |
| Hauptseite > Publikationsdatenbank > Surrogate methods for spike pattern detection in non-Poisson data |
| Poster (After Call) | FZJ-2021-03095 |
; ; ;
2021
Please use a persistent id in citations: http://hdl.handle.net/2128/28450
Abstract: In order to detect significant spatio-temporal spike patterns (STPs) at ms-precision, we developed the SPADE method[1-3]. SPADE enables the detection and evaluation of spatio-temporal patterns (STPs), i.e., spike patterns across neurons and with temporal delays. For the significance assessment of STPs, surrogates are generated to implement the null hypothesis. Here we demonstrate the requirements for appropriate surrogates.SPADE first discretizes the spike trains into bins of a few ms width. The discretization also includes clipping, i.e., if a bin is occupied by 1 or more spikes, its content is set to 1. The binarized spike trains are then mined for STPs with Frequent Itemset Mining, counting identical patterns. For the assessment of these patterns' significance, surrogata spike trains are used. The surrogate data are mined as the original data resulting in a p-value spectrum for the significance evaluation[3].Surrogate data are modifications of the original data where potential time-correlations are destroyed and thus implement the null hypothesis of independence. For that purpose, the surrogate data need to keep the statistical features of the original data as similar as possible to avoid false positives. A classical choice for a surrogate is uniform dithering (UD), which independently displaces each individual spike according to a uniform distribution. We show that UD makes the spike trains more Poisson-like and does not preserve a potential dead time after the spikes. As a consequence, more spikes are clipped away as compared to the original data. Thus, UD surrogate data reduce the expectation for the patterns.To overcome this problem, we evaluate different surrogate techniques. The first is a modification of UD that preserves the dead time. Further, we employ (joint-)ISI dithering, preserving the (joint-)ISI distribution[4]. Another surrogate is based on shuffling bins of already discretized spike data within a small window. Lastly, we evaluate trial shifting that shifts the whole spike trains against the others, trial by trial, according to a uniform distribution. To evaluate the effect of the different surrogate methods on significance assessment, we first analyze the surrogate modifications on different types of stochastic spike models, such as Poisson spike trains, Gamma spike trains but also Poisson spike trains with dead time[5]. We find that all surrogates but UD are robust to clipping. Trial shifting is the technique that preserves best the statistical features of the spike trains. Further, we analyze artificial data sets for the occurrence of false-positive patterns. These data sets were generated with non-stationary firing rates and interval statistics taken from an experimental data set but are otherwise independent. We find many false positives for UD but all other surrogates show a consistently low number of false-positive patterns. Based on these results, we conclude with a recommendation on which surrogate method to use.References1. Torre et al (2016) DOI:10.1523/JNEUROSCI.4375-15.2016.2. Quaglio et al. (2017). DOI:10.3389/fncom.2017.00041.3. Stella et al. (2019). DOI:10.1016/j.biosystems.2019.104022. 4. Gerstein (2004).5. Deger at al. (2011). DOI: 10.1007/s10827-011-0362-8.
|
The record appears in these collections: |
PDF
Fulltext by OpenAccess repository