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001     885610
005     20240313103121.0
024 7 _ |a 10.12751/NNCN.BC2020.0231
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024 7 _ |a 2128/25880
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037 _ _ |a FZJ-2020-03962
041 _ _ |a English
100 1 _ |a Stella, Alessandra
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111 2 _ |a Bernstein Conference 2020
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|d 2020-09-29 - 2020-10-02
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245 _ _ |a Reoccurring spatio-temporal spike patterns in parallel spike trains
260 _ _ |c 2020
336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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502 _ _ |c RWTH Aachen
520 _ _ |a We designed the statistical method SPADE [1,2,3] to detect ms-precise significant spatio-temporal spike patterns (STPs) across neurons in electrophysiological data. SPADE first discretizes the spike trains by binning and clipping into 0-1 sequences. These parallel sequences are then mined for candidate patterns through Frequent Itemset Mining [4]. The patterns found and their counts are evaluated for significance using surrogates.A classical choice for surrogate generation is uniform dithering (UD) [5], i.e., uniform independent displacement of each spike around its original position. In this work, however, we evidence that UD surrogate spike trains do not conserve the ISI distribution and the regularity of the original data. Moreover, we observe that the combination of spike train binarization and UD causes a reduction of the surrogates’ spike count, yielding an overestimation of the significance of mined patterns (leading to false positives). Thus, our objective is to investigate alternatives to UD for the significance test of spatio-temporal patterns in spike train data.Therefore, we present five (novel as well as established) surrogate techniques and contrast them to UD: uniform dithering with refractoriness, ISI-dithering, joint-ISI dithering [6,8], spike train shifting [7,8] and bin shuffling. We compare these surrogate techniques in terms of the different statistical features of the original spike trains that they preserve, and we test them on randomly generated data reflecting the statistics of our experimental data.We see that only the UD leads to a large number of false positive patterns. Moreover, when analyzing experimental data from monkey pre-/motor cortex we detect many behavior-related patterns and find that all methods besides UD lead to very similar results in terms of the found patterns and their compositions, underlining the robustness of the extracted patterns.AcknowledgementsThe project is funded by the Helmholtz Association Initiative and Networking Fund (ZT-I-0003), by Human Brain Project HBP Grant No. 785907 (SGA2 and SGA3), and by RTG2416 MultiSenses-MultiScales (DFG).References Torre et al. (2016), 10.1523/jneurosci.4375-15.2016 Quaglio et al. (2017), 10.3389/fncom.2017.00041 Stella et al. (2019), 10.1016/j.biosystems.2019.104022 Picado-Muiño et al. (2013), 10.3389/fninf.2013.00009 Date et al. (1998) Gerstein (2004) Pipa et al. (2008), 10.1007/s10827-007-0065-3 Louis et al. (2010), 10.1007/978-1-4419-5675-0_17 Brochier et al. (2018), 10.1038/sdata.2018.55
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536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
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700 1 _ |a Bouss, Peter
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700 1 _ |a Palm, Günther
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700 1 _ |a Grün, Sonja
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773 _ _ |a 10.12751/NNCN.BC2020.0231
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