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Monday, July 20 • 8:00pm - 9:00pm
P72: Comparison of surrogate techniques for evaluation of spatio-temporal patterns in massively parallel spike trains

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Google Meet link for the poster session: meet.google.com/vuo-rhxm-wip

Link video on poster: https://fz-juelich.sciebo.de/s/dFcwYXhTEIopEj0
  
 Alessandra Stella(1,2), Peter Bouss(1,2), Günther Palm(1,3), Sonja Gruen(1,2)

1 Institute of Neuroscience and Medicine (INM-6, INM-10), Institute for Advanced Simulation (IAS-6), and JARA Brain Institute I (INM-10), Jülich Research Centre, Jülich, Germany

2 Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany

3 Institute of Neural Information Processing, University of Ulm, Ulm, Germany

To identify active cell assemblies we developed a method to detect significant spatio-temporal spike patterns (STPs). The method, called SPADE [1,2,3], identifies repeating ms-precise spike patterns across neurons. SPADE first discretizes the spike trains in exclusive bins (defining the pattern precision, e.g. 5ms) and clips the bin content to 1 if more than 1 spike is therein. Second, STPs are mined by Frequent Itemset Mining [4], and their counts are evaluated for significance through comparison to surrogate data. The distribution of the pattern counts in the surrogate data provides p-values for determining the significance of grouped patterns. The surrogate data implement the null-hypothesis of independence, and a classical choice is to apply uniform dithering (UD) [7], i.e. independent, uniformly distributed displacement of each spike (e.g. in a range of +/- 5 times the bin width [1]). This approach does not maintain the absolute refractory period and a potentially existing ISI regularity. The binarization leads in the surrogates to a higher probability of more than 1 spike per bin, and thus by the consecutive clipping to a reduction of the spike count (up to 12%, in particular for high firing rates) as compared to the original data. This may cause false positives (cmp. [9]). Therefore, we explored further methods for surrogate generation. To not have different spike counts in the original and the surrogate data, bin-shuffling shuffles the bins after binning the original data. To keep the refractory period (RP) uniform dithering with refractory period (UD-RP) does not allow dithered spikes within a short time interval after each spike. Dithering according to the ISI distribution (ISI-D) [e.g. 7] or the Joint-ISI distribution (J-ISI-D) [5] conserves the ISI and ISI/J-ISI distributions, respectively. Spike-train shifting (ST-Shift) [6,7] moves the whole spike train, trial by trial, by a random amount, thereby only affecting the relation of spike trains to each other. Thus all of these implement different null-hypotheses. We applied all surrogate methods (within SPADE) and compared their results using artificial, and experimental spike data simultaneously recorded in pre-/motor cortex of a macaque monkey performing a reach-to-grasp task [8]. We find that all methods besides UD lead to very similar results in terms of number of patterns and their composition. UD results in a much larger number of patterns, in particular if neurons have very high firing rates and exhibit regular spike trains. We conclude that the reduction in the spike count using UD increases the false positive rate for spike trains with CV < 1 and/or high firing rates, the other methods are much less affected, the least spike train shifting.

References:
[1] 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] Picado-Muiño et al (2013). DOI: 10.3389/fninf.2013.00009
[5] Gerstein (2004)
[6] Pipa et al (2008) DOI: 10.1007/s10827-007-0065-3
[7] Louis et al (2010) DOI: 10.1007/978-1-4419-5675-0_17
[8] Brochier et al (2018) DOI: 10.1038/sdata.2018.55
[9] Pipa et al (2013)DOI: 10.1162/NECO_a_00432

Speakers
avatar for Alessandra Stella

Alessandra Stella

Doctoral Researcher, Forschungszentrum Juelich
I am deeply interested in statistical analysis of spike trains, in particular in the development of novel methods for high-order correlation detection in such data, and their application to electrophysiological data. In the current time I am concentrating on spike pattern detection... Read More →



Monday July 20, 2020 8:00pm - 9:00pm CEST
Slot 13