Journal Article

FZJ-2022-02804

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Comparing Surrogates to Evaluate Precisely Timed Higher-Order Spike Correlations

Stella, A. (Corresponding author)FZJ* ; Bouss, P.FZJ* ; Palm, G.FZJ* ; Grün, S.FZJ*

2022
Soc. Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/31685  doi:10.1523/ENEURO.0505-21.2022

Abstract: The generation of surrogate data, i.e., the modification of data to destroy a certain feature, can be considered as the implementation of a null-hypothesis whenever an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess the significance of spike correlations in parallel spike trains. In this context, one of the main challenges is to properly construct the desired null-hypothesis distribution and to avoid altering the single spike train statistics. A classical surrogate technique is uniform dithering (UD), which displaces spikes locally and uniformly distributed, to destroy temporal properties on a fine timescale while keeping them on a coarser one. Here, we compare UD against five similar surrogate techniques in the context of the detection of significant spatiotemporal spike patterns. We evaluate the surrogates for their performance, first on spike trains based on point process models with constant firing rate, and second on modeled nonstationary artificial data to assess the potential detection of false positive (FP) patterns in a more complex and realistic setting. We determine which statistical features of the spike trains are modified and to which extent. Moreover, we find that UD fails as an appropriate surrogate because it leads to a loss of spikes in the context of binning and clipping, and thus to a large number of FP patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from the pre-/motor cortex of macaque monkeys during a reaching-and-grasping task.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)
3. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 5231 - Neuroscientific Foundations (POF4-523) (POF4-523)
2. GRK 2416:  MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240) (368482240)
3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
4. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
5. HAF - Helmholtz Analytics Framework (ZT-I-0003) (ZT-I-0003)
6. JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) (JL SMHB-2021-2027)
7. Open-Access-Publikationskosten Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2022

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